Please login first
Use of statistical approach combined with SAR polarimetric indices for surface moisture estimation over bare agricultural soil
* 1 , 2
1  Centre d’Études de la BIOsphère (CESBIO), Université de Toulouse, CNES/CNRS/INRAe/IRD/UT3, Toulouse, France
2  Centre d’Études de la BIOsphère (CESBIO), Université de Toulouse, CNES/CNRS/INRAe/IRD/UPS, Toulouse, France


Numerous studies based on synthetic aperture radar (SAR) imagery have demonstrated the usefulness of microwave remote sensing data for surface soil moisture (SSM) estimation. Among the parameters that can be derived from these images, backscatter coefficients have been the subject of most studies, unlike polarimetric approaches whose performance and limitations remain to be established. In this context, this paper aims at addressing the potential of polarimetric indices derived from C-band Radarsat-2 images to estimate the surface soil moisture over bare agricultural soils (at the plot spatial scale). 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·m−3, 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 with co and cross-polarization states 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 derived from the SAR images 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·m−3. These performances remain limited although similar to those obtained using other approaches (empirical, physical based, or model inversion).

Keywords: Surface soil moisture; bare soils; synthetic aperture radar; Radarsat-2; polarimetry; random forest