Abstract
Reliable crop mapping is essential for understanding agricultural practices, optimizing resource use, and analyzing rural dynamics, while also supporting modelling and sustainable agriculture planning. However, obtaining 10 m crop distribution maps remains challenging in Mediterranean regions, where data availability is often limited and landscapes are fragmented. The main European land use dataset, Corine Land Cover (CLC), lacks both the crop specificity required for accurate crop differentiation and the temporal frequency needed for timely monitoring. This study addresses these limitations by implementing the IOTA2 automated chain in Sardinia (Italy), to create a large-scale crop map specifically targeting Mediterranean crops. The methodology leverages open-source satellite imagery with supervised machine learning, using the 2018 Land Parcel Identification System (LPIS), CLC, and Urban Atlas dataset for training. We compared two nomenclatures, detailed (32 classes) versus simplified (25 classes), testing each across three training sample sizes (10%, 50%, and 100%). Results indicate that the simplified nomenclature (N25) provided more robust performances, achieving an overall accuracy (OA) of 0.77 with full sampling, compared to 0.61 for the detailed version. These OA values refer to the subset of reference polygons held out from the reference data for independent pixel-level validation. Moreover, a final map was produced using the entire reference dataset for training and evaluated through zonal area agreement. Mapping showed high performance for specific crops like rice, citrus, and grapevine, while classes such as cereals and fruit trees presented classification challenges due to high fragmentation of the landscape and irregular crop-distribution patterns. Despite these challenges, this work delivers a 10 m spatial resolution reproducible framework that enhances thematic details of current European datasets. By running as a single automated processing chain rather than a sequence of manually executed steps, it offers a scalable solution for rapid, annual crop monitoring in complex, data-scarce Mediterranean environments.