Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6-12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Our objective was to develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, and to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field. However, for soil moisture retrievals from Sentinel 1 observations in grasslands, there is still the need to identify robust and parsimonious solutions, accounting for the effects of vegetation attenuation and their seasonal variability. In a grass experimental site in Sardinia, where field measurements of soil moisture were available for the 2016-2018 period, three common retrieval methods have been compared to estimate soil moisture from Sentinel 1 data, with increasing complexity and physical interpretation of the processes: the empirical change detection method, the semi-empirical Dubois model, and the physically-based Fung model. In operational approaches for soil moisture mapping from remote sensing, the parameterization simplification of soil moisture retrieval techniques is encouraged, looking for parameter estimates without a priori information. We have proposed a simplified approach for estimating a key parameter of retrieval methods, the surface roughness, from the normalized difference vegetation index (NDVI) derived by simultaneous Sentinel 2 optical observations. Soil moisture was estimated better using the proposed approach and the Dubois model than by using the other methods, which accounted vegetation effects through the common water cloud model. Furthermore, we successfully merged radar-based soil moisture observations and a land surface model, through a data assimilation approach based on the Ensemble Kalman filter, providing robust predictions of soil moisture.