The objective of this work was to find a model to estimate surface soil moisture by using the available andfree satellite imagery data of synergy of single polarized C-band sentinel-1 and Optical Sentinel-2 Data acquired in dry and wet seasons on study site in Niger.Water cloud model (WCM) was selected in this study due to its ability to describe backscattering coefficients interms of soil proprieties and vegetation proprieties. First, we founded the relation between Sentinel-1 backscattering coefficients???????????? ???? and ???????????? ???? in Cbandthat are adjusted to minimizevegetation effect, and ground measurement of soil moisture [downloaded from the International Soil Moisture Network (ISMN)], using linear regression with Gradient descent. The sensitivity of the SAR backscatter to in situ measurement was estimated as 0.7 and 0.43db/Vol% for VV and VH polarizations, respectively. Second, the Normalized Difference Vegetation Index (NDVI) was used as vegetation descriptor in the WCM, it is extracted directly from the preprocessed sentinel-2 optical images. The Calibration of WCM consists in fitting the model against ground measurements and estimated parameters for VV and VH polarization by minimizing the sum of squares of the differences between the simulated and measured radar signal.