Updated datasets 2025-01-24 UTC

datasets/ NGA178 _1.0.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "\n                  NGA178\n                _1.0",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more.",
     "links": [
         {

datasets/ NGA183 _1.0.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "\n                  NGA183\n                _1.0",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy\u2019s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy\u2019s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).",
     "links": [
         {

datasets/ NGA232 _1.0.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "\n                  NGA232\n                _1.0",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "Remote sensing data collected from Brookhaven National Laboratory\u2019s (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform \u2013 the Osprey \u2013 operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface \u201cskin\u201d temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy\u2019s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy\u2019s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).",
     "links": [
         {

datasets/ GEOS-CF Products_1.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": " GEOS-CF Products_1",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The NASA Global Earth Observing System (GEOS) model has been expanded to provide global nearreal-\r\ntime forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (about 25\r\nkm). This GEOS Composition Forecast (GEOS-CF) system combines the GEOS weather analysis and\r\nforecasting system with the state-of-the-science GEOS-Chem chemistry module (Bey et al., 2001;\r\nKeller et al., 2014; Long et al., 2015) to provide detailed chemical analysis of a wide range of air\r\npollutants including ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).",
     "links": [
         {

datasets/001aab54-295d-4fb3-b269-748b8e0b9a04_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "001aab54-295d-4fb3-b269-748b8e0b9a04_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale.",
     "links": [
         {

datasets/004fd44ff5124174ad3c03dd2c67d548_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "004fd44ff5124174ad3c03dd2c67d548_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements.This dataset is based on measurements from AVHRR (onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19 satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-PMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-PM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.  To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; W\u00c3\u00bcrzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.",
     "links": [
         {

datasets/024292dcda5d42ceb326850f89f8b40d_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "024292dcda5d42ceb326850f89f8b40d_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note,  the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)",
     "links": [
         {

datasets/0470e96f2d8245549ef2ba81842cdfd8_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "0470e96f2d8245549ef2ba81842cdfd8_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from SARAL-AltiKa for 2013-2017. This new experimental product of surface elevation change is based on data from the AltiKa-instrument onboard the France (CNES)/Indian (ISRO) SARAL satellite. The AktiKa altimeter utilizes Ka-band radar signals, which have less penetration in the upper snow. However, the surface slope and roughness has an imprint in the derived signal and the new product is only available for the flatter central parts of the Greenland ice sheet.The corresponding SEC grid from Cryosat-2 is included for comparison. The algorithm used to devive the product is described in the paper \u00e2\u0080\u009cImplications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry\u00e2\u0080\u009d by S.B. Simonsen and L.S. S\u00c3\u00b8rensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012.   The approach used here corresponds to Least Squares Method (LSM) 5 described in the paper, in which the slope within each grid cell is accounted for by subtraction of the GIMP DEM; the data are corrected for both backscatter and leading edge width; and the LSM is solved at 1 km grid resolution (2 km search radius) and averaged in the post-processing to 5 km grid resolution and with a correlation length of 20 km.",
     "links": [
         {

datasets/04bc222136f7429eb04d3eb3543ef3e8_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "04bc222136f7429eb04d3eb3543ef3e8_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ORAC algorithm, version 4.01.   It covers the period from 1995-2003For further details about these data products please see the linked documentation.",
     "links": [
         {

datasets/057dd6c36f0741d3b97f9eee688b7835_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "057dd6c36f0741d3b97f9eee688b7835_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070",
     "links": [
         {

datasets/065f6040ef08485db989cbd89d536167_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "065f6040ef08485db989cbd89d536167_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This dataset is part of v1.1 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2016.   Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator).   Gridded data products are also available in a separate dataset.",
     "links": [
         {

datasets/0875b4675f1e46ebadb526e0b95505c5_NA.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "0875b4675f1e46ebadb526e0b95505c5_NA",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022.  Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)",
     "links": [
         {

datasets/0944645.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "0944645",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis.\n\nPetrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the \u2018backside\u2019 of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids.",
     "links": [
         {

datasets/0ac98747-eb94-4c9f-aef8-56f9d3a04740.json

@@ -1,7 +1,7 @@
 {
     "type": "Collection",
     "id": "0ac98747-eb94-4c9f-aef8-56f9d3a04740",
-    "stac_version": "1.0.0",
+    "stac_version": "1.1.0",
     "description": "The map (risk map) presents the results of earthquake annual average losses (AAL) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (AAL-absolute value) and millar (AAL/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL.",
     "links": [
         {