Cropland Mapping Using Earth Observation Derived Phenological Metrics ^†

Satellite Earth observations provide timely and spatially explicit information on crop phenology that can support decision making and sustainable agricultural land management. Accurate classification and mapping of croplands is primary information for agricultural assessments. This study presents a digital agriculture approach that integrates Earth Observation big data analytics based on machine learning technologies to classify and map main crop types. Two supervised machine learning models were calibrated using the Random Forest algorithm from phenological metrics, estimated from time series of NDVI and LAI vegetation indices calculated using Sentinel-2 MSI satellite acquisitions. Models were calibrated for the Toscana region in Italy. The results show a satisfactory overall accuracy (~78%) in cropland classification, and the model calibrated using LAI time series performed slightly better than the model calibrated using NDVI time series. The proposed approach offers the potential to accurately map crop types in a way that is useful to support agricultural land management and monitoring systems for large areas over time.