Soil moisture (SM) is an important variable related to the health of terrestrial ecosystems, agriculture, continental water cycle, etc. It also provides an opportunity for drought monitoring, flood forecasting, weather forecasting, and calibration of hydrological models. This study aims to estimate surface soil moisture at high spatial resolution (10m) by combining radar and optical remote sensing data and improving spatial resolution and accuracy. Synthetic Aperture Radar (SAR) operates with the competence to acquire data in any weather condition. SAR images were acquired by C-band SAR sensors in the VV polarization boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral 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 model (parameters include vegetation descriptors and model coefficients) to estimate SM and reduce the effect of vegetation cover. The method is applied in 13 months of time-series satellite data from November 7, 2019, to October 20, 2020, over Salamanca (western Spain) and is validated using field data acquired at a study site with the use TDR sensor. The results showed good accuracy between retrieves and ground measurement, soil moisture data (Root Mean Square Error (RMSE) of 0.53 m^3/m^3 and the obtained accuracy is promising compared to recent similar works.