Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient ${\sigma }_{HV}^0$; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (${\varphi }_{HH-VV}$) and the co-polarized backscattering coefficient ${\sigma }_{HH}^0$. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m³/m³) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m³/m³, with an explained variance of 44%. The simplified model has the lowest error (0.074 m³/m³) and explains 53.5% of soil moisture variation.