Global GIS data

Abstract and Limitations of Use: The Annual Global High-Resolution Extreme Heat Estimates (GEHE), 1983-2016 data set provides global 0.05 degrees (~5 km) gridded annual counts of the number of days where the maximum Wet Bulb Globe Temperature (WBGTmax) exceeded dangerous hot-humid heat thresholds for the period 1983 to 2016. This historical data set does not have a routine update frequency. The thresholds are based on the International Standards Organization (ISO) criteria for occupational heat-related risk, defined as days where WBGTmax > 28, 30, and 32 degrees Celsius. This data set also includes the annual rate of change in the number of extreme humid-heat days that exceeded these thresholds. GEHE has a wide array of applications for mapping and quantifying extreme humid-heat dynamics over a 34-year time period, and is the highest resolution data set of its kind to date. GEHE provides scientific researchers and decision makers from a wide range of arenas, including climate change, public and occupational health, urban planning and design, hazards risk reduction, and food security, insights into how humid-heat has impacted human and environmental systems worldwide. The data set can be used to pinpoint how changes in extreme humid-heat impact human health and well-being, as well as ecological systems, across scales of analysis, from local, to national, to global. Use the Multidimensional Slider in ArcGIS Map Viewer to select a WBGT variable and explore annual slices from 1983–2016. This is the recommended method for viewing changes over time in this multidimensional dataset. Trend variables represent change across the full 1983–2016 period and do not have individual yearly slices. Intended Use: To provide a high-resolution, longitudinal global data set of annual extreme humid-heat. Variables: Wet Bulb Globe TemperatureWBGTmax28  WBGTmax28-trend WBGTmax30 WBGTmax30-trend WBGTmax32 WBGTmax32-trend LegendSymbol  Pixel Value  Description       High: 366 A high-resolution, longitudinal global data set of annual extreme humid-heat Low: 0   Citation: Tuholske, C., P. Peterson, C. Funk, and K. Caylor. 2023. Annual Global High-Resolution Extreme Heat Estimates (GEHE), 1983-2016. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/hff0-k565.  Publication References:Tuholske, C., K. Caylor, C. Funk, A. Verdin, S. Sweeney, K. Grace, P. Peterson, and T. Evans. 2021. Global Urban Population Exposure to Extreme Heat. Proceedings of the National Academy of Sciences 118(41), e2024792118. DOI: https://doi.org/10.1073/pnas.2024792118 Contact:For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum.

Abstract and Limitations of Use: The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper "Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR). Because this is a historical dataset, this item does not follow a routine update frequency.   Intended Use: To provide estimates of urban and rural populations and land areas for the years 1990, 2000, 2015 for 234 countries and statistical areas with contiguous coastal elevations of less than or equal to 5m above sea level, 5-10m above sea level, and national totals using multiple updated data sources for comparative analysis.  Citation: Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. (2021). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4TM782G Legend:ColorService Pixel ValueLegend LabelDescription: 50 - 5m LECZLECZs are derived from Multi-Error-Removed Improved Terrain Digital Elevation Model (MERIT-DEM) are provided as a spatial layer in a 9 arc second resolution raster (GeoTIFF). This data categorically demarcates the two low elevation coastal zones (0-5m and 5-10m LECZs) and a third category representing the non-coastal areas at any elevation (outside of LECZs) coded as 31. This raster data is also provided as a web map and image service. It is in the WGS84 coordinate system. 10>5 - 10m LECZ 31>10m - Outside of LECZResolution: nine arc-seconds (~300m)  Publication References:Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates, Global Rural-Urban Mapping Project (GRUMP), Alpha Version. McGranahan, G., D. Balk, and B. Anderson. (2007). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4TM782G Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2. Center for International Earth Science Information Network - CIESIN - Columbia University. (2013). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4MW2F2J Sea Level Rise Impacts on Ramsar Wetlands of International Importance. Center for International Earth Science Information Network - CIESIN - Columbia University. (2013). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4CC0XMD Documentation for the Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Center For International Earth Science Information Network - CIESIN - Columbia University. (2021). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/z0r0-gc08

Overview: OPERA Level-3 Dynamic Surface Water Extent (DSWx) product from Harmonized Landsat-8/9 and Sentinel-2A/B/C (HLS), processed and developed by OPERA (JPL/USGS). Values for B01 WTR are a masked interpreted water classification layer. This represents pixel-wise classification into one of three water classes (not water, open water, and partial surface water), HLS snow/ice, HLS cloud/cloud shadow and adjacent to cloud/cloud shadow, or no data classes.   This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described in the Product Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. This EGIS service offers access to the water classification data layer.   More information about the dataset is available at https://www.earthdata.nasa.gov/data/catalog/pocloud-opera-l3-dswx-hls-v1-1.0 OPERA was informed by the Satellite Needs Working Group (SNWG), an interagency effort of the U.S. Government dedicated to identifying and addressing Earth observation needs across U.S. civilian federal agencies. Specifications: Temporal coverage: January 1, 2023 – Present Version: 1.0 Spatial Resolution: 3,660 rows and 3,660 columns at 30-m pixel spacing Processing Level: 3 Spatial coverage/reference: Near Global (N: 84 S: -84 E: 180 W: -180) coverage distributed over a projected map coordinate using the Universal Transverse Mercator (UTM) projection (109.8 km × 109.8 km) (EPSG:3857). Sensor: The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. Time interval: Daily Time standard: Coordinated Universal Time (UTC) Update frequency: Updated daily (based on ~3-day satellite acquisition latency)   Fields  objectid: The unique, internal database ID for the specific image. name: The granule/filename of the scene. minps, maxps, lowps, highps: "Pixel Spacing (ps)" metrics used by the ArcGIS server's backend to decide at what zoom levels the image should be visible on a map. category: A backend GIS tag indicating the type of raster (1 for primary data/source raster & 2 for overviews). centerx & centery: The precise center point (Longitude and Latitude) of the image footprint. shape: The geographic footprint of the image. processingdate: The date and time the image was uploaded and processed into the image service. tileid: The specific tile identifier of the product, which is also the tile ID of the input HLS product in the Military Grid Reference System (MGRS). (e.g., T11SPA or T12STF). datetime: The acquisition date and time derived from the input HLS granule name. productgenerationdatetime: The date and time at which the product was generated by OPERA. cloud_coverage: The percentage of pixels that is obscured by clouds. spacecraft_name: The name of the sensor platform (e.g., Landsat-8, Sentinel-2A, Sentinel-2B). downloadurl: The direct web link to download the individual data file (requires NASA Earthdata authentication). urldisplay: A secondary URL field sometimes used by web maps to generate clickable links in pop-up windows. Colormap: Values for the B01 WTR layer are a masked interpreted water classification layer. This represents pixel-wise classification into one of three water classes (not water, open water, and partial surface water), HLS snow/ice, HLS cloud/cloud shadow and adjacent to cloud/cloud shadow, or no data classes.  Intended Use: This service provides the ability to perform temporal trend analysis, zonal statistics, and to track dynamic water volume/extent changes over time.    Limitations of Use: If opening with the MapViewer in the browser, due to the size of the underlying data, it can take a few minutes for the data to render on the map. Tips: zoom into your area of interest on the map; and/or change time slider settings - see the PODAAC Cookbook tutorial.  Known issues and caveats on usage are described in the Product Documentation. Contact: If you have feedback on this service, please provide them via this form. For questions about this service, please contact podaac@podaac.jpl.nasa.gov For tutorials on using this service, please visit the PO.DAAC Cookbook.

Overview The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis. Intended use Provides on‑demand access to harmonized Landsat and Sentinel‑2 imagery for visualization and analysis without requiring users to download large datasets. Enabling time‑filtered, area‑specific queries, and dynamic processing useful for monitoring land, vegetation, and water changes. Limitations of Use HLSL30 v02 data contain several known limitations related to atmospheric correction, cloud masking, geolocation, and metadata that users should carefully consider before analysis. A full description of these known issues is available here. Latency: Imagery tiles are configured to render at a 150 meter scale. Dynamic Mosaic rendering can take an extended period of time. Overview Tiles: This service uses overview tiles for any rendering greater than 150 meter scale using the most recent "best" granules within two weeks. These are static images that persist throughout temporal range queries unless within the specified scale. Current pop ups are configured to display "_overview_" in the tile name and have a category of "Overview".  If opening with the MapViewer in the browser, due to the size of the underlying data, it can take a few minutes for the data to render on the map. Tips: zoom into your area of interest on the map and/or change time slider settings Key Properties: Geographic Coverage: Global Land Temporal Coverage: April 2013 - present Spatial Resolution: 30-meter Revisit Time: ~1.6 Days Update Frequency: Daily Product Level: Level-3 Version: 2.0 Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84 Service Coordinate System: Web Mercator Auxiliary Sphere (EPSG:3857) Multispectral Bands NameDescriptionUnitsData TypeFill ValueScale FactorBand 1Coastal AerosolN/Aint16-99990.0001Band 2BlueN/Aint16-99990.0001Band 3GreenN/Aint16-99990.0001Band 4RedN/Aint16-99990.0001Band 5NIRN/Aint16-99990.0001Band 6SWIR 1N/Aint16-99990.0001Band 7SWIR 2N/Aint16-99990.0001Band 9CirrusN/Aint16-99990.0001Band 10TIRS 1N/Aint16-99990.01Band 11TIRS 2N/Aint16-99990.01FmaskQuality BitsBit Fielduint8255N/ASAASun Azimuth AngleDegreeuint16400000.01SZASun Zenith AngleDegreeuint16400000.01VAAView Azimuth AngleDegreeuint16400000.01VZAView Zenith AngleDegreeuint16400000.01Processing Templates Server-side renderings available on this layer (select via the Rendering / Image Display options). The default rendering is Natural Color. Band-combination templates are for visualization; index templates (NDVI, NDWI, NBR, NDBI) return values suitable for analysis. "DRA" applies dynamic range adjustment; "Cloud Mask" variants suppress pixels flagged by the Fmask quality band. Bathymetric — Bands red, green, coastal/aerosol(4, 3, 1) with fixed stretched applied. Useful in bathymetric mapping applicaitons. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Color Infrared — Bands near-infrared, red, green (5,4,3) with fixed stretch applied. Healthy vegetation is bright red while stressed vegetation is dull red. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Geology — Bands shortwave IR-2, shortwave IR-1, blue (7, 6, 2) with fixed stretched applied. Highlights geological features. (also available: Cloud Mask, DRA, DRA and Cloud Mask) NDVI Colormap — Normalized difference vegetation index (NDVI) with colormap. Dark green represents vigorous vegetation and brown represents sparse vegetation. It is computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDVI Raw — Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDWI Raw — The NDWI index is computed as Green(Band03)-NIR(Band05)/ Green(Band03)+NIR(Band05) and is most suitable for mapping of water bodies, droughts, boundary evaluation. Water bodies have strong absorbability and low radiation within the visible to infrared spectral ranges and this index is based on this phenomenon. (also available: Cloud Mask) Natural Color — Natural Color bands red, green, blue(4, 3, 2) displayed with fixed stretch applied. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Normalized Burn Ratio — The NBR index is computed as NIR(Band05)-SWIR(Band7)/NIR(Band05)+SWIR(Band7) and appropriate for highlighting burned areas and estimating fire severity. It highlights burnt areas in fire zones greater than 500 acres. You will need to generate a differenced NBR image by subtracting a post-fire image from a pre-fire image. The resulting darker pixels represent burned areas. (also available: Cloud Mask) Normalized Difference Built-Up Index — The NDBI index is computed as SWIR(Band6)-NIR(Band05)/ SWIR(Band6)+NIR(Band05) and is suitable for watershed runoff predictions, tracking urbananisation and land-use planning. It highlights urban areas with higher reflectance in the SWIR region, compared to the NIR region. This index also contains a cloud mask. (also available: Cloud Mask) Short-wave Infrared — Bands shortwave infrared-2, shortwave infrared-1, red (7, 6, 4) with fixed stretch. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Resource Links: Download Data User Guide General Documentation For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum Citation: Neigh, C., Ju, J., Roger, J.-C., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., & Justice, C. (2021). HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 [Dataset]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/HLS/HLSL30.002

Overview The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis. Intended use Provides on‑demand access to harmonized Landsat and Sentinel‑2 imagery for visualization and analysis without requiring users to download large datasets. Enabling time‑filtered, area‑specific queries, and dynamic processing useful for monitoring land, vegetation, and water changes. Limitations of Use HLSS30 v02 data contain several known limitations related to atmospheric correction, cloud masking, geolocation, and metadata that users should carefully consider before analysis. A full description of these known issues is available here. Latency: Imagery tiles are configured to render at a 150 meter scale. Dynamic Mosaic rendering can take an extended period of time. Overview Tiles: This service uses overview tiles for any rendering greater than 150 meter scale using the most recent "best" granules within two weeks. These are static images that persist throughout temporal range queries unless within the specified scale. Current pop ups are configured to display "_overview_" in the tile name and have a category of "Overview".  If opening with the MapViewer in the browser, due to the size of the underlying data, it can take a few minutes for the data to render on the map. Tips: zoom into your area of interest on the map and/or change time slider settings Key Properties: Geographic Coverage: Global Land Temporal Coverage: November 2015 - present Spatial Resolution: 30-meter Revisit Time: ~1.6 Days Update Frequency: Daily Product Level: Level-3 Version: 2.0 Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84 Service Coordinate System: Web Mercator Auxiliary Sphere (EPSG:3857) Multispectral Bands NameDescriptionUnitsData TypeFill ValueScale FactorBand 1Coastal AerosolN/Aint16-99990.0001Band 2BlueN/Aint16-99990.0001Band 3GreenN/Aint16-99990.0001Band 4RedN/Aint16-99990.0001Band 5Red Edge 1N/Aint16-99990.0001Band 6Red Edge 2N/Aint16-99990.0001Band 7Red Edge 3N/Aint16-99990.0001Band 8NIR BroadN/Aint16-99990.0001Band 8ANIR NarrowN/Aint16-99990.0001Band 9Water VaporN/Aint16-99990.0001Band 10CirrusN/Aint16-99990.0001Band 11SWIR 1N/Aint16-99990.0001Band 12SWIR 2N/Aint16-99990.0001FmaskQuality BitsBit Fielduint8255N/ASAASun Azimuth AngleDegreeuint16400000.01SZASun Zenith AngleDegreeuint16400000.01VAAView Azimuth AngleDegreeuint16400000.01VZAView Zenith AngleDegreeuint16400000.01Processing Templates Server-side renderings available on this layer (select via the Rendering / Image Display options). The default rendering is Natural Color. Band-combination templates are for visualization; index templates (NDVI, NDWI, NBR, NDBI) return values suitable for analysis. "DRA" applies dynamic range adjustment; "Cloud Mask" variants suppress pixels flagged by the Fmask quality band. Bathymetric — Bands red, green, coastal/aerosol(4, 3, 1) with fixed stretched applied. Useful in bathymetric mapping applicaitons. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Color Infrared — Bands near-infrared, red, green (5,4,3) with fixed stretch applied. Healthy vegetation is bright red while stressed vegetation is dull red. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Geology — Bands shortwave IR-2, shortwave IR-1, blue (7, 6, 2) with fixed stretched applied. Highlights geological features. (also available: Cloud Mask, DRA, DRA and Cloud Mask) NDVI Colormap — Normalized difference vegetation index (NDVI) with colormap. Dark green represents vigorous vegetation and brown represents sparse vegetation. It is computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDVI Raw — Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4) and is suitable for vegetation, land cover and plant health monitoring. (also available: Cloud Mask) NDWI Raw — The NDWI index is computed as Green(Band03)-NIR(Band05)/ Green(Band03)+NIR(Band05) and is most suitable for mapping of water bodies, droughts, boundary evaluation. Water bodies have strong absorbability and low radiation within the visible to infrared spectral ranges and this index is based on this phenomenon. (also available: Cloud Mask) Natural Color — Natural Color bands red, green, blue(4, 3, 2) displayed with fixed stretch applied. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Normalized Burn Ratio — The NBR index is computed as NIR(Band05)-SWIR(Band7)/NIR(Band05)+SWIR(Band7) and appropriate for highlighting burned areas and estimating fire severity. It highlights burnt areas in fire zones greater than 500 acres. You will need to generate a differenced NBR image by subtracting a post-fire image from a pre-fire image. The resulting darker pixels represent burned areas. (also available: Cloud Mask) Normalized Difference Built-Up Index — The NDBI index is computed as SWIR(Band6)-NIR(Band05)/ SWIR(Band6)+NIR(Band05) and is suitable for watershed runoff predictions, tracking urbananisation and land-use planning. It highlights urban areas with higher reflectance in the SWIR region, compared to the NIR region. This index also contains a cloud mask. (also available: Cloud Mask) Short-wave Infrared — Bands shortwave infrared-2, shortwave infrared-1, red (7, 6, 4) with fixed stretch. (also available: Cloud Mask, DRA, DRA and Cloud Mask) Resource Links: Download Data User Guide General Documentation For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum Citation: Neigh, C., Ju, J., Roger, J.-C., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., & Justice, C. (2021). HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0 [Dataset]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/HLS/HLSS30.002

Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases. For more information, see the GlobalMLBuildingFootprints repository on GitHub. ## Building footprint creation The building extraction is done in two stages: 1. Semantic Segmentation – Recognizing building pixels on an aerial image using deep neural networks (DNNs) 2. Polygonization – Converting building pixel detections into polygons **Stage 1: Semantic Segmentation** !Semantic segmentation **Stage 2: Polygonization** !Polygonization ## Data assets The building footprints are provided as a set of geoparquet datasets in [Delta][delta] table format. The data are partitioned by 1. Region 2. quadkey at [Bing Map Tiles][tiles] level 9 Each `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area. Note that older items in this dataset are *not* spatially partitioned. We recommend using data with a processing date of 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata for each item (see below). ## Delta Format The collection-level asset under the `delta` key gives you the fsspec-style URL to the Delta table. This can be used to efficiently query for matching partitions by `Region` and `quadkey`. See the notebook for an example using Python. ## STAC metadata This STAC collection has one STAC item per region. The `msbuildings:region` property can be used to filter items to a specific region, and the `msbuildings:quadkey` property can be used to filter items to a specific quadkey (though you can also search by the `geometry`). Note that older STAC items are not spatially partitioned. We recommend filtering on items with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection summary for `msbuildings:processing-date` for a list of valid values. [delta]: https://delta.io/ [tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system

The European Space Agency (ESA) WorldCover product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of Sentinel-1 radar data and Sentinel-2 imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in cloud-optimized GeoTIFF format. The WorldCover product is developed by a consortium of European service providers and research organizations. VITO (Belgium) is the prime contractor of the WorldCover consortium together with Brockmann Consult (Germany), CS SI (France), Gamma Remote Sensing AG (Switzerland), International Institute for Applied Systems Analysis (Austria), and Wageningen University (The Netherlands). Two versions of the WorldCover product are available: - WorldCover 2020 produced using v100 of the algorithm - WorldCover 2020 v100 User Manual - [WorldCover 2020 v100 Validation Report]() - WorldCover 2021 produced using v200 of the algorithm - [WorldCover 2021 v200 User Manual]() - [WorldCover 2021 v200 Validaton Report]() Since the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.

Overview  Modified Anthocyanin Reflectance Index (mARI) from the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). mARI describes the canopy content of anthocyanins in terrestrial vegetation. mARI is calculated as ((1/R550)-(1/R705))*R800, where R550, R705, and R800 are the surface reflectance at 550, 705 and 800 nanometers (nm), respectively (Gitelson et al. 2007). Data provided as global, 8-day averages mapped to a standard, equal-angle, latitude-longitude grid of 0.02 degrees, which equates to ~2 kilometers (km) at the equator. Data has been corrected for atmospheric scattering and absorption. Pixels with no data values are masked (excluded) due to presence of water, cloud cover, or other features marked by quality control flags as described below. Data Layer Summary Name, units Modified Anthocyanin Reflectance Index (mARI) Measure of terrestrial canopy anthocyanin content, a photoprotective pigment. mARI is unitless. Spatial resolution 2 km2 Level-3, mapped to a standard grid, global coverage Time Resolution 8 day average Each pixel represents an average of the valid, daily values observed within that grid element during the 8-day interval (approx 1 week). Note: 8 day average data layer is calculated from daily, "refined" (archive-quality) Level 3 PACE mARI products available here from NASA Earthdata in NetCDF format. Earthdata account required. Each NetCDF file includes all 10 PACE vegetation indexes, including mARI. Data Version Version 3.1 Data versions change as improved calibration, validation, algorithm refinements, etc are applied over time. See Version 3.1 Release Notes for details. Latency ~1 month This imagery service provides “refined” (archive-quality) PACE data products from March 2024-present, which have an approx 1 month latency. Note: Daily, “near real time (NRT)” Level 3 PACE mARI products with less than approximately 24-48 hour latency available here from NASA Earthdata in NetCDF format. Earthdata account required. Each NetCDF file includes all 10 PACE vegetation indexes, including mARI. Intended Use: What can I do with this layer? There are three major classes of plant pigments: chlorophylls, carotenoids, and anthocyanins. The mARI product provides information on changes in vegetation anthocyanin content and photophysiological status. Anthocyanins play a variety of roles in plants and are a common pigment in all leaves. For certain species or growth stages anthocyanins may even become the dominant type of pigment. Anthocyanins can alter the light environment within a leaf and thus regulate photosynthesis and limit photoinhibition and photobleaching. Beyond photoprotection, anthocyanins may provide some protection from freezing and drought stress as well as aid recovery after injury. The time (8 day average) and space (2 km2) resolution of this data layer enables enables weekly to seasonal and annual insight at local to global scales.   Limitations of Use: What should I know about the data? Data Status (Maturity): This product is provisional, which means that product results have been reviewed and are in family with heritage data products or other basis of expectation, but have not yet been extensively validated and so may still contain significant errors. Data layer is sufficient for qualitative determinations of product fitness-for-purpose, for testing by operational users, and may be suitable for scientific publication. Data Masking: This is a Level 3, globally-binned product from NASA’s Ocean Biology Processing Group (OBPG). By default, OBPG Level 3 data values are masked (value is set to null, or no data) according to a standard set of quality control parameters. Please see Level 3 Algorithm Usage Constraints for masking parameter details. Algorithm Theoretical Basis Document (ATBD): See ATBD for algorithm details including scientific theory, calculations, etc. https://doi.org/10.5067/8L77O0Y8GHV8.   Where do I go with further questions? Visit NASA’s Earthdata Forum where users can post questions that are answered by the personnel working at NASA with the PACE mission and/or the Ocean Biology Distributed Active Archive Center (OB.DAAC) that processes and distributes PACE data. First, follow this link to view only “PACE” and “OBDAAC” tagged questions on the Earthdata Forum. Then search and read through previous posts for your question. If it has not yet been asked please post your question in the Forum and be sure "PACE" and "OBDAAC" filter tags are applied.     Mission and Product Background PACE Mission Overview: Launched in February 2024, NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observatory uniquely provides near-daily, hyperspectral coverage of Earth’s surface, plus hyperspectral and hyperangular polarimetry. PACE is a comprehensive Earth System mission, delivering all new, space-based information on the health and diversity of aquatic and terrestrial life (algae, plants), aerosol properties & modes (e.g. smoke, dust), cloud types, and more. The Ocean Color Instrument (OCI), PACE's primary sensor, is a hyperspectral (5 nm resolution) imaging spectrometer that measures properties of light from the ultraviolet (UV, 340 nm) through visible and near infrared (NIR, 895 nm) portions of the electromagnetic spectrum in 1.25 to 2.5 nm steps, plus 7 shortwave infrared (SWIR) bands (Werdell et al. 2019). Observations from OCI are moderate spatial resolution (1.2 km2), enabling a relatively frequent revisit time of 1-2 days. This combination of continuous, global coverage and spectral capabilities is unmatched by any other satellite platform, public or private. PACE benefits society by expanding our foundational knowledge of Earth and enabling novel, space-based science and applications tools across water resources (e.g. fisheries), air quality, land monitoring & management, and more.   Terrestrial Remote Sensing from an Ocean Color Sensor? Designed and optimized to operate across a broad dynamic range, from the dark target of the ocean to bright, highly reflective cloud tops, PACE’s OCI also collects surface reflectance data from the world's terrestrial environments across 122 bands, resolving spectral properties of the Earth more finely than previous multispectral missions could achieve. Work thus far has demonstrated viability of OCI’s land data products for terrestrial investigations (Caplan et al. 2025). OCI’s coverage and capabilities enable provision of both novel, hyperspectral-enabled land observations as well as maintenance and extension of certain historical multispectral data records. Advanced vegetation indexes (VIs) enabled by OCI’s narrow-wavelength surface reflectance measurements provide continuous, synoptic insights into foliar pigment amounts (chlorophylls, carotenoids, and anthocyanins), water content, and photosynthetic light use efficiency across the globe each day. Heritage VIs (e.g., normalized difference vegetation index, NDVI; reviewed in Huang et al. 2021) can also be calculated by aggregating (averaging) OCI’s narrow bandwidths across ranges that approximate the multi-spectral, broadband reflectances of instruments such as MODIS, see the Mathematical Theory section of the ATBD for details. See the full list of heritage and hyperspectral-enabled VIs produced by PACE here.   What other PACE data layers are available? This data layer is 1 of 11 PACE products initially selected for the Living Atlas catalog from the mission’s comprehensive aquatic, atmospheric, and terrestrial product catalog. Click here to view all 11 PACE Living Atlas imagery services. The Level 3, 122-band hyperspectral surface reflectance data (SFREFL) used to derive all VIs is available from NASA Earthdata to download at 2 kilometer resolution in NetCDF format. To access PACE mission data products available from NASA, first learn about PACE data access here, next decide which PACE product(s) you would like to use, then go to NASA Earthdata to find PACE products in NetCDF format, which can be downloaded and then imported into ArcGIS. An Earthdata account is needed to download data from NASA, sign up here. To learn about using Earthdata Search to find data, watch this tutorial. How can I stay up to date with the PACE mission?   Join the PACE Community for mission news, events, new product release announcements, and to receive our newsletters. Would you like to see imagery services for more PACE products? Please let us know here.   References: Algorithm Theoretical Basis Document: https://doi.org/10.5067/8L77O0Y8GHV8 Caplan, S. and Huemmrich, K. F. (2025) Unveiling PACE OCI’s hyperspectral terrestrial data products. Remote Sensing Letters, 16(4), 422–433. https://doi.org/10.1080/2150704X.2025.2470905 Gitelson, A. A., Merzlyak, M. N., and Chivkunova, O. B. (2007) Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1):38–45. https://doi.org/10.1562/0031-8655(2001)0740038OPANEO2.0.CO2 Huang, S., Tang, L., Hupy, J. P., Wang, Y. and Shao, G. (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1-6. https://doi.org/10.1007/s11676-020-01155-1 Werdell, P. J. et al. (2019) The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances. Bulletin of the American Meteorological Society, 100, 1775–1794. https://doi.org/10.1175/BAMS-D-18-0056.1

Point-based air monitoring observations from the City of Minneapolis Community Air Monitoring project. Each point records sensor readings such as timestamp, humidity, temperature, pressure, and particulate matter counts and concentrations. Published by the City of Minneapolis.

This Collection provides Sentinel-5P Level-2 CO products, which contains high-resolution imagery of carbon monoxide concentrations.

Current and recent earthquakes alerts from the Global Disaster Alert and Coordination System (GDACS), a cooperation of the United Nations and the European Commission, with alert level, severity and affected population.

Upper Hutt's current District Plan Features. See Upper Hutt City Council's District Plan for more information.

City Centre port noise. The overlay is applied to land near Auckland’s downtown port. Buildings accommodating noise sensitive land uses within the overlay must be insulated to achieve an internal noise level appropriate to the activity. This will ensure that noise sensitive land uses within the overlay achieve a good standard of amenity and the port is able to operate efficiently.

uav imagery by WebODM, acquired 2026-01-06 to 2026-01-06, 5.9 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2026-01-02 to 2026-01-02, 4.4 cm per pixel, published on OpenAerialMap.

This Collection provides Sentinel-5P Level-2 CH4 products, which contains high-resolution imagery of methane concentrations.

OPERA Level-2 Radiometric Terrain Corrected (RTC) products in VH polarization generated from Sentinel-1 SAR imagery, processed by OPERA (JPL). Values for the VH (cross-polarized) polarization are generally driven by volume scattering, with more complex volume scatterers (such as dense vegetation) returning higher backscatter values. Surface water generally appears very dark, as it is predominantly a surface scatterer; most returns remain in the primary polarization. OPERA RTC-S1 data is available over all landmasses (excluding Antarctica) for Sentinel-1 acquisitions starting April 14, 2016, but only products from October 4, 2023, onward are included in this image service. The source rasters referenced in this service are hosted by the Alaska Satellite Facility in NASA's Earthdata Cloud, and the service is updated daily to include new products. More information about the dataset is available at https://asf.alaska.edu/datasets/daac/opera/. OPERA includes partners from NASA’s Goddard Space Flight Center, the U.S. Geological Survey (USGS), the University of Maryland College Park, the University of Alaska Fairbanks, Southern Methodist University, and EarthScope. Refer to https://www.jpl.nasa.gov/go/opera for more details.  This service is still under development. Please contact hjkristenson@alaska.edu if you encounter any problems.

Overview: OPERA Level-3 Dynamic Surface Water Extent (DSWx) product from Harmonized Landsat-8/9 and Sentinel-2A/B/C (HLS), processed and developed by OPERA (JPL/USGS). A representation of the confidence associated with the WTR classification that is based on a combination of Diagnostic Layer (DIAG) results and quality assurance information provided with the input HLS data. For example, the Open water class of the WTR layer is split into two classes: High Confidence and Moderate Confidence while WTR Partial surface water Class pixels of WTR are flagged as either Partial surface water Conservative or Partial surface water Aggressive, with the latter exhibiting less certainty. As in the WTR layers, pixels for which water retrievals are difficult or impossible due to HLS snow/ice, HLS cloud/shadow, or fill values are also noted in the CONF layer.   This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning January 2023. Known issues and caveats on usage are described in the Product Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. This EGIS service offers access to the confidence data layer. More information about the dataset is available at https://www.earthdata.nasa.gov/data/catalog/pocloud-opera-l3-dswx-hls-v1-1.0   OPERA was informed by the Satellite Needs Working Group (SNWG), an interagency effort of the U.S. Government dedicated to identifying and addressing Earth observation needs across U.S. civilian federal agencies.   Specifications: Temporal coverage: January 1, 2023 – Present Version: 1.0 Spatial Resolution: 3,660 rows and 3,660 columns at 30-m pixel spacing Processing Level: 3 Spatial coverage/reference: Near Global (N: 84 S: -84 E: 180 W: -180) coverage distributed over a projected map coordinate using the Universal Transverse Mercator (UTM) projection (109.8 km × 109.8 km) (EPSG:3857). Sensor: The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. Time interval: Daily Time standard: Coordinated Universal Time (UTC) Update frequency: Updated daily (based on ~3-day satellite acquisition latency)   Fields  objectid: The unique, internal database ID for the specific image. name: The granule/filename of the scene. minps, maxps, lowps, highps: "Pixel Spacing (ps)" metrics used by the ArcGIS server's backend to decide at what zoom levels the image should be visible on a map. category: A backend GIS tag indicating the type of raster (1 for primary data/source raster & 2 for overviews). centerx & centery: The precise center point (Longitude and Latitude) of the image footprint. shape: The geographic footprint of the image. processingdate: The date and time the image was uploaded and processed into the image service. tileid: The specific tile identifier of the product, which is also the tile ID of the input HLS product in the Military Grid Reference System (MGRS). (e.g., T11SPA or T12STF). datetime: The acquisition date and time derived from the input HLS granule name. productgenerationdatetime: The date and time at which the product was generated by OPERA. cloud_coverage: The percentage of pixels that is obscured by clouds. spacecraft_name: The name of the sensor platform (e.g., Landsat-8, Sentinel-2A, Sentinel-2B). downloadurl: The direct web link to download the individual data file (requires NASA Earthdata authentication). urldisplay: A secondary URL field sometimes used by web maps to generate clickable links in pop-up windows. Colormap: The B03 CONF layer is a representation of the confidence associated with the WTR classification that is based on a combination of Diagnostic Layer (DIAG) results and quality assurance information provided with the input HLS data. For example, the Open water class of the WTR layer is split into two classes: High Confidence and Moderate Confidence while WTR Partial surface water Class pixels of WTR are flagged as either Partial surface water Conservative or Partial surface water Aggressive, with the latter exhibiting less certainty. As in the WTR layers, pixels for which water retrievals are difficult or impossible due to HLS snow/ice, HLS cloud/shadow, or fill values are also noted in the CONF layer.  Intended Use: This service provides the ability to perform temporal trend analysis, zonal statistics, and to track dynamic water volume/extent changes over time.    Limitations of Use: If opening with the MapViewer in the browser, due to the size of the underlying data, it can take a few minutes for the data to render on the map. Tips: zoom into your area of interest on the map; and/or change time slider settings - see the PODAAC Cookbook tutorial.  Known issues and caveats on usage are described in the Product Documentation. Contact: If you have feedback on this service, please provide them via this form. For questions about this service, please contact podaac@podaac.jpl.nasa.gov For tutorials on using this service, please visit the PO.DAAC Cookbook.

The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated. **NOTE:** Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders. For more information, visit the National Wetlands Inventory homepage. ## STAC Metadata In addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a geoparquet asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` role to select just the geoparquet assets. See the Example Notebook for more.

This Collection provides Sentinel-5P Level-2 Cloud products, which contains high-resolution imagery of cloud parameters.

This Collection provides Sentinel-5P Level-2 NP BD6 products, which contains information on cloud and scene homogeneity.

aircraft imagery by Andre Chouin, acquired 2025-04-03 to 2025-04-03, 3.9 cm per pixel, published on OpenAerialMap.

This Collection provides Sentinel-5P Level-2 NP BD7 products, which contains information on cloud and scene homogeneity.

This Collection provides Sentinel-5P Level-2 O3 products, which contains high-resolution imagery of total column ozone concentrations.

This Collection provides Sentinel-5P Level-2 NP BD7 products, which contains information on cloud and scene homogeneity.

The Sentinel-6 P4 Level 1B Non Time Critical (NTC) product delivers the highest quality ocean surface measurements (sea surface height, wind speed, and wave height), designed for climate research and long-term monitoring. The data are organized by full satellite passes and delivered within 60 days of acquisition. The product is provided in NetCDF format and is subject to reprocessing to maintain consistency with evolving standards.

This Collection provides Sentinel-5P Level-2 NP BD3 products, which contains information on cloud and scene homogeneity.

This Collection provides Sentinel-3 SRAL Level-2 Ocean Altimetry products, which contain data on ocean radar altimetry measurements.

The Sentinel-6 AMR-C Near Real-Time (NRT) product provides low-latency measurements of brightness temperatures at 18.7, 23.8, and 34 GHz, acquired with linear polarisation. These data enable rapid estimation of wet tropospheric path delay and atmospheric attenuation corrections for altimetry in near real-time. The product is segmented into 10-minute granules and delivered within three hours of acquisition, supporting operational sea level and weather forecasting systems.

This Collection provides Sentinel-3 SRAL Level-2 Land Altimetry Sea Ice products, which contains estimations of the radar freeboard over the Arctic and Antarctic sea ice.

This Collection provides Sentinel-5P Level-2 SO2 products, which contains high-resolution imagery of atmospheric sulfur dioxide concentrations.

The identification of species is one of the most basic, and yet critically important, issues in biology with far-reaching potential implications for fields such as biodiversity conservation, population ecology and epidemiology. Morphology has long been the primary tool biologists have used to categorize life. However, we now know that a significant portion of natural diversity is morphologically hidden, and therefore, we must integrate nonmorphological tools into the description of biodiversity. Here, we demonstrate the utility of multilocus population genetic data for identifying and characterizing cryptic species complexes, even when species share large amounts of genetic variability. Specifically, we have used DNA sequence data from 12 genomic regions to characterize two widespread species complexes in the coral genus Acropora: A. cytherea and A. hyacinthus. These two morphospecies have each been sampled from 5 to 7 locations throughout their Indo-Pacific distributions, and with the use of structure and hierarchical clustering, we demonstrate the presence of at least six widespread cryptic species within these two morphospecies complexes. After identifying cryptic lineages, we then utilize the genetic data to examine the history of introgressive hybridization within and between these morphospecies complexes. Our data indicate that these two complexes form a global syngameon with consistent patterns of introgression between species across large geographic distributions. Citation:  Southwestern Pacific OBIS (2015). Extensive sympatry, cryptic diversity and introgression throughout the geographic distribution of two coral species complexes. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 20 records. Online:  http://nzobisipt.niwa.co.nz/resource.do?r=smpatry_of_coral Released on April 7, 2015. Bibliography:  LADNER, J. T. and PALUMBI, S. R. (2012), Extensive sympatry, cryptic diversity and introgression throughout the geographic distribution of two coral species complexes. Molecular Ecology, 21: 2224–2238. doi: 10.1111/j.1365-294X.2012.05528.x_________________________________________________________________________________________________________________ Item Page Created: 2016-06-09 02:30 Item Page Last Modified: 2025-04-05 18:55 Owner: NIWA_OpenData Sympatry_of_two_coral_species_complexes No data edit dates available Fields: id,modified,language,bibliographicCitation,institutionCode,collectionCode,datasetName,basisOfRecord,dynamicProperties,catalogNumber,occurrenceRemarks,individualCount,sex,lifeStage,occurrenceStatus,associatedTaxa,eventID,samplingProtocol,eventDate,startDayOfYear,year,month,day,fieldNumber,waterBody,country,stateProvince,county,locality,minimumDepthInMeters,maximumDepthInMeters,decimalLatitude,decimalLongitude,geodeticDatum,coordinateUncertaintyInMeters,footprintWKT,identifiedBy,typeStatus,scientificNameID,scientificName,kingdom,phylum,class,order_,family,genus,subgenus,specificEpithet,infraspecificEpithet,scientificNameAuthorship,FID

This collection offers 48 parameters at near-surface level from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes. ## Data collection height There are 3 forecast heights used within the near-surface this collection: * Surface: the default collection height * Screen level: 1.5m above the surface * Wind parameters: 10m above the surface ## Timesteps For most parameters, the following time steps are available, see exceptions below: * every hour from 0 to 54 hours * every 3 hours from 57 to 144 hours * every 6 hours from 150 to 168 hours Exceptions (for `accumulation`, `min`, `max` and `mean` parameters): * Height of orography (height_of_orography) is only available at 0H * Hourly latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT01H) is only available every hour from 1 to 54 hours * 3H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT03H) is only available every three hours from 57 to 144 hours * 6H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT06H) is only available every six hours from 150 to 168 hours * Hourly precipitation accumulation (precipitation_accumulation-PT01H) is only available every hour from 1 to 54 hours * 3H precipitation accumulation (precipitation_accumulation-PT03H) is only available every three hours from 57 to 144 hours * 6H precipitation accumulation (precipitation_accumulation-PT06H) is only available every six hours from 150 to 168 hours * Radiation flux in longwave downward at surface (radiation_flux_in_longwave_downward_at_surface) has six hourly timesteps from 150 to 162 hours * Radiation flux in uv downward at surface (radiation_flux_in_uv_downward_at_surface) has three hourly timesteps from 0 to 144 hours and six hourly timesteps from 150 to 162 hours * Hourly rainfall accumulation (rainfall_accumulation-PT01H) is only available every hour from 1 to 54 hours * 3H rainfall accumulation (rainfall_accumulation-PT03H) is only available every three hours from 57 to 144 hours * 6H rainfall accumulation (rainfall_accumulation-PT06H) is only available every six hours from 150 to 168 hours * Hourly rainfall rate from convection max (rainfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours * 3H rainfall rate from convection max (rainfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours * 6H rainfall rate from convection max (rainfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours * Hourly snowfall rate from convection max (snowfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours * 3H snowfall rate from convection max (snowfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours * 6H snowfall rate from convection max (snowfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours * Hourly snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT01H) is only available every hour from 1 to 54 hours * 3H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT03H) is only available every three hours from 57 to 144 hours * 6H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT06H) is only available every six hours from 150 to 168 hours * Hourly temperature at screen level max (temperature_at_screen_level_max-PT01H) is only available every hour from 1 to 54 hours * 3H temperature at screen level max (temperature_at_screen_level_max-PT03H) is only available every three hours from 57 to 144 hours * 6H temperature at screen level max (temperature_at_screen_level_max-PT06H) is only available every six hours from 150 to 168 hours * Hourly temperature at screen level min (temperature_at_screen_level_min-PT01H) is only available every hour from 1 to 54 hours * 3H remperature at screen level min (temperature_at_screen_level_min-PT03H) is only available every three hours from 57 to 144 hours * 6H temperature at screen level min (temperature_at_screen_level_min-PT06H) is only available every six hours from 150 to 168 hours * Hourly wind gust at 10m max (wind_gust_at_10m_max-PT01H) is only available every hour from 1 to 54 hours * 3H wind gust at 10m max (wind_gust_at_10m_max-PT03H) is only available every three hours from 3 to 144 hours * 6H wind gust at 10m max (wind_gust_at_10m_max-PT06H) is only available every six hours from 54 to 168 hours ## Update Frequency The model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC). The runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours. The forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available. ## Archive length and latency As of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. The data is typically available 6 hours after the model run time. ## Technical specs The data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: * Variables store the data * Dimensions give relevant dimension information for the variables * Attributes provide auxiliary information about the variables or dataset itself NetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers. Iris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. Find further support on using Iris with NetCDF files. ## Help us improve the data services we offer Join the Met Office research panel to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities. ## How to cite Met Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). ## License British Crown copyright 2023-2025, the Met Office, is licensed under CC BY-SA. ## Providers Met Office See all datasets managed by Met Office. ## Contact [servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.

This Collection provides Sentinel-5P Level-2 CO products, which contains high-resolution imagery of carbon monoxide concentrations.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows housing costs as a percentage of household income by age. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey.  This layer is symbolized to show the predominant housing type for householders where the householder is age 65+ and spending at least 30% of their income on housing. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B25072, B25093Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).    The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

This Collection provides Sentinel-3 SRAL Level-2 Land Altimetry Hydrology products, which contains surface topography estimations of lakes and rivers.