Mowing detection using Sentinel-1 and Sentinel-2 time series for large scale grassland monitoring

Managed grasslands cover about one third of the European utilized agricultural area. Appropriate grassland management is key for balancing trade-offs between provisioning and regulating ecosystem services. The timing and frequency of mowing events are major factors of grassland management. Recent studies have shown the feasibility of detecting mowing events using remote sensing time series from optical and radar satellites. In this study, we present a new method combining the regular observations of Sentinel-1 (S1) and the better accuracy of Sentinel-2 (S2) grassland mowing detection algorithms. This multi-source approach for grassland monitoring was assessed over large areas and in various contexts. The method was first validated in six European countries, based on Planet image interpretation. Its performances and sensitivity were then thoroughly assessed in an independent study area using a more precise and complete reference dataset based on an intensive field campaign. Results showed the robustness of the method across all study areas and different types of grasslands. The method reached a F1-score of 79% for detecting mowing events on hay meadows. Furthermore, the detection of mowing events along the growing season allows to classify mowing practices with an overall accuracy of 69%. This is promising for differentiating grasslands in terms of management intensity. The method could therefore be used for largescale grassland monitoring to support agri-environmental schemes in Europe.

Automated summary

In this study, a new method combining the observations of Sentinel-1 (S1) and the accuracy of Sentinel-2 (S2) satellites was proposed for grassland monitoring. The method was validated and assessed in European countries and showed good stability and accuracy across different environments. This method has the potential to be used for supporting agri-environmental schemes in Europe.