Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring

During the last decades, massive amounts of satellite images are becoming available that can be enriched with semantic annotations for the creation of value-added earth observation products. One challenge is to extract knowledge from the raw satellite data in an automated way and to effectively manage the extracted information in a semantic way, to allow fast and accurate decisions of spatiotemporal nature in a real operational scenario. In this work, we present a framework that combines supervised learning for crop type classification on satellite imagery time-series with semantic web and linked data technologies to assist in the implementation of rule sets by the European common agricultural policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. We analyze image time-series that cover the entire cultivation period and link each parcel with a specific crop. On top of that, we introduce a semantic layer to facilitate a knowledge-driven management of the available information, capitalizing on ontologies for knowledge representation and semantic rules, to identify possible farmers noncompliance according to the Greening 1 (crop diversification) and SMR 1 rule (protection of waters against pollution caused by nitrates) rules of the CAP. Experiments show the effectiveness of the proposed integrated approach in three different scenarios for crop type monitoring and consistency checking for noncompliance to the CAP rules: the smart sampling of on-the-spot checks; the automatic detection of CAP's Greening 1 rule; and the automatic detection of susceptible parcels according to the CAP's SMR 1 rule.