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.
Objective: Fair Trade is an activity area where CSOs play a key role. Fair Trade is also an activity area promoting the best social, economic and environmental practices of sustainable development. Unfortunately CSOs do not have the means to develop concerted strategies in Research and Technology Development. Their short term concerns often prevent them to have the necessary hindsight. The main objective of the Geo Fair Trade project is to bring together Fair Trade CSOs and RTD performers. Discussions with the principle Fair Trade stakeholders and actors have shown that their basic needs to win new markets and new consummers are Transparency and Traceability. Taking advantage of the results obtained in two FP5 and FP6 research projects, the CCI Gers and its partners CRA-W and CIRAD, together with six Fair Trade CSOs, have defined the main objectives of the project whose: - To select sustainable development indicators with a spatial component and related to the three dimensions of Fair Trade (social, economic and environmental) - To adapt the Geo-traceability Integrated System, set-up in the previous research projects, enabling finding and browsing of all relevant information corresponding to the needs of Fair Trade actors - To validate this approach with five case studies chosen by the CSOs - To develop training and educative tools to disseminate this approach. The Geo Fair Trade project is based on a permanent communication between the RTD performers and the CSOs, who will participate in all scheduled progress meetings, to validate the results or to reorientate the Research and Technology Development activities. The final expected result is a reference framework built on the sustainable development geo-indicators that can be used in all the traceability systems already implemented in Fair Trade. This reference framework will improve the certification of the best practices implemented by the Fair Trade actors.
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