SWIM Water Extent is a global surface water product at 10 m pixel spacing based on Sentinel-1/2 data. The collection contains binary layers indicating open surface water for each Sentinel-1/2 scene. Clouds and cloud shadows are removed using ukis-csmask (see: https://github.com/dlr-eoc/ukis-csmask ) and are represented as NoData. The water extent extraction is based on convolutional neural networks (CNN). For further information, please see the following publications: https://doi.org/10.1016/j.rse.2019.05.022 and https://doi.org/10.3390/rs11192330
IceLines (Ice Shelf and Glacier Front Time Series) is an automated calving front monitoring service providing monthly ice shelf front time series of major Antarctic ice shelves. The provided time series allows to discover the dynamics of ice shelf front changes and calving events. The front positions are automatically derived from Sentinel-1 data based on a deep neuronal network called HED-U-Net. The time series covers the timespan 2014 to today (partly limited due to Sentinel-1 data availability). Incorrectly extracted fronts are truncated which might lead to gaps in the time series especially between December to March due to strong surface melt. Annual averages are calculated based on the extracted monthly fronts (excluding the summer months) and provide more robust results due to temporal aggregation
This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2018. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022. This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2017. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022.
Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2019.
The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2022 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data, radar data from Sentinel-1, and a digital elevation model. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 80,000 training and test samples were compiled from publicly accessible sources, including urban tree inventories, Google Earth Pro, Google Street View, and field observations. The final classification was generated using an XGBoost machine learning algorithm. The Tree Species Germany product achieves an overall F1-score of 0.89. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.76 to 0.98, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, and fir range from 0.88 to 0.96. The product provides a consistent, high-resolution, and up-to-date representation of tree species distribution across Germany. Its transferable, cost-efficient, and repeatable methodology enables reliable large-scale forest monitoring and offers a valuable basis for assessing spatial patterns and temporal changes in forest composition in the context of ongoing climatic and environmental dynamics.
The product shows forest structure information on canopy height, total canopy cover and Above-ground biomass density (AGBD) in Germany as annual products in 10 m spatial resolution. The products were generated using a machine learning modelling approach that combines complementary spaceborne remote sensing sensors, namely GEDI (Global Ecosystem Dynamics Investigation; NASA; full-waveform LiDAR), Sentinel-1 (Synthetic-Aperture-Radar; ESA, C-band) and Sentinel-2 (Multispectral Instrument; ESA; VIS-NIR-SWIR). Sample estimates on forest structure from GEDI were modelled in 10 m spatial resolution as annual products based on spatio-temporal composites from Sentinel-1 and -2. The derived products are the first consistent data sets on canopy height, total canopy cover and AGBD for Germany which enable a quantitative assessment of recent forest structure dynamics, e.g. in the context of repeated drought events since 2018. The full description of the method and results can be found in the publication of Kacic et al. (2023).
Das Dynamische Mosaik setzt sich aus aktuellen wolkenfreien Aufnahmen von Sentinel-2 Orthobildern zusammen. Die beiden baugleichen Sentinel-2 Satelliten des europäischen Erdbeobachtungsprogramms Copernicus liefern seit 2015 bzw. 2017 kontinuierlich Aufnahmen der Erdoberfläche. Der multispektrale optische Sensor verfügt über 13 Spektralkanäle im sichtbaren und infraroten Bereich. Dabei variiert die räumliche Auflösung von 10 m (Kanäle B02, B03, B04, B08) über 20 m (Kanäle B05, B06, B07, B08A, B11, B12) bis hin zu 60 m (Kanäle B01, B09, B10). Die Sentinel-2 Daten werden originär in den Prozessierungsleveln Level-1C (Top-Of-Atmosphere) und Level-2A (Bottom-Of-Atmosphere) angeboten und in Kachelgrößen von 100 x 100 km2 in UTM/WGS84 Projektion bereitgestellt. Für NRW liegen durch die hohe Wiederkehrrate der Satelliten alle 2-3 Tage flächendeckend aktuelle Aufnahmen vor. Da es sich bei dem Multispektralinstrument um ein passives System handelt, ist die Verwendbarkeit der Aufnahmen allerdings wetterabhängig. Die verfügbaren Orthobilder weisen unterschiedliche Wolkenbedeckungsgrade auf. Zur Ableitung des Dynamischen Mosaiks werden die aktuellen Sentinel-2 Bilder auf Wolkenbedeckung überprüft, so dass die wolkenfreien Bereiche selektiert werden können. Bereiche älterer Aufnahmen werden kontinuierlich durch aktuelle wolkenfreie Bilder ersetzt. Dabei werden die 4 Spektralbänder mit einer räumlichen Auflösung von 10 m (Rot, Grün, Blau, Nahes Infrarot) der Level-2A Daten berücksichtigt. Der Datensatz wird bei Vorliegen eines wolkenfreien Bereichs ab einer Größe von 100 zusammenhängenden 10 m x 10 m Pixeln fortgeschrieben, so dass stets die aktuellen wolkenfreien Aufnahmen im Mosaik enthalten sind. Das Dynamische Mosaik wird als Darstellungsdienst in den Ausprägungen RGB (Komposit aus den Spektralbändern B04-B03-B02) und CIR (Komposit aus den Spektralbändern B08-B04-B03) bereitgestellt. Darüber hinaus wird das Aufnahmedatum der jeweiligen Sentinel-2 Szene für jeden wolkenfreien Bereich zur Verfügung gestellt. Das Aufnahmedatum wird über die Sachdatenabfrage des Metadatenlayers angezeigt.
The SoilSuite contains a collection of different image data products that provide information about the spectral and statistical properties of European soils and other bare surfaces such as rocks. It is created using DLR's Soil Composite Mapping Processor (ScMAP), which utilises the Sentinel-2 data archive. SCMaP is a specialised processing chain for detecting and analysing bare soils/surfaces on a large (continental) scale. Bare surface and soil pixels are selected using a combined NDVI and NBR index (PVIR2) that optimises the exclusion of photosynthetically active and non-active vegetation. The index is calculated and applied for each individual pixel. All SoilSuite products are calculated based on the available Sentinel-2 scenes recorded between January 2018 and December 2022 in Europe. The data package excludes all scenes with a cloud cover of > 80 % and a sun elevation of < 20°. The spectral composite products are calculated from the mean value after extensive removal of clouds, haze and snow effects at both scene and pixel level. The spectral data products are available at a pixel size of 20 m and contain 10 Sentinel-2 bands (B02, B03, B04, B05, B06, B07, B08, B08a, B11, B12). The SoilSuite comprises: (a) “Bare Surface Reflectance Composite – Mean” that provides the spectral properties of soils that vary due to different soil organic carbon (SOC) content, soil moisture and soil minerology. This product is often used for spectral and digital soil mapping approaches, (b) “Bare Surface Reflectance Composite - Standard deviation” informing about the spectral dynamic of bare surfaces and soils, (c) “Bare Surface Reflectance Composite – 95% Confidence” contains information about the reliability of the spectral information due to the number of valid observations per pixel, (d) “Bare Surface Statistics Product” provides the number of bare soil occurrences over the total number of valid observations (Band 1), the number of bare soil occurrences (Band 2) and the total number of valid observations (Band 3), (e) “Mask” is a product that aggregates simple landcover classes that occur during the time period between 2018 - 2022 (Sentinel-2). The three-class Mask contains bare surface occurrences (1), permanent vegetation (2) and other surfaces such as water bodies, urban areas, roads (3). Additionally, the SoilSuite provides (f) “Reflectance Composite – Mean” that represents the mean reflectance of all valid Sentinel-2 observations between 2018 – 2022 including vegetation, bare and other surfaces, and (g) “Reflectance Composite – Standard deviation”, which contains the standard deviation per band for all valid Sentinel-2 observations between 2018 – 2022.
The World Settlement Footprint (WSF) 3D provides detailed quantification of the average height, total volume, total area and the fraction of buildings at 90 m resolution at a global scale. It is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery in combination with digital elevation data and radar imagery collected by the TanDEM-X mission. The framework includes three basic workflows: i) the estimation of the mean building height based on an analysis of height differences along potential building edges, ii) the determination of building fraction and total building area within each 90 m cell, and iii) the combination of the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. In addition, global height information on skyscrapers and high-rise buildings provided by the Emporis database is integrated into the processing framework, to improve the WSF 3D Building Height and subsequently the Building Volume Layer. A comprehensive validation campaign has been performed to assess the accuracy of the dataset quantitatively by using VHR 3D building models from 19 globally distributed regions (~86,000 km2) as reference data. The WSF 3D standard layers are provided in the format of Lempel-Ziv-Welch (LZW)-compressed GeoTiff files, with each file - or image tile - covering an area of 1 x 1 ° geographical lat/lon at a geometric resolution of 2.8 arcsec (~ 90 m at the equator). Following the system established by the TDX-DEM mission, the latitude resolution is decreased in multiple steps when moving towards the poles to compensate for the reduced circumference of the Earth.
The World Settlement Footprint (WSF) 3D provides detailed quantification of the average height, total volume, total area and the fraction of buildings at 90 m resolution at a global scale. It is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery in combination with digital elevation data and radar imagery collected by the TanDEM-X mission. The framework includes three basic workflows: i) the estimation of the mean building height based on an analysis of height differences along potential building edges, ii) the determination of building fraction and total building area within each 90 m cell, and iii) the combination of the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. In addition, global height information on skyscrapers and high-rise buildings provided by the Emporis database is integrated into the processing framework, to improve the WSF 3D Building Height and subsequently the Building Volume Layer. A comprehensive validation campaign has been performed to assess the accuracy of the dataset quantitatively by using VHR 3D building models from 19 globally distributed regions (~86,000 km2) as reference data. The WSF 3D standard layers are provided in the format of Lempel-Ziv-Welch (LZW)-compressed GeoTiff files, with each file - or image tile - covering an area of 1 x 1 ° geographical lat/lon at a geometric resolution of 2.8 arcsec (~ 90 m at the equator). Following the system established by the TDX-DEM mission, the latitude resolution is decreased in multiple steps when moving towards the poles to compensate for the reduced circumference of the Earth.
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