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The Shuttle Radar Topography Mission (SRTM) was an international research effort that obtained digital elevation models on a near-global scale.

Burnt Area is a monitoring service showing the full extent of forest fires on a daily basis and over time.

The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth’s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making.

The EOC provides comprehensive information on the structure and composition of forest areas and vegetation.

Further datasets which do not relate to a specific data group.

Global SnowPack is a global service which maps snow cover extent at different temporal scales.

Global WaterPack (GWP) contains information about open surface water coverage at different temporal scales.

IceLines is an automated calving front monitoring service providing monthly ice shelf front time series of major Antarctic ice shelves.

The EOC provides comprehensive information on physical material at the Earth's surface, including vegetation, water, bare ground, and human-made structure for Germany.

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the American satellites Terra and Aqua.

RapidEye Group

Sentinel-5 is an Earth observation mission from the EU's Copernicus Programme. This group contains several datasets providing information about global measurements of atmospheric properties.

Sentinel-2 is an Earth observation mission from the EU's Copernicus Programme.

The objective of the TanDEM-X mission (TerraSAR-X add-on for Digital Elevation Measurement) is to produce a highly accurate, three-dimensional image of our Earth with uniform quality and unprecedented accuracy.

The World Settlement Footprint Suite is a collection of novel global datasets designed to provide high-resolution and reliable information on the extent of settlement areas.

The Soil Suite is a collection of Earth observation products that describe the properties of the top pedosphere layer.

The EnMAP HSI L1C dataset collection comprises a standardized, consistent, systematically processed, and cloud-native level-1C dataset series for the entire mission. It is especially useful for big data or time series analyses. The dataset is providing all 224 bands in cloud-optimized GeoTIFF format and radiometrically and geometrically corrected for direct access and download. The metadata follows the CEOS Analysis Ready Data (CEOS-ARD) framework. The database is constantly updated with newly acquired data. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth’s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making.

The EnMAP HSI L2A dataset collection comprises a standardized, consistent, systematically processed, and cloud-native level-2A dataset series for the entire mission. It is especially useful for big data or time series analyses. The dataset is processed with the atmospheric correction over land processor and is provided in cloud-optimized GeoTIFF format for direct access and download. The metadata follows the CEOS Analysis Ready Data (CEOS-ARD) framework. The database is constantly updated with newly acquired data. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth’s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making.

HyBiomass is a nearly globally distributed benchmark dataset pairing EnMAP HSI with GEDI-derived Aboveground Biomass (AGB) labels for geospatial foundation model evaluation. Comprehensive evaluation of geospatial foundation models (Geo-FMs) requires benchmarking across diverse tasks, sensors, and geographic regions. However, most existing benchmark datasets are limited to segmentation or classification tasks, and focus on specific geographic areas. To address this gap, we introduce a globally distributed dataset for forest aboveground biomass (AGB) estimation, a pixelwise regression task. This benchmark dataset combines co-located hyperspectral imagery (HSI) from the Environmental Mapping and Analysis Program (EnMAP) satellite and predictions of AGB density estimates derived from the global ecosystem dynamics investigation (GEDI) lidars, covering seven continental regions. Our experimental results on this dataset demonstrate that the evaluated Geo-FMs can match or, in some cases, surpass the performance of a baseline U-Net, especially when fine-tuning the encoder. By releasing this globally distributed hyperspectral benchmark dataset, we aim to facilitate the development and evaluation of Geo-FMs for HSI applications. Leveraging this dataset additionally enables research into geographic bias and the generalization capacity of Geo-FMs. Published in IEEE Geoscience and Remote Sensing Letters <a href="https://doi.org/10.1109/LGRS.2025.3610178">https://doi.org/10.1109/LGRS.2025.3610178</a>.