Spatiotemporal detailed crop cover and management factor maps as agri-environmental indicators for soil erosion in Germany

• Published in: International Soil and Water Conservation Research
• Main Authors: Steinhoff-Knopp, Bastian, Neuenfeldt, Sebastian, Erasmi, Stefan, Saggau, Philipp
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2025
• Subjects: C factor; Crop rotation; Earth observation; Remote sensing; RUSLE; Soil erosion modelling; USLE; USLE; Soil erosion modelling; RUSLE; C factor; Crop rotation; Earth observation; Remote sensing
• ISSN: 2095-6339
• DOI: 10.1016/j.iswcr.2025.06.002

The crop cover and management factor (C factor) is crucial to assess the impact of management on soil erosion by water within the (R)USLE (Revised Universal Soil Loss Equation) modelling framework. Its derivation is challenging due to the need for spatiotemporal data on crop sequences. Therefore, the aim of this study is the generation of spatiotemporal detailed C factor datasets for Germany by integrating (a) crop composition data from agricultural statistics on the municipality level for six individual years from 1999 to 2020 and (b) high-resolution (10 × 10 m) crop sequence information for 2017 to 2023 derived from earth observation data in the C factor estimation. The results reveal an overall increase of 8.7 % in the mean C factor for German municipalities from 1999 to 2020, which can be attributed to policy-driven changes in crop composition. The comparison of the two C factor datasets emphasises the importance of multi-annual information on crops in (R)USLE-based erosion modelling as (i) high-resolution C factors based on single years show a weak agreement with crop sequence-derived C factors (RMSE of 0.062) and (ii) C factors based on crop composition data from agricultural statistics are 5.7 % lower compared to high-resolution crop sequence-derived C factors. As high-resolution crop type data from earth observation is updated yearly, the C factor maps presented here can be incorporated into German monitoring systems as agri-environmental indicators. Further research is needed to obtain more detailed information on cover crops and tillage practices to improve C factor derivation. These findings and the visible heterogenious patterns in the pixel-based multi-annual C factor data highlight that spatiotemporal high-resolution input data is key in C factor estimation.