Reliable crop monitoring is paramount to achieve the objectives of the Common Agricultural Policy (CAP) and Food and Agriculture Organization. Synthetic Aperture Radar (SAR) provides high-resolution imaging and all-weather data acquisition capabilities for crop monitoring. This study investigates the sensitivity of parcel-level Sentinel-1 interferometric coherence to farming activities (e.g. planting, emergence, harvest and tillage) and weather events. A methodology to detect activities was developed and validated using ground-truth data from four crop types, collected over four years. The proposed approach was able to detect over 60% of all nine different farming activities. The results show that interferometric coherence is a reliable indicator for farming activities that can be considered as events resulting in a clear structural change (e.g. tillage 100%), but less reliable for gradual changes (e.g. Emergence 40%).

Synthetic Aperture Radar (SAR) data handling, processing, and interpretation are barriers preventing a rapid uptake of SAR data by application specialists and non-expert domain users in the field of agricultural monitoring. To improve the accessibility of Sentinel-1 data, we have generated a reduced-volume, multi-year Sentinel-1 SAR database. It includes mean and standard deviation of VV, VH and VH/VV backscatter, pixel counts, geometry, crop type, local incidence angle and azimuth angle at parcel-level. The database uses around 3100 Sentinel-1 images (5 TB) to produce a 12 GB time series database for approximately 770,000 crop parcels over the Netherlands for a period of three years. The database can be queried by Sentinel-1 system parameters (e.g. relative orbit) or user application-specific parameters (e.g. crop type, spatial extent, time period) for parcel level assessment. The database can be used to accelerate the development of new tools, applications and methodologies for agricultural and water related applications, such as parcel-level crop bio-geophysical parameter estimation, inter-annual variability analysis, drought monitoring, grassland monitoring and agricultural management decision-support.