AndreaBozzo/ceres-open-data-index huggingface dataset:
ISRIC World Soil Texture Classification

ISRIC World Soil Texture Classification at the surface level. Broken into 12 classes of 1:Cl, 2:SiCl, 3:SaCl, 4:ClLo, 5:SiClLo, 6:SaClLo, 7:Lo, 8:SiLo, 9:SaLo, 10:Si, 11:LoSa, 12:Sa.
Caldor Fire Burn Severity

`.geojson` and `tif` files describing the progression and active fire behavior of the 2021 Caldor Fire in California via the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. This includes an extra `.tif` file detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team.
Caldor Fire Behavior

`.geojson` and `tif` files describing the progression and active fire behavior of the 2021 Caldor Fire in California via the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. This includes an extra `.tif` file detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team.
OMI/Aura Sulfur Dioxide (SO2) Total Column L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 as Cloud-Optimized GeoTIFFs (COGs)

OMI/Aura Sulfur Dioxide (SO2) Total Column L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 as Cloud-Optimized GeoTIFFs (COGs)
Gridded Daily OCO-2 Carbon Dioxide assimilated dataset

The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2’s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth's carbon cycle and atmospheric transport. OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA's Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.
Bi-Monthly NDVI GIMMS-3G+

This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions. The data are provided in NetCDF format.
16-Day MODIS Terra Normalized Difference Vegetation Index (NDVI) - L3 NRT

The Vegetation Index (L3, 16-Day) layer is created from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) data which are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The algorithm chooses the best available pixel value from all the acquisitions from the 16-day period. The criteria used is low clouds, low view angle, and the highest NDVI value. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. The MODIS NDVI product is computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. References: MOD13Q1 doi:10.5067/MODIS/MOD13Q1.061
Brightness Temperature (Band 31)

The MODIS Level-1B data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 µm to 14.4 µm region of the electromagentic spectrum. These data are generated from MODIS Level-1A scans of raw radiance, and in the process are converted to geophysical units of W/(m2 µm sr). In addition, the Bidirectional Reflectance Distribution Function (BRDF) may be determined for the solar reflective bands (1-19, 26) through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data.
Global TWS Non-Stationarity Index

The global Terrestrial Water Storage (TWS) non-stationarity index integrates the trend, seasonal shifts, and variability change of TWS for the period of 2003 - 2020. TWS is derived by jointly assimilating the MODIS Leaf Area Index, the ESA CCI surface soil moisture, and the GSFC GRACE mascon-based TWS anomalies into the Noah-MP land surface model within the NASA Land Information System (LIS) at 10 km spatial resolution forced by the combination of MERRA2 and IMERG meteorological fields. The smaller the non-stationarity index is, the more the water cycle is under a non-stationary process. Glaciers and Greenland are excluded from the analysis.
NLDAS-3 Precipitation Forcing Dataset

NLDAS-3 provides a fine-scale (1 km) meteorological forcing (precipitation) in both retrospective and near real-time over North and Central America, including Alaska, Hawaii, and Puerto Rico, by leveraging high-quality gauge, satellite, and model datasets through advanced data assimilation methods. Read more: https://ldas.gsfc.nasa.gov/nldas/v3
NCEO Africa Aboveground Woody Biomass 2017

The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK’s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes.
Change in transpiration for 2020 fires using LIS outputs

Change in vegetation transpiration for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of transpiration in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire transpiration and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in transpiration.
NLDAS-2 Precipitation Forcing Dataset

NLDAS-2 provides a fine-scale (1 km) meteorological forcing (precipitation) in both retrospective and near real-time over North and Central America, including Alaska, Hawaii, and Puerto Rico, by leveraging high-quality gauge, satellite, and model datasets through advanced data assimilation methods. Read more: https://ldas.gsfc.nasa.gov/nldas/v2
Selected Landsat 7 through 9 Surface Reflectance Scenes for Vänern

Selected low-cloud-cover Landsat Collection-2 Level-2 Surface Reflectance scenes covering Vänern. Products from multiple missions and instruments are combined in this single collection for the purpose of visualizing RGB images. Note that the full item records are not interchangeable across instruments for purposes other than visualization and that the assets available are not the same for each instrument.
Selected Landsat 7 through 9 Surface Reflectance Scenes for Tonlé Sap

Selected low-cloud-cover Landsat Collection-2 Level-2 Surface Reflectance scenes covering Tonlé Sap. Products from multiple missions and instruments are combined in this single collection for the purpose of visualizing RGB images. Note that the full item records are not interchangeable across instruments for purposes other than visualization and that the assets available are not the same for each instrument.
Selected Landsat 7 through 9 Surface Reflectance Scenes for Lake Biwa

Selected low-cloud-cover Landsat Collection-2 Level-2 Surface Reflectance scenes covering Lake Biwa. Products from multiple missions and instruments are combined in this single collection for the purpose of visualizing RGB images. Note that the full item records are not interchangeable across instruments for purposes other than visualization and that the assets available are not the same for each instrument.
Selected Landsat 7 through 9 Surface Reflectance Scenes for Lake Balaton

Selected low-cloud-cover Landsat Collection-2 Level-2 Surface Reflectance scenes covering Lake Balaton. Products from multiple missions and instruments are combined in this single collection for the purpose of visualizing RGB images. Note that the full item records are not interchangeable across instruments for purposes other than visualization and that the assets available are not the same for each instrument.
Selected Landsat 7 through 9 Surface Reflectance Scenes for the Aral Sea

Selected low-cloud-cover Landsat Collection-2 Level-2 Surface Reflectance scenes covering the Aral Sea. Products from multiple missions and instruments are combined in this single collection for the purpose of visualizing RGB images. Note that the full item records are not interchangeable across instruments for purposes other than visualization and that the assets available are not the same for each instrument.
Selected Landsat 7 through 9 Surface Reflectance Scenes for the Thwaites Glacier

Selected low-cloud-cover Landsat Collection-2 Level-2 Surface Reflectance scenes covering the Thwaites Glacier. Products from multiple missions and instruments are combined in this single collection for the purpose of visualizing RGB images. Note that the full item records are not interchangeable across instruments for purposes other than visualization and that the assets available are not the same for each instrument.
Selected Landsat 7 through 9 Surface Reflectance Scenes for the Pine Island Glacier

Selected low-cloud-cover Landsat Collection-2 Level-2 Surface Reflectance scenes covering the Pine Island Glacier. Products from multiple missions and instruments are combined in this single collection for the purpose of visualizing RGB images. Note that the full item records are not interchangeable across instruments for purposes other than visualization and that the assets available are not the same for each instrument.
Annual LAI maps for 2003 and 2021 (Bangladesh)

The annual median Leaf Area Index (LAI) maps of 2003 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 dataset (MCD15A3H Version 6.1, dataset link: https://modis.gsfc.nasa.gov/data/dataprod/mod15.php). The actual dataset represents one-sided green leaf area per unit ground area at 500 meters spatial resolution and provides information at every 4 days. Annual median of the LAI datasets were calculated for both the years of 2003 and 2021 to illustrate the difference in vegetation cover.
Change in ET for 2020 fires using LIS outputs

Change in ET for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of ET in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET.
USGS Landslide Hazard Estimate Model - Hurricane Helene

 The two modeling approaches used were adapted from existing models and were run in early October 2024 using initial precipitation estimates to produce rapid event-specific landslide hazard estimate maps. These preliminary maps were initially released provisionally to meet the need for timely best science during the emergency response (version 1.0). The outputs were used to provide early situational awareness of what areas may have been more severely impacted by landslides and debris flows. These results were also used in planning reconnaissance.
GEOGLAM Crop Monitor

The Crop Monitors were designed to provide a public good of open, timely, science-driven information on crop conditions in support of market transparency for the G20 Agricultural Market Information System (AMIS). Reflecting an international, multi-source, consensus assessment of crop growing conditions, status, and agro-climatic factors likely to impact global production, focusing on the major producing and trading countries for the four primary crops monitored by AMIS (wheat, maize, rice, and soybeans). The Crop Monitor for AMIS brings together over 40 partners from national, regional (i.e. sub-continental), and global monitoring systems, space agencies, agriculture organizations and universities. Read more: https://cropmonitor.org/index.php/about/aboutus/
GEDI Mean Canopy Height AGL

The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.
ECCO sea-surface height change from 1992 to 2017

Gridded global sea-surface height change from 1992 to 2017 from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate. The dataset was calculated as the difference between the annual means over 2017 and 1992, from the 0.5 degree, gridded monthly mean data product available on PO.DAAC (https://podaac.jpl.nasa.gov/dataset/ECCO_L4_SSH_05DEG_MONTHLY_V4R4).
Annual land cover maps for 2001 and 2020

The annual land cover maps of 2001 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset (MCD12Q1 V6, dataset link: https://lpdaac.usgs.gov/products/mcd12q1v006/). The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country.
Brightness Temperature (Band I5)

The VIIRS Brightness Temperature (Band I5, Day) layer is the brightness temperature, measured in Kelvin (K), calculated from the top-of-the-atmosphere radiances. It does not provide an accurate temperature of either clouds or the land surface, but it does show relative temperature differences which can be used to distinguish features both in clouds and over clear land. It can be used to distinguish land, sea ice, and open water over the polar regions during winter (in cloudless areas). The VIIRS Brightness Temperature layer is calculated from VIIRS Calibrated Radiances (VNP02) and is available from the joint NASA/NOAA Suomi National Polar orbiting Partnership (Suomi NPP) satellite. The sensor resolution is 375m, the imagery resolution is 250m, and the temporal resolution is daily.