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The Germany Mosaic is an analysis-ready spatiotemporal dataset with a consistent data-cube structure, covering the entire territory of Germany from 1984 to 2025. It provides consistently processed Landsat-based raster and vector products on a common spatial grid, enabling long-term analysis of environmental and landscape dynamics as well as machine-learning and AI-based applications. For each year, two seasonal six-band multispectral composites are provided, representing the periods May–July and August–October. All usable Landsat observations acquired within the respective seasonal period are included in the compositing process to maximize spatial coverage and minimize cloud contamination, resulting in virtually cloud-free seasonal image composites. The spatial organization is consistent across the complete time series: all years use the same pixel size, spatial grid, tile geometry, band structure, and variable schema for the vector products. Germany is represented by 54 overlapping tiles of 120 × 120 km with a 20 km overlap. This standardized structure enables direct spatial and temporal comparison across more than four decades without additional spatial harmonization or restructuring. In addition to the multispectral raster data, the dataset contains image-segmentation polygons (“zones”) representing approximately homogeneous image regions, together with associated spectral, textural, statistical, geometric, and contextual attributes. The vector datasets include 32 attributes covering seasonal spectral information, vegetation indices, temperature, image quality, texture and variability measures, Rao’s diversity, elevation-related information, and indicators concerning the form, size, connectivity, distribution, and spatial relationships of the zones. Additional raster products provide elevation data and statistics for seasonal vegetation change. The consistent data-cube structure makes the Germany Mosaic particularly suitable for large-scale time-series analysis, change detection, environmental monitoring, machine learning, and other AI-based workflows. It can also provide a data foundation for environmental modelling and digital-twin applications. This dataset constitutes the second version and continuation of the previously published Germany Mosaic 1984–2023 dataset (DOI: 10.1594/PANGAEA.967266) and extends the temporal coverage through 2025 while introducing a revised and standardized spatial organization based on 54 consistently defined tiles, a 20 km overlap between the tiles, and new raster and vector features. Content - Two seasonal six-band multispectral image composites per year: May–July and August–October - Seasonal vegetation-change statistics as a raster image (AIR Files) - One separate set of elevation data with identical tiling - Image-segmentation polygons (“zones”) with 32 spectral, textural, statistical, geometric, and contextual attributes - Raster and vector products for each year from 1984 to 2025 - Data organized in 54 overlapping tiles covering Germany - Formats: GeoTIFF for all raster products; Esri Shapefile for all vector products - Optical Sensors: Thematic Mapper (TM) on Landsat 4, Enhanced Thematic Mapper (ETM) on Landsat 5, Operational Land Imager (OLI) on Landsat 8 and 9 - Thermal Sensors: Thermal Infrared Sensor (TIRS) on Landsat 4, 5, 8, and 9 - Elevation Sensors: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra Satellite - All usable observations within the two seasonal periods are included in the compositing process Features - Time: Two seasonal composites per year, May–July and August–October - Data cube: Same pixel size, spatial grid, tile geometry, band structure, and variable schema for all years - Raster: Six-band spectral image composites for each season (summer and autumn) and one three-band image composite for seasonal vegetation-change statistics - Elevation: Raster data once for each tile - Vector: Image-segmentation polygons (“zones”) with spectral and geometric attributes - Attributes: 32 attributes covering seasonal spectral composites (six bands), seasonal vegetation indices (NDVI, NIRv), seasonal vegetation-change statistics, seasonal temperature, elevation, seasonal image-quality index, textures, Rao’s diversity, size, geometry, connections, and spatial relationships of the zones - Tiles: 54 overlapping 120 × 120 km tiles with 20 km overlap - CRS: WGS 84 / UTM zone 32N (EPSG:32632) - Quality: Virtually cloud-free seasonal composites generated from all usable observations See D-Mosaic_Manual for details and background. Applications - Time-series and change analysis, environmental monitoring, machine learning, AI-based workflows, environmental modelling, and digital twins Figures Figure 1 – RGB visualization of three vector attributes based on polygons: - Red: structural diversity (texture) - Green: highest vegetation activity (plant metabolic activity) during the year - Blue: seasonal variation between summer and autumn - Black: zone boundaries Figure 2 – Distribution of the 54 overlapping 120 × 120 km tiles covering Germany.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
Web Map Service (WMS) für die Bereitstellung von Satellitendaten des Copernicus-Programms. Der LGV bereitet die Daten zur einfacheren Nutzung quartalsweise auf. Die dargestellten Geodaten beinhalten zusätzlich eine zeitliche Dimension (WMS-Time). Zur genaueren Beschreibung der Daten und Datenverantwortung nutzen Sie bitte den Verweis zur Datensatzbeschreibung.
**Hinweis** Dieser Datensatz wird bis zur Fertigstellung der neuen Version (2026) nicht mehr aktualisiert. Die Europäische Weltraumorganisation (ESA) stellt innerhalb des Copernicus-Programms kostenfrei Satellitendaten der Missionen Sentinel (dt. Wächter) zur Verfügung. Der LGV bereitet die Daten zur einfacheren Nutzung quartalsweise auf. Originär können die Satellitendaten über die nationale Plattform CODE-DE (Copernicus Data and Exploitation Platform – Deutschland) bezogen werden. Datengrundlage: - Sentinel-2 L2A: Multispektrale, atmosphärisch korrigierte Daten - Georeferenziertes Mosaik - Kachel-Anzahl: 25 - Kachel-Größe: 8 km x 8 km - Kachel-Auswahl: Aktualität und Grad der Wolkenbedeckung - Farbdarstellung: RGB, CIR, NDVI - Bodenauflösung: 10m - Farbtiefe: 8 bit RGB (Red Green Blue): Die Bandkombination aus Rot (B4), Grün (B3) und Blau (B2) bildet die menschliche Farbwahrnehmung nach. Gesunde Vegetation wird grün, urbane Flächen werden weiß / grau und Wasserflächen werden, abhängig der Trübung, blau dargestellt. CIR (Color Infrared): Die Bandkombination aus nahem Infrarot (B8), Rot (B4) und Grün (B3) hebt die Vegetation hervor. Diese reflektiert aufgrund des Chlorophyllgehalts der Pflanzen im nahen Infrarotbereich besonders stark und wird rötlich dargestellt. Urbane Flächen erscheinen cyan-blau / grau und Wasserflächen dunkelblau. NDVI (Normalized Difference Vegetation Index): Der NDVI ist ein häufig angewendeter Index, welcher zur Einschätzung der Vegetation herangezogen wird. Er berechnet sich aus den Bändern Nahes Infrarot (B8) und Rot (B4): NDVI = (NIR-Rot)/(NIR+Rot) <b>Hinweis</b> "Keine Daten verfügbar": Wenn innerhalb eines Quartals ausschließlich Daten mit hoher Wolkenbedeckung vorliegen, wird kein Mosaik erzeugt. Dies ist insbesondere in Wintermonaten möglich. [© Contains modified Copernicus Sentinel data [2018-2024], processed on CODE-DE]
Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2016.
Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2019.
Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2026.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
Every day, three successive NOAA-AVHRR scenes are used to derive a synthesis product in stereographic projection known as the "Normalized Difference Vegetation Index" for Europe and North Africa. It is calculated by dividing the difference in technical albedos between measurements in the near infrared and visible red part of the spectrum by the sum of both measurements. This value provides important information about the "greenness" and density of vegetation. Weekly and monthly thematic synthesis products are also derived from this daily operational product, at each step becoming successively free of clouds. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/
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