Grassland mowing event detection using combined optical, SAR, and weather time series

The European Union’s Common Agricultural Policy (CAP) and the Habitats Directive aim to improve biodiversity in agricultural landscapes. Both policies require enormous monitoring, which can be facilitated by remote sensing. Use intensity, measured by mowing frequency is an important indicator of biodiversity in permanent grasslands. The frequency and timing of mowing can be determined using satellite remote sensing because photosynthetically active biomass changes rapidly in response to mowing. However, the rapid regrowth of grasses requires very dense satellite time series for reliable detection. Radar time series can complement optical time series and fill in cloud-related gaps to overcome this problem. Additional weather data can support the detection of grassland mowing events, as mowing events are associated with specific meteorological conditions. However, previous studies have not fully exploited both potentials or different machine learning approaches for mowing event detection.