SURFACE MOISTURE AND IRRIGATION MAPPING AT AGRICULTURAL FIELD SCALE USING THE SYNERGY SENTINEL-1/SENTINEL-2 DATA

We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.

In this study, we investigate the combination of surface soil moisture information with process-related features, namely, evaporation efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-related features yielded more accurate predictions in the case of arid and semiarid conditions. However, they have no to little added value in temperate to tropical conditions.

In the context of major changes, the southern Mediterranean area faces serious challenges with low and continuously decreasing water resources mainly attributed to agricultural use. A method for projecting irrigation water demand under both anthropogenic and climatic changes is proposed. Time series of satellite imagery are used to determine a set of semiempirical equations that can be easily adapted to different future scenarios.