FILTERING LPIS DATA FOR BUILDING TRUSTWORTHY TRAINING DATASETS FOR CROP TYPE MAPPING: A CASE STUDY IN GREECE

The need for effective crop monitoring in large geographical scales has become increasingly important in recent years and constitutes a technological and scientific challenge for remote sensing applications. In Europe, member states of the European Union collect geospatial data in the framework of the Land Parcel Information System (LPIS) for agricultural management and subsidizing farmers. These data can be exploited as training datasets of machine learning classifiers for crop-type mapping applications. However, the way the LPIS data are being generated, concerning primarily errors in the farmers’ declarations in terms of crop-type labels, exact geometries, etc, constrains their direct use in such classification frameworks. In this study, we present and assess a methodology for filtering LPIS data based on geometric and spectral criteria in order to build a trustworthy training dataset for machine learning crop-type classifiers. A new nomenclature was developed, oriented towards the spectral discrimination of crop-type classes and sub-classes in Greece. The filtering methodology was applied at national scale for the agricultural year of 2019 and resulted in the selection of a sub-set of the LPIS parcels that were assessed as the most suitable and reliable to represent the different levels of the nomenclature. The developed filtering procedure was validated against actual crop-type labels derived from field visits, while the resulted filtered data were successfully utilized on various crop-type mapping experiments in Greece.