Grassland age estimates for Germany (1990–2023) derived from Landsat and Sentinel-2 time series
Authors/Creators
Description
This dataset provides Germany-wide estimates of the grassland age from 1990 to 2023, as described in Blickensdörfer et al. (2026). The grassland age, defined as the number of years since the last land use change, is a key indicator for carbon sequestration potential, biodiversity, and ecosystem resilience. The results of this study are intended to support ecosystem service modelling, biodiversity assessments, and agricultural land use research.
The grassland history was estimated from a multidecadal satellite image time series by combining Landsat (from 1986) and Sentinel-2 data. Data preprocessing was performed using the FORCE environment (Frantz, 2019). First, all available clear-sky observations over grassland areas were classified as bare soil or non-bare soil. Next, per-pixel bare soil occurrence was normalized to a seasonal frequency metric, and a calibrated rule set was applied to derive grassland age based on temporal patterns in this frequency. Agricultural land use periods and years of land use change were identified via elevated bare soil occurrence.
We provide this dataset "as is" without any warranty regarding the quality or completeness of the resulting maps, and exclude all liability. Please refer to Blickensdörfer et al. (2026) for the accuracy assessment and the potential limitations of the methods, or contact the authors directly.
The maps are available as cloud-optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python, or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so, the entire map area or only the regions of interest can be accessed.
Technical info
The workflow is applied within a grassland mask derived from remote sensing-based agricultural land use maps (Blickensdörfer et al., 2022; Schwieder et al., 2024a, Schwieder et al., 2024b). The mask includes all permanent or temporary grassland areas between 2017 and 2023 to minimize the omission of potentially relevant grassland fields. Note that, in turn, the analysis mask includes some temporary grassland, which was part of agricultural crop rotation between 2017 and 2023. Those parcels get attributed very recent establishment years by our method and remain in the dataset.
Raster values indicate the year of initial grassland establishment (1990-2023).
Grassland established before 1990 has a raster value of 0.
The no data value (-32768) represents non-grassland areas.
Files
grassland_age_1990_2023.tif
Files
(71.6 MB)
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Additional details
Related works
- Is supplement to
- Journal article: 10.1016/j.rse.2026.115465 (DOI)
References
- Blickensdörfer, L., Broeg, T., Lobert, F., Schwieder, M., Hostert, P., Erasmi, S., 2026. Leveraging multidecadal satellite time series to estimate grassland age on national scale. Remote Sens. Environ., 342, 115465. https://doi.org/10.1016/j.rse.2026.115465.
- Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., Hostert, P., 2022. Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sens. Environ. 269, 112831. https://doi.org/10.1016/j.rse.2021.112831.
- Frantz, D., 2019. FORCE—Landsat + Sentinel-2 analysis ready data and beyond. Remote Sens. 11, 1124. https://doi.org/10.3390/rs11091124.
- Schwieder, M., Tetteh, G.O., Blickensdörfer, L., Gocht, A., Erasmi, S., 2024a. Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) [dataset]. Zenodo v202. https://doi.org/10.5281/zenodo.10640528.
- Schwieder, M., Tetteh, G.O., Blickensdörfer, L., Gocht, A., Erasmi, S., 2024b. Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2022) [dataset]. Zenodo v202. https://doi.org/10.5281/zenodo.10645427.