Characterizing spatio-temporal patterns of winter cropland cover in Germany based on Landsat and Sentinel-2 time series
Continuous winter cover on croplands reduces soil erosion and enhances soil organic carbon, making it essential for climate-smart agriculture and greenhouse gas mitigation. However, spatially and temporally explicit data on winter cropland cover remain limited across Europe. We present a national-sc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003759 |
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| Summary: | Continuous winter cover on croplands reduces soil erosion and enhances soil organic carbon, making it essential for climate-smart agriculture and greenhouse gas mitigation. However, spatially and temporally explicit data on winter cropland cover remain limited across Europe. We present a national-scale assessment of winter cropland cover in Germany from 2017 to 2023 using spectral unmixing-derived fractional cover time series from Sentinel-2 and Landsat imagery. Four winter cover types were distinguished: winter crops, green cover (including cover crops and spontaneous vegetation), residue cover, and no cover (bare soil). Phenological metrics were derived for green cover to assess vegetation duration, biomass accumulation, and termination type.Our results reveal an increasing share of green cover, rising from 17 % in 2017/18 to 21 % in 2022/23, and a decline in bare soil from 20 % to 12 %, indicating a shift toward more protective winter management. Green cover was most frequent in northwestern and southern Germany, while bare soil remained prevalent in central and eastern regions. Classification results showed good agreement with Integrated Administration and Control System (IACS) parcel-level records of winter cover crops (F1-Score = 0.68) and agricultural census data at the federal state level (R2 = 0.82, MAE = 5 percentage points). Remote sensing estimates consistently exceeded reported cover crop shares, reflecting the presence of non-subsidized or spontaneous vegetation. Our approach provides valuable input for enhanced erosion and carbon modeling and establishes a basis for monitoring and future evaluation of Common Agricultural Policy (CAP) instruments, including Good Agricultural and Environmental Conditions (GAEC) standards. |
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| ISSN: | 1569-8432 |