Advances in data availability, Earth observation technologies, and geospatial sciences have transformed our ability to map Global Surface Water Extents (GSWE). However, traditional GSWE mapping has been limited to static estimates, with more recent efforts focusing on annual averages and temporal attributes like frequency and occurrence of long-term variations. We harnessed remotely sensed Sentinel-2 based near real-time Dynamic World land cover product to produce the first public, routinely available 10-meter resolution global surface water datasets. Our key contribution is an Open Science operational framework to rapidly extract the latest available Dynamic World products every 2-5 days, run geospatial analytics, and create actionable water information for educators, researchers, and stakeholders at any scale of practical interest.

This dataset was developed by the Hydrology & Hydroinformatics Innovation Lab at the University of Texas at Arlington, United States.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Retirement Notice: This item is in mature support as of February 2023 and will be retired in December 2025. 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 displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map Viewer To show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021 By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this: 4. Click the styles button.5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off. Showing just one pair of years in ArcGIS Pro To show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well. How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This dataset contains preprocessed Sentinel-2 imagery and corresponding Dynamic World land-cover labels for six wetland areas in the Netherlands. It was created to support land-cover classification and segmentation tasks in ecologically dynamic floodplain environments. The data covers the period January 2017 to November 2024 and includes only scenes with less than 5% cloud cover.

Sentinel-2 imagery was retrieved using the Google Earth Engine (GEE) API from the COPERNICUS/S2 SR HARMONIZED collection, which provides Harmonized Level-2A data at 10 m spatial resolution. From the 26 available bands, 9 were selected based on their relevance for wetland delineation: RGB, Red Edge 1–3, Near-Infrared (NIR), and Shortwave Infrared (SWIR 1–2). The imagery was tiled into 256×256 pixel patches and filtered for quality (e.g., excluding patches with >10% black pixels).

Dynamic World land-cover labels (Brown et al., 2022) were used to generate pixel-wise semantic segmentation masks by selecting the most probable class (out of 9 land-cover types) for each pixel. The resulting masks are single-band images where pixel values 0–8 represent land-cover classes as follows:

0: Water 1: Trees 2: Grass 3: Flooded Vegetation 4: Crops 5: Shrub & Scrub 6: Built 7: Bare 8: Snow & Ice

The dataset includes the following splits:

Training set: Gelderse Poort, Oostvaardersplassen, Loosdrechtse Plassen, Land van Saeftinghe (1,701 images)

Validation set: Lauwersmeer (948 images)

Test set: Biesbosch (1,140 images)

This resource enables benchmarking of supervised and self-supervised learning methods for wetland classification in medium-resolution optical satellite data.

Reference:Brown, C.F., Brumby, S.P., Guzder-Williams, B., Birch, T., Hyde, S.B., Mazzariello, J., Czerwinski, W., Pasquarella, V.J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A.M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01307-4

Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD.

"*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".

CLCD in 2023 is now available.

1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the Collection 2 SR data to update the CLCD.

2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check here for more details.

3. Internal overviews and color tables are built into each file to speed up software loading and rendering.

The Global Land Cover Characterization Project was established to meet science data requirements identified by the International Geosphere and Biosphere Programme (IGBP), and the U. S. Global Change Research Program. The overall goal is to produce flexible large-area land cover databases to meet evolving requirements of the earth science research community.

The project was implemented by the United States Geological Survey/EROS Data Center (EDC), the University of Nebraska-Lincoln (UNL), and the Joint Research (JRC) of European Commission. This effort is part of the National Aeronautic's and Space Administration (NASA) Earth Observing System Pathfinder Program.

Funding for the project was provided by the USGS, NASA, the U.S. Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), U.S. Forest Service (USFS) , and the United Nations Environment Programme.

The data base has been adopted by the International Geosphere-Biosphere Programme Data and Information System office (IGBP-DIS) to fill its requirement for a global 1-km land cover data set.

[Summary provided by the USGS.]

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated from Impact Observatory’s deep learning AI land classification model used a massive training dataset of billions of human-labeled image pixels developed by the National Geographic Society. The global maps were produced by applying this model to the Sentinel-2 scene collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for 10 classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2021 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2021.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. It should be noted that since land use focus does not provide the spatial detail of a land cover map for the built area classification – yards, parks, small groves will appear as built area rather than trees or rangeland classes This layer can also be used in analyses that require land use/land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

Dynamic World Private Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020.By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter.1. Click the filter button.2. Next, click add expression.3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button.5. Under unique values click style options.6. Click the symbol next to No Change at the bottom of the legend.7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro.1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties.2. In the dialogue that comes up, choose the tab that says processing templates.3. On the right where it says processing template, choose the pair of years you would like to display.The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer:Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe.Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes.Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map.Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This dataset contains a comprehensive collection of geospatial raster data (GeoTIFF format) detailing the spatio-temporal impacts of land cover change in the Paguyaman Watershed, Gorontalo, Indonesia, for the years 2016, 2020, and 2024. The data was generated as part of a doctoral dissertation at IPB University.

The dataset includes annual maps for four key sustainability indicators:
1. Erosion Potential: Estimated using the RUSLE model (tons/ha/year).
2. Surface Runoff Potential: Estimated using the SCS-CN model for a 100 mm/24-hour rainfall scenario (runoff depth in mm).
3. Land Productivity: Estimated using the Normalized Difference Vegetation Index (NDVI) from annual Landsat composites, focused on agricultural areas.
4. Water Yield: Estimated using a water balance approach (Precipitation - Evapotranspiration) with CHIRPS and ERA5-Land data (water yield depth in mm).

These datasets are based on a consistent, newly generated set of land cover maps derived from Google Dynamic World data, which are also included. This research was conducted using Google Earth Engine and desktop GIS software. The data is intended to support research in watershed management, hydrology, land use planning, and system dynamics modeling. For complete methodology and interpretation, please refer to the associated publications.

The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6.1 data product provides global land surface phenology metrics at yearly intervals from 2001 to 2021. The MCD12Q2 Version 6.1 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. For pixels with more than two valid vegetation cycles, the data represent the two cycles with the largest NBAR-EVI2 amplitudes.Provided in each MCD12Q2 Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for the total number of vegetation cycles detected for the product year, the onset of greenness, greenup midpoint, maturity, peak greenness, senescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation cycle, as well as overall and phenology metric-specific quality information. SDS layers may be multi-dimensional with up to two valid vegetation cycles. For areas where the NBAR-EVI2 values are missing due to cloud cover or other reasons, the data gaps are filled with good quality NBAR-EVI2 values from the year directly preceding or following the product year.Known Issues Known issues are described in Section 3.2 of the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.Improvements/Changes from Previous Versions The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). The MCD12Q2 Version 6.1 product has an improved approach to snow filtering.

Land cover (LC) is an important terrestrial variable and key information for understanding the interaction between human activities and global change. As the cause and result of global environmental change, land cover change (LCC) influences the global energy balance and biogeochemical cycles. Continuous and dynamic monitoring of global LC is urgently needed. Effective monitoring and comprehensive analysis of LCC at the global scale is rare. Using the latest version of GLASS (The Global Land Surface Satellite) CDRs (Climate Data Records) from 1982 to 2015, we built the first set of CDRs to record the annual dynamics of global land cover (GLASS-GLC) at 5 km resolution using the Google Earth Engine (GEE) platform. Compared to earlier global LC products, GLASS-GLC is characterized by high consistency, more detailed classes, and longer temporal coverage. The average overall accuracy is 85 %. We implemented a systematic uncertainty analysis at the global scale. In addition, we carried out a comprehensive spatiotemporal pattern analysis. Significant changes and patterns at various scales were found, including deforestation and agricultural land expansion in the tropics, afforestation and forest expansion in northern high latitudes, land degradation in Asian grassland and reclamation in northeast China, etc. A global quantitative analysis of human factors showed that the average human impact level in areas with significant LCC was about 25.49 %. The anthropogenic influence has a strong correlation with the noticeable Earth greening. Based on GLASS-GLC, we can conduct long-term LCC analysis, improve our understanding of global environmental change, and mitigate its negative impact. GLASS-GLC will be further applied in Earth system modeling in order to facilitate research on global carbon and water cycling, vegetation dynamics and climate change.

HILDA+ (HIstoric Land Dynamics Assessment+) is a global dataset on annual land use/cover change between 1960-2019 at 1 km spatial resolution. It is based on a data-driven reconstruction approach and integrates multiple open data streams (from high-resolution remote sensing, long-term land use reconstructions and statistics). It covers six generic land use/cover categories: 1: Urban areas, 2: Cropland, 3: Pasture/rangeland, 4: Forest, 5: Unmanaged grass/shrubland, 6: Sparse/no vegetation. […]

This resource will be a part of the collection "CUAHSI Legacy Documents", and will contain 1 pdf.

The purpose of this report is as follows: Experience from the CUAHSI Hydrologic Information Systems project has shown that a multi-phase approach is needed to move from concept to a community facility. The conceptual phase must be followed up by a pilot phase to scope a project more precisely and to determine the best approach to delivering a service. The pilot phase is then followed by a developmental phase when the specific tools and services are “hardened” by testing them with a limited clientele to ensure reliability and operational readiness to serve the community. Only then have the necessary attributes of an operational community service been fully defined. This science plan summarizes the results of these interim community planning efforts.

This image classification of forest cover in the MAV was created using Google Dynamic World (https://www.nature.com/articles/s41597-022-01307-4 - https://dynamicworld.app/) to determine what was classified as forest. This dataset is a result of an automated land classification for every Sentinel image that is released. The code used for this process is as follows. ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') \ .filterBounds(geometry) \ .filterDate(oldstartDate, oldendDate) \ .select('label') \ .mode() \ .eq(1) \ .updateMask(urban) We selected the Dynamic World dataset and filtered by our area of interest by the extents of the Lower Mississippi Joint Venture boundary (i.e. Mississippi Alluvial Valley and West Gulf Coastal Plain ecological bird conservation regions (BCRs).We filtered the dataset based on a start and end date which is the first of 2021 and the last day of 2021.With this dataset each class has a band that represents probability of that pixel having complete coverage of that class (https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1#bands)Data accuracy was assessed at @82% accuracy and data resolution is 10m. Each image has a ‘label’ band with a discrete classification of LULC, but also 9 probability bands with class-specific probability scores generated by the deep learning model on the basis of the pixel’s spatial context. To generate an annual LULC composite comparable with WC and Esri, we calculated the mode of the predicted LULC class in the ‘label’ band of all DW images for 2020.Michael Mitchell with Ducks Unlimited Southern Regional Office led the development of this effort, in coordination and collaboration with Lower Mississippi Valley Joint Venture staff.

This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline.

1) Maize cover and yield seasonal composites

The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at:

• https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite
• https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite

Land cover and maize classification

The land cover classification file is found at data/composites/lulc_classifier_Rwanda_2019to2023.nc.

The land cover classification images contain 3 bands/variables: maizeProb, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; majorityClass, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and optimalClass, the categorical land cover class adjusted to agree with national statistics for expected maize area.

The land cover classes map to the raster values as follows:

{
  1: 'maize',
  2: 'nonmaize_annual',
  3: 'nonmaize_perennial',
  4: 'scrub_shrub_land',
  5: 'forest',
  6: 'flooded_vegetation',
  7: 'water',
  8: 'structure',
  9: 'bare'
}

The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.

Maize yield

The maize yield file is found at data/composites/maize_yield_Rwanda_2019to2023.nc.

Each of the images in the yield composites has 3 bands/variables also: maizeYield, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; maizeYield_majorityClass, predicted maize yield masked to the majority class land classification; and maizeYieldAdj_optimalClass, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics.

The dataset includes the same seasons as the classification product; see above for a description.

2) End-to-end machine learning pipeline

All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the scripts/environment.yml file. The user must have Google Cloud Storage (GCS) and Google Earth Engine (GEE) accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season).

The scripts needed to perform the pipeline are located in the scripts folder.

The files contained in the scripts/helpers directory will be called by various subsequent scripts and do not to be run interactively by the user.

Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed.

Google Cloud Storage and Earth Engine set-up

Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the Objects section of scripts/helpers/maize_pipeline_0_workspace.py.

Pipeline set-up

In scripts/pipeline_setup, you will find the following scripts to perform data preparation of inputs into model building and prediction.

• maize_pipeline_1_clean_training_data.py - Cleans and merges all available crop label and yield data for model training and validation
• maize_pipeline_2_dwnld_data_training.py - Downloads satellite-derived and auxiliary features at training data points for model building
• maize_pipeline_3_dwnld_data_inference.py - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction

Land cover and maize classification

In scripts/maize_classification, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.

• maize_classifier_1_feature_selection.py - Selects features subset for land cover classification with mutual information score or variable importance
• maize_classifier_2_build_model.py - Builds gradient boosted tree model for land cover classification from training data
• maize_classifier_3_prediction.py - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district
• maize_classifier_4_postprocess.py - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics

Maize yield

In scripts/maize_yield, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation.

• maize_yield_1_build_model.py - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data
• maize_yield_2_prediction.py - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district
• maize_yield_3_postprocess.py - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics

If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run maize_pipeline_3_dwnld_data_inference.py to download the seasonal feature data across Rwanda and maize_classifier_3_prediction.pyand maize_classifier_4_postprocess.py for classification predictions or maize_yield_2_prediction.py and maize_yield_3_postprocess.py for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at data/models).

3) Input data into machine learning pipeline

A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory data/baselayers. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.

Administrative and geological boundaries

• World Countries - Downloaded from The World Bank Official Boundaries and included here at data/baselayers/World_Countries.
• Rwanda district boundaries - Downloaded from The World Bank Rwanda Admin Boundaries And Villages and included here at data/baselayers/WB_NISR_2018. This should be loaded into a FeatureCollection GEE asset named districts_fc for use in the pipeline.
• Rwanda agro-ecological zones - Downloaded from Nzeyimana, Hartemink & Geissen (2016) and included here at data/baselayers/MINAGRI_AEZ_1980. This should be loaded into a FeatureCollection GEE asset named aez_rwanda for use in the pipeline.

Global land cover classification product

• Microsoft/Impact Observatory LULC - Although the 10m Annual Land Use Land Cover (9-class) V1 product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at data/baselayers/impactobs_lulc_rwa_2021.tif. This should be loaded into an ImageCollection GEE asset named impact_obs_lulc for use in the pipeline.

(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)

Land cover labels and maize yield crop cuttings

• One Acre Fund - Contact authors to request access as this dataset is not hosted publicly.
• RTI International - The original source of

LPJ-PROSAIL simulated data products are produced through the coupling of the Lund-Potsdam-Jena dynamic global vegetation model (LPJ) and PROSAIL, a radiative transfer model. The simulated imaging spectroscopy data were produced to aid in the development of workflows, algorithm testing, and other activities during the lead up to future global spaceborne imaging spectroscopy missions such as NASA’s Surface Biology and Geology (SBG). The LPJ-PROSAIL Level 2 Global Simulated Dynamic Surface Reflectance (LPJ_L2_SSREF) Version 1 data product provides simulated dynamic surface reflectance data in five Network Common Data Format 4 (netCDF4) files, each containing a different reflectance stream at a spatial resolution of 0.5 degrees (~50 kilometers): bidirectional (BDR), bi-hemispherical (BHR), hemispherical-directional (HDR), directional-hemispherical (DHR), and directional (DR). Each reflectance file within a granule contains simulated surface reflectance measurements of 211 bands with 10 nanometer (nm) spectral resolution across a spectral range of 400 to 2500 nm for the entire globe. The data are presented with four dimensions: latitude, longitude, bands (wavelength), and time. Each netCDF4 file holds a one-dimensional list for each of the four dimensions containing the values that are associated with those dimensions. LPJ_L2_SSREF Version 1 is composed of one granule containing data for the year 2020 with monthly time increments. Known Issues* Data Usage Warning: These data are meant to be used in development of workflows, algorithms, and other instances where large imaging spectroscopy datasets are needed for testing. Due to the simulated nature of these data, these data are not intended for scientific use and should not be used for any real-world scientific analyses or conclusions.

World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.The tiled services (Terrain 3D,TopoBathy 3D,World Hillshade,World Hillshade (Dark)) also include an additional data source from Maxar's Precision3D covering parts of the globe.Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Maxar Precision3D and Airbus WorldDEM4Ortho.To view the all the sources in a table format, check out World Elevation Data Sources Table.Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

This dynamic World Elevation Terrain service provides numeric values representing ground surface heights, based on a digital terrain model (DTM). The ground heights are based on multiple sources. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis.Note: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, you can filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS desktop, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Data Sources and Coverage: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: The accuracy of these services will vary as a function of location and data source. Please refer to the metadata available in the services, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.