Feature image: Crop type occurrence in the demo field in Czechia captured by the PEOPLE4CAP project.
The Common Agricultural Policy (CAP) has evolved to prioritise performance-driven environmental and climate goals. Eco-schemes are at the heart of this transition, incentivising sustainable farming practices that go beyond compliance with mandatory requirements. These include crop rotation, soil conservation, grassland management, and more.
Monitoring compliance with these eco-schemes poses challenges for authorities. Traditional on-site inspections are labour-intensive, costly, and limited in scalability. As the transition to area-based monitoring systems advances, reliable and frequent data on agricultural practices is essential to ensure accuracy and accountability in subsidy allocation.
High-resolution satellite data from Sentinel-1 (radar) and Sentinel-2 (optical) satellites provide insights into crop health, vegetation patterns, and soil conditions. By analysing temporal vegetation indices such as NDVI, EO data enables:
- Tracking crop rotation, fallow lands, and soil cover.
- Monitoring grassland management activities such as mowing and grazing.
- Identifying tillage practices and assessing compliance with eco-scheme requirements.
Artificial Intelligence (AI) and machine learning models enhance EO data by detecting patterns, validating ground truth, and automating monitoring processes. This reduces the need for manual inspections and ensures a transparent decision-making framework for subsidy allocation.
