This paper aims at addressing the potential of polarimetric indices derived from C-band Radarsat-2 images to estimate the surface soil moisture (SSM) over bare agricultural soils. Images have been acquired during the Multispectral Crop Monitoring (MCM) experiment throughout an agricultural season over a study site located in southwestern France. Synchronously with the acquisitions of the 22 SAR images, field measurements of soil descriptors were collected on surface states with contrasting conditions, with SSM levels ranging from 2.4% to 35.3% m3
, surface roughness characterized by standard deviation of roughness heights ranging from 0.5 to 7.9 cm, and soil texture showing fractions of clay, silt and sand between 9%–58%, 22%–77%, and 4%–53%, respectively. The dataset was used to independently train and validate a statistical algorithm (random forest), SSM being estimated using the polarimetric indices and backscatter coefficients derived from the SAR images. Among the SAR signals tested, the performance levels are very uneven, as evidenced by magnitude of correlation (R2
) ranging from 0.35 to 0.67. The following polarimetric indices present the best estimates of SSM: the first, second and third elements of the diagonal (T11, T22, and T33), eigenvalues (λ1, λ2, λ3 from Cloude–Pottier decomposition), Shannon entropy, Freeman double-bounce and volume scattering mechanisms, the total scattered power (SPAN), and the backscattering coefficients whatever the polarization state, with correlations greater than 0.6 and with RMSE ranged between 4.8% and 5.3% m3
. These performances remain limited although they are among the best SSM estimates using C-band images, comparable to those obtained with other approaches (i.e., empirical, physical based, or model inversion).