An Operational High Resolution Soil Moisture Retrieval Algorithm Using Sentinel-1 Images

Abstract

Monitoring the surface soil moisture (SSM) in agricultural areas at plot scale helps in many applications such as irrigation planning and crop management. Over the last decade, SAR (Synthetic Aperture Radar) data have shown great potential in the estimation SSM in agriculture areas. Today, Sentinel-1 (S1) and Sentinel-2 (S2) satellites present a good opportunity for operational SSM estimates in agricultural areas because they provide free and open access data at high spatial resolution (10 m × 10 m) and high revisit time (6 days over Europe). The aim of this communication is to present an operational approach for mapping soil moisture at high spatial resolution (plot scale) in agriculture areas by coupling S1 and S2 images. The proposed approach is based on the inversion of the Water Cloud Model (WCM) combined with the modified Integral Equation Model (IEM). Neural networks were developed and validated using synthetic SAR C-band database. The results showed that the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol.%.