Retrieval of Soil Moisture Using Time Series of Radar and Optical Remote Sensing Data at 10 m Resolution

Soil moisture (SM) is an important variable related to the health of terrestrial ecosystems, agriculture, the continental water cycle, etc. It also provides an opportunity for drought monitoring, flood forecasting, weather forecasting, and the calibration of hydrological models. This study aims to estimate the surface soil moisture at a high spatial resolution (10 m) by combining radar and optical remote sensing data and improving the spatial resolution and accuracy. Synthetic aperture radar (SAR) operates with the competence to acquire data in any weather condition. The SAR images were acquired by C-band SAR sensors in the VV polarization boarded on Sentinel-1 satellites and the optical images were obtained from a Sentinel-2 multispectral instrument. The main algorithm involves the retrieval of soil moisture using radar data through a change detection (CD) method that is somehow combined with the WCM (parameters include vegetation descriptors and model coefficients) to estimate the SM and reduce the effect of vegetation cover. The method is applied to 13 months of time-series satellite data, from 7 November 2019 to 20 October 2020, over Salamanca (western Spain) and is validated using field data acquired at a study site with the use of a TDR sensor. The results showed good accuracy between the retrieved and ground measurement soil moisture data (Root Mean Square Error (RMSE) of 0.053 m 3 / m 3 ) and the obtained accuracy is promising compared to recent similar works.