How Accurately and in What Detail Can Land Use and Land Cover Be Mapped Using Copernicus Sentinel and LUCAS 2022 Data?

This study explored the potential of the Land Use/Cover Area frame Survey (LUCAS) data for generating detailed Land Use and Land Cover (LULC) maps. Although earth observation (EO) satellites provide extensive temporal and spatial coverage, limited representative field data often results in LULC maps with broad classification schemes. In this research, we investigated the classification of detailed vegetation cover classes in 27 countries that are part of the European Union (EU) in 2022 using incrementally refined classification schemes, intending to increase the thematic depth and maintain meaningful accuracy. The LUCAS 2022 field survey dataset with 52 LULC classes and a Random Forest (RF) classifier was used to test flat and hierarchical classification approaches, along with class imbalance analysis. Based on balanced and imbalanced datasets, a 26-class classification scheme balances accuracy and detail. This study emphasized the potential of LUCAS data to provide thematic depth in vegetation cover mapping. In contrast, our previous studies focused on crop type classification utilizing Copernicus Sentinel-1 and -2 imagery and LUCAS data on a broader LULC scheme. The study also showed the importance of data balancing for achieving better classification outcomes and provides insights for large-scale LULC mapping applications in agriculture.