Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

Abstract

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain)

“…For instance, Schmedtmann and Campagnolo [24] proposed a reliable crop identification (12 classes, OA = 68%) over Portuguese parcels covering 1057 km 2 , Sitokonstantinou et al [25] developed a parcel-based crop identification (nine classes, OA = 91.3% and k = 0.87) scheme for CAP and greening obligations over a Spanish area of 215 km 2 . Kanjir et al [48] developed a change detection method to support land use control within CAP activities over three areas of 7 km 2 in Slovenia, and Blaes et al [23] developed a framework for area-based subsidies control in Belgium. The present study reported overall accuracy reaching ≈ 94%, which is similar to the aforementioned studies.…”

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

“…Sentinel-2 holds great potential for assisting the crop type inspections and there are numerous methods available for various different agricultural applications [10], but still no common workflow, method, or tool that can be easily adopted by the PAs. The DiAS DSS prototype complements current efforts in this direction from the Sen2-Agri project [16], the RECAP project [17,18], and the Sen4CAP [19], by providing a simple workflow through an easily operated tool, with instructions that were developed in a participatory approach.…”

“…The RECAP project aims to provide on one hand, information to authorities and agricultural consultants, and on the other hand, assists farmers' decisions according to CAP regulations and prerequisites [17,18]. The Sen4CAP project attempts to incorporate Sentinel data in order to support the aims of modernizing the new CAP post-2020 by focusing in developing advanced algorithms and effective workflows [19]. However, the operational capacity of the proposed workflows of these projects have not been fully assessed and in recognition of the complexity of the problem, the discussions of PAs have not yet reached to a conclusion.…”

Towards the sustainable intensification of agriculture—a systems approach to policy formulation

“…A second, less prescriptive, approach is to support eco-agriculture [72] or ecological intensification [73] , which creates local diversity through the redesign of the farming system [54] . Finally the policy must be monitored, addressing the range of core indicators, using remote sensing [74] and/or surveys [43] .…”