Irrigation mapping using Sentinel-1 and Sentinel-2 data

Water resource management is a key issue in climate change conditions, considering the increasing number of drought events, as well as the increase in water use for irrigation in Mediterranean region. In this context, different decision tools have been developed to optimize water use for irrigation. One crucial question for managers is the precise identification of irrigated areas. Remote sensing has shown great potential for irrigation mapping. This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 and Sentinel-2 data. An application is proposed at two study sites in Mediterranean region, in Spain and in Italy, with two climatic contexts, semiarid and humid respectively. Several classifiers are proposed to separate irrigated and rainfed areas. They are based on statistical variables from Sentinel-1 and Sentinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5-km surroundings. Support Vector Machine (SVM) classification approach is tested with different options to evaluate the robustness of the proposed methodologies. The optimal number of metrics found is five. The highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. In addition, the accuracy is consistent at the two study sites.