This dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegetated) water within each 300 m grid cell. Belowground sources of respiration (i.e., root and microbial) are included. The gridded soil CO2 estimates were obtained using seasonal Random Forest models, information from remote sensing, and a new compilation of in-situ soil CO2 flux from Soil Respiration Stations and eddy covariance towers. The flux tower data are provided along with daily gap-filled flux observations for each Soil Respiration station forced diffusion (FD) chamber record. The data cover the NASA ABoVE Domain.

This dataset contains WebMercator tiles which contain gray-scale shaded relief (hill shades), and nothing else. The tiles have a resolution of 256×256px, suitable for web mapping libraries such as Leaflet. The hill shades are generated from SRTM altitude data, which cover the land area between 60° northern and 58° southern latitude, and which lies in the public domain. Map material without political or infrastructural features can be desirable, for example, in use cases where historical data is visualized on a map. The concrete motivation for generating this map material was the Dhimmis & Muslims project (project page, home page, GitHub, DaRUS dataset), which analyzed peaceful coexistence of religious groups in the medieval Middle East. A particular goal with creating the dataset was to have map material available under a permissive license for screenshots and publications, instead of relying on proprietary mapping services such as Mapbox. Teaser image: The hillshades of Cyprus on zoom level 9. This image is hosted externally by GitHub, but is also present in the repository as teaser.png. Coverage. The dataset covers zoom level 0 (entire world in one tile) to 12 (entire world in 4096×4096 tiles). The total size of the dataset is 22,369,621 tiles. However, of those, 19,753,304 tiles (88.3%) are empty, either because the landscape there is fully flat (i.e., on water), or because they lie fully outside the latitude range covered by the SRTM altitude data. The empty tiles are not stored. Instead, a singular placeholder file is stored in the repository, alongside a list of the empty tiles. During extraction, the placeholder empty tile can be symbolically linked in the file system to all the places where it is needed. The total size of the non-empty tiles is about 103GB. Files. Besides the placeholder file and the list of empty tiles, the repository also contains a manifest file. This file lists all non-empty tiles by the ZIP file they are contained in. The tiles themselves are grouped into ZIP files by the following schema: All tiles from levels 0 to 5 are contained in one ZIP file. All tiles of level N, N≥6 are contained in a ZIP file which is named after the tile of level N-6 (block level) that contains the tile in question, named tiles_.zip. Hence, all tiles of level 6 are contained in a singular ZIP file named tiles_6_0_0_0.zip. The tiles of level 7 are split up into four group ZIP files named tiles_7_1_{0,1}_{0,1}.zip, the tiles of level 8 into 16 group ZIP files named tiles_8_2_{0..3}_{0..3}.zip, and so on. Both the manifest file and the commands to generate the distribution of tiles on ZIP files can be generated using the linked software repository. Usage. The tile ZIP files can be downloaded and extracted. By serving the extracted directory structure in a web server, a slippy map tile server can be created. The linked software repository also contains a command-line utility that generates the required shell commands to download the ZIP files, extract them, and softlink (ln -s) the empty tiles to the appropriate places. This command-line utility can also optionally read in a GeoJSON file of an area of interest. In this case, only tiles within that area are downloaded in a higher zoom level, whereas tiles completely outside the area are only downloaded to a lower zoom level; both zoom levels are also configurable. See the documentation in the repository and the command-line utility’s help (-h) output for more details.

This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada, Alaska, US, and parts of far eastern Russia at 6.25-km resolution for the period 1988-2023. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) stations, and compared to an established Freeze-Thaw ESDR (FT-ESDR) spring onset date record. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes within the ABoVE (Arctic Boreal Vulnerability Experiment) domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). No coastal mask is used for the 2017-2023 data. The full data are included, and data users should be aware that values adjacent to large water bodies can be adversely affected.

Update Frequency:DailySummary:Land Information System (LIS) 0-200 cm layer Soil Moisture Percentile generated by the NASA SPoRT Center over a Contiguous United States domain.The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications and against U.S. Drought Monitor products.SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2 through 4 days prior to the current time, and by the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates from 4 days ago to the present time. A unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of VIIRS Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model.The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific day of the year. Soil moisture percentile maps are constructed for the model layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product.Suggested Use:This product can be used for drought assessment, fire risk assessment, potential for flooding hazards associated with heavy precipitation and high percentiles; contextualizing soil moisture content to historical values.Soil moisture percentiles are shown using a Classified Color Ramp (Multi-Color, 11-classes) that colorize the low percentile categories (≤ 30th) as shown in the U.S. Drought Monitor weekly products, ranging from yellow to dark red. The high percentile categories (≥ 70th) are colorized with increasing blue intensity. Intermediate percentiles in the 30th to 70th range are assigned a nominal gray shade.The 0-200 cm layer combines SPoRT-LIS soil moisture analyses from all four model layers 0-10 cm, 10-40 cm, 40-100 cm, and 100-200 cm depths. The 0-200 cm cumulative layer adjusts slowly to precipitation episodes or the lack thereof compared to the other cumulative layered percentile products. It takes considerably longer time periods for intercepted rainfall and snowmelt to infiltrate from the upper layers into the lower layers at 40-100 cm and 100-200 cm, or conversely for the deeper soil layer to dry from evapotranspiration processes. Expect anomalies of soil moisture percentiles in the total column 0-200 cm layer to respond to meteorological features on the order of months to years (especially for drying periods), depending on the soil classification and soil responsiveness.Data Caveats:The SPoRT-LIS is as good as the input forcing analyses, so occasional soil moisture artifacts may appear in the horizontal maps related to quality-control issues of the input datasets. These can be manifested with unusually low or high percentiles, especially along international borders, coastlines, and isolated dry “bulls-eyes” at rain gauge with quality issues.Data Visualization:The Soil Moisture Percentile is the histogram rank of the current day’s soil moisture value compared to the 33-year climatology for the present day. The percentile places into historical context the soil moisture to determine how unusually wet or dry, or typical the conditions are. Percentile thresholds as established by the drought community are used to categorize soil moisture dry anomalies can be found here.

This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.

This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), water vapor concentrations, air temperature, pressure, and wind speed and direction as well as airborne remote sensing measurements of column average CO2 collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, US, and the Yukon and Northwest Territories of Canada. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements were taken onboard a DC-8 aircraft. The ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. The data are provided in ICARTT format along with an archive of flight videos.

This dataset provides annual maps of the snowoff (SO) date from 1988-2023 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012).

The Shuttle Radar Topography Mission (SRTM) successfully collected Interferometric Synthetic Aperture Radar (IFSAR) data over 80 percent of the landmass of the Earth between 60 degrees North and 56 degrees South latitudes in February 2000. The mission was co-sponsored by the National Aeronautics and Space Administration (NASA) and National Geospatial-Intelligence Agency (NGA). NASA's Jet Propulsion Laboratory (JPL) performed preliminary processing of SRTM data and forwarded partially finished data directly to NGA for finishing by NGA's contractors and subsequent monthly deliveries to the NGA Digital Products Data Wharehouse (DPDW). All the data products delivered by the contractors conform to the NGA SRTM products and the NGA Digital Terrain Elevation Data (DTED) to the Earth Resources Observation & Science (EROS) Center. The DPDW ingests the SRTM data products, checks them for formatting errors, loads the SRTM DTED into the NGA data distribution system, and ships the public domain SRTM DTED to the U.S. Geological Survey (USGS) Earth Resources Observation & Science (EROS) Center.

Two resolutions of finished grade SRTM data are available through EarthExplorer from the collection held in the USGS EROS archive:

1 arc-second (approximately 30-meter) high resolution elevation data are only available for the United States.

3 arc-second (approximately 90-meter) medium resolution elevation data are available for global coverage. The 3 arc-second data were resampled using cubic convolution interpolation for regions between 60° north and 56° south latitude.

[Summary provided by the USGS.]

The SWOT Level 2 KaRIn High Rate Raster Product (SWOT_L2_HR_Raster_D) provides rasterized estimates of water surface elevation, inundation extent, and radar backscatter derived from high-resolution radar observations by the Ka-band Radar Interferometer (KaRIn) on the SWOT satellite. This product aggregates the irregularly spaced pixel cloud data from the PIXC and PIXCVec products onto a uniform geographic grid to facilitate spatial analysis of water surface features across inland, estuarine, and coastal domains.Standard granules cover non-overlapping 128 × 128 km² scenes in the UTM projection at 100 m and 250 m resolution, stored in NetCDF-4 format. Each file contains 2D image layers representing water surface elevation (corrected for geoid, solid Earth, load, and pole tides, as well as atmospheric and ionospheric path delays), surface area, water fraction, and sigma0, along with quality flags and uncertainty estimates. On-demand versions are available at user-specified resolutions and projections, with optional overlapping granules and GeoTIFF output via SWODLR: https://swodlr.podaac.earthdatacloud.nasa.gov/The raster product offers a gridded alternative to the unstructured pixel cloud, supporting hydrologic and geomorphic analyses in complex flow environments such as braided rivers, floodplains, wetlands, and coastal zones. It enables consistent spatiotemporal sampling while reducing noise through spatial aggregation, making it especially suitable for applications that require map-like continuity or integration with geospatial models.This dataset is the parent collection to the following sub-collections: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_D https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_D

GCOM-C/SGLI L3 Map Atmospheric corrected reflectance (RN11) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily map-projected statistics product. This dataset includes Reflectance of VNR-NP Band 11 co-registered for VNR-PL (Center Wavelength is 868.5 nm). The physical quantity unit is W/m^2/um/sr. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

This dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegetated) water within each 300 m grid cell. Belowground sources of respiration (i.e., root and microbial) are included. The gridded soil CO2 estimates were obtained using seasonal Random Forest models, information from remote sensing, and a new compilation of in-situ soil CO2 flux from Soil Respiration Stations and eddy covariance towers. The flux tower data are provided along with daily gap-filled flux observations for each Soil Respiration station forced diffusion (FD) chamber record. The data cover the NASA ABoVE Domain.

This dataset contains airborne sea surface temperature (SST) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Multiscale Observing System of the Ocean Surface (MOSES) is an aerial observing system that primarily uses a longwave infrared (LWIR) camera to record SST at a resolution of several meters. Individual images are mosaiced together to provide a synoptic map of the sample domain covering approximately 200 km. MOSES is mounted on the B200 aircraft which flies daily surveys of the field domain during deployments. Data are available in netCDF format.

Date of Images:5/6/2024Date of Next Image:UnknownSummary:The floodwater depth raster produced for the 06th May 2024 for long-term floods in southern Brazil was generated using the Flood Water Depth Estimation Tool (Cohen et al, 2018, 2019; Peter et al, 2020). The Floodwater Depth Estimation Tool (FwDET) is a solution for producing timely floodwater depth data during flood activations that require emergency response and post-flood assessment.FwDET is based solely on a flood extent layer and a digital elevation model (DEM). The DEM used here was the NASA SRTM data. The flood extent layer was derived from the OPERA Dynamic Surface Water eXtent (DSWx) from the Harmonized Landsat Sentinel-2 (HLS) product. The DEM used was the NASA Shuttle Radar Topography Mission (SRTM) data. The computation involves identifying elevations at the boundaries of the flood extent layer and using those elevations to assign floodwater surface elevation to each cell within the flooded domain by identifying its nearest boundary cell. The modeled flood surface elevation is then subtracted from the original DEM to retrieve depth.The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the flood inundation depth. The flood depth map may contain errors due to inaccurate elevations in the NASA SRTM DEM or the water surface estimation.Data Sources:OPERA Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS) - based flood inundation extent raster generated by the ARIA/OPERA group at NASA JPL. NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine)Suggested Use:The darkest shades of blue indicate where the product is estimating the largest floodwater depth, while lighter shades indicate shallower floodingSatellite/Sensor:Harmonized Landsat Sentinel-2 (HLS)MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellitesNASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine)Resolution:30 metersCredits:Dinuke Munasinghe - NASA JPL Water and Ecosystems Team, NASA JPL ARIA/OPERA TeamProduct POCs:Dinuke Munasinghe (dinuke.nanayakkara.munasinghe@jpl.nasa.gov)FwDET Algorithm Documentation:Cohen, S., Brakenridge, G.R., Kettner, A., Bates, B., Nelson, J., McDonald, R., Huang, Y, Munasinghe, D., and J. Zhang (2018). Estimating Floodwater Depths from Flood Inundation Maps and Topography. Journal of the American Water Resources Association, 54(4), 847-858. DOI: https://doi.org/10.1111/1752-1688.12609Cohen, S., Raney, A., Munasinghe D., Loftis, D., Molthan, A., Bell, J., Rogers, L., Galantowicz, J., Brakenridge, G.R., Kettner, A., Huang, Y., and Y. Tsang (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19(9), 2053-2065. DOI: https://doi.org/10.5194/nhess-19-2053-2019Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.E., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183.Peter, B., Cohen, S., Lucey, R., Munasinghe, D., Raney, A., and G. Brakenridge (2020). "Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis". IEEE Geoscience and Remote Sensing Letters. DOI: https://doi.org/10.1109/lgrs.2020.3031190OPERA. 2023. OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 CalVal Database (Version 1). Ver. 1.0. PO.DAAC, CA, USA. Dataset accessed [2024-05-09] at https://doi.org/10.5067/OPDSW-PCVV1Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/brasil_flood_202405/fwdet_flood_depth/MapServer/WMSServerData Download:https://aria-share.jpl.nasa.gov/202405-RioGrandeSul_Brazil-floods/FloodDepth/

GCOM-C/SGLI L3 Map Photosynthetically available radiation (PAR) (8-Days,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes PAR: Photosynthetically available radiation and QA_Flag. The physical quantity unit is Ein/m^2/day. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

Date of Images:5/6/2024Date of Next Image:UnknownSummary:The floodwater depth raster produced for the 06th May 2024 for long-term floods in southern Brazil was generated using the Flood Water Depth Estimation Tool (Cohen et al, 2018, 2019; Peter et al, 2020). The Floodwater Depth Estimation Tool (FwDET) is a solution for producing timely floodwater depth data during flood activations that require emergency response and post-flood assessment.FwDET is based solely on a flood extent layer and a digital elevation model (DEM). The DEM used here was the NASA SRTM data. The flood extent layer was derived from the OPERA Dynamic Surface Water eXtent (DSWx) from the Harmonized Landsat Sentinel-2 (HLS) product. The DEM used was the NASA Shuttle Radar Topography Mission (SRTM) data. The computation involves identifying elevations at the boundaries of the flood extent layer and using those elevations to assign floodwater surface elevation to each cell within the flooded domain by identifying its nearest boundary cell. The modeled flood surface elevation is then subtracted from the original DEM to retrieve depth.The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the flood inundation depth. The flood depth map may contain errors due to inaccurate elevations in the NASA SRTM DEM or the water surface estimation.Data Sources:OPERA Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS) - based flood inundation extent raster generated by the ARIA/OPERA group at NASA JPL. NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine)Suggested Use:The darkest shades of blue indicate where the product is estimating the largest floodwater depth, while lighter shades indicate shallower floodingSatellite/Sensor:Harmonized Landsat Sentinel-2 (HLS)MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellitesNASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine)Resolution:30 metersCredits:Dinuke Munasinghe - NASA JPL Water and Ecosystems Team, NASA JPL ARIA/OPERA TeamProduct POCs:Dinuke Munasinghe (dinuke.nanayakkara.munasinghe@jpl.nasa.gov)FwDET Algorithm Documentation:Cohen, S., Brakenridge, G.R., Kettner, A., Bates, B., Nelson, J., McDonald, R., Huang, Y, Munasinghe, D., and J. Zhang (2018). Estimating Floodwater Depths from Flood Inundation Maps and Topography. Journal of the American Water Resources Association, 54(4), 847-858. DOI: https://doi.org/10.1111/1752-1688.12609Cohen, S., Raney, A., Munasinghe D., Loftis, D., Molthan, A., Bell, J., Rogers, L., Galantowicz, J., Brakenridge, G.R., Kettner, A., Huang, Y., and Y. Tsang (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19(9), 2053-2065. DOI: https://doi.org/10.5194/nhess-19-2053-2019Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.E., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183.Peter, B., Cohen, S., Lucey, R., Munasinghe, D., Raney, A., and G. Brakenridge (2020). "Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis". IEEE Geoscience and Remote Sensing Letters. DOI: https://doi.org/10.1109/lgrs.2020.3031190OPERA. 2023. OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 CalVal Database (Version 1). Ver. 1.0. PO.DAAC, CA, USA. Dataset accessed [2024-05-09] at https://doi.org/10.5067/OPDSW-PCVV1Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/brasil_flood_202405/fwdet_flood_depth/MapServer/WMSServerData Download:https://aria-share.jpl.nasa.gov/202405-RioGrandeSul_Brazil-floods/FloodDepth/

Sichuan Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for Sichuan Province, China. The top level directory sichuan-dataset is a GRASS GIS location for WGS 84 / UTM zone 48N with EPSG code 32648. Inside the location there is the PERMANENT mapset, color tables, category tables, a license file, and readme file.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• ALOS Global Digital Surface Model
• Anthromes
• NASA Shuttle Radar Topography Mission (SRTM)
• ESA CCI Landcover
• European Soil Data Centre (ESDAC)
• Database of Global Administrative Areas (GADM)
• Harvard World Map
• IUCN Red List
• Protected Planet
• NASA Blue Marble
• Natural Earth
• Open Street Map
• WWF HydroSHEDS
• WWF Terrestrial Ecoregions of the World

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

This product contains data derived from permanent in situ soil stations and observations by the Passive Active L-band System (PALS) microwave aircraft instrument. The PALS instrument was mounted to a DC-3 aircraft, which flew six parallel flight lines at an altitude of 3000 m in order to map a 26 km x 48 km domain in Manitoba, Canada. Nine permanent soil stations were distributed throughout this same area. The soil characteristics included in this data set are volumetric soil moisture, vertically and horizontally polarized brightness temperature, effective soil temperature, effective vegetation temperature, vegetation water content, land cover classification, sand and clay fraction, and volumetric soil moisture uncertainty estimates.

This data set, ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001, contains global land cover classifications (dominant type, classification confidence and fractional cover) generated using a full year of MODerate Resolution Imaging Spectroradiometer (MODIS) data covering the period from October 2000 to October 2001. The objective of the MODIS Land Cover Product is to provide a suite of land cover types useful to global system science modelers by exploiting the information content of MODIS data in the spectral, temporal, spatial, and directional domains. These products describe the geographic distribution of the 17 land cover classification scheme proposed by the International Geosphere-Biosphere Programme (IGBP).

NASA has released version 2 of the Shuttle Radar Topography Mission digital topographic data (also known as the "finished" version). Version 2 is the result of a substantial editing effort by the National Geospatial Intelligence Agency and exhibits well-defined water bodies and coastlines and the absence of spikes and wells (single pixel errors), although some areas of missing data ('voids') are still present. The Version 2 directory also contains the vector coastline mask derived by NGA during the editing, called the SRTM Water Body Data (SWBD), in ESRI Shapefile format.

                [Summary provided by NASA.]

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. The vegetation data set includes one variable: vegetation type. Vegetation types are defined physiognomically in terms of dominant lifeform and leaf characteristics (including leaf seasonal duration, shape, and size) and, in the case of grasslands, physiologically with respect to dominance of species with the C3 versus C4 photosynthetic pathway. The physiognomic classification criteria are based on our understanding of vegetation characteristics that influence biogeochemical dynamics (Running et al. 1994). The U.S. distribution of these types is based on a 0.5 degree latitude/longitude gridded map of Kuchler's (1964, 1975) potential natural vegetation provided by the TEM group (D. Kicklighter and A.D. McGuire, personal communication). Kuchler's map is based on current vegetation and historical information and, for purposes of VEMAP Phase I model experiments, is presumed to represent potential vegetation under current climate and atmospheric CO2 concentrations (355 ppm). A complete users guide to the VEMAP Phase I database which includes more information about this data set can be found at ftp://daac.ornl.gov/data/vemap-1/comp/Phase_1_User_Guide.pdf. ORNL DAAC maintains additional information associated with the VEMAP Project. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 1998. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.