LPJ-PROSAIL L2 Global Simulated Dynamic Surface Reflectance V001

The dataset is based on OpenStreetMap (OSM) data downloaded from the official OpenStreetMap distribution platform. The data have subsequently been processed and published as a customised web service. OpenStreetMap is an open, crowdsourced geospatial dataset that is continuously maintained and updated by a global community of contributors. The dataset contains vector-based geographic features, including transport networks, buildings, land use, hydrography, natural features, and points of interest. The data are used as a background map and reference dataset for map visualisation and spatial analysis at local, regional, and national scales. Data content and quality vary geographically and depend on contributor activity.

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

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

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 O3 products, which contains high-resolution imagery of total column ozone concentrations.

Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. All years are available under a Creative Commons BY-4.0.