Abstract
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·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 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·m−3. 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).
Highlights
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 best-performing parameters, estimates based on backscatter coefficients regardless of the polarization state show correlations greater than 0.60, as do the following polarimetric indicators: 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, and the total scattered power (SPAN)
For these parameters derived from full-polarization images, the error level is between 4.8% and 5.3% m3·m−3
Summary
Numerous studies based on synthetic aperture radar (SAR) imagery have demonstrated the usefulness of microwave remote sensing data for surface soil moisture (SSM) estimation. The continuity of satellite missions in this frequency since the 1990s (with ERS-1/2, Envisat, Radarsat-1/2 or Sentinel-1a/b) explains the numerous studies, compared to the work carried out with other antenna configurations. In the majority of cases, the images delivered by these missions were characterized by one or even two polarization states. With missions such as Radarsat-2 and in particular the acquisition beam modes giving access to the four polarization states, the study of other metrics derived from satellite images became possible. The performance and limitations associated with polarimetric approaches remain to be established, as only a few studies have been carried out on the contribution of these data to the estimation of SSM
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