Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data

Abstract

Soil moisture (SM) plays important roles in surface energy conversion, crop growth, environmental protection, and drought monitoring. As crops grow, the associated vegetation seriously affects the ability of satellites to retrieve SM data. Here, we collected such data at different growth stages of maize using Bragg and X-Bragg scattering models based on the Freeman–Durden polarization decomposition method. We used the H/A/Alpha polarization decomposition approach to extract accurate threshold values of decomposed scattering components. The results showed that the H and Alpha values of bare soil areas were lower and those of vegetated areas were higher. The threshold values of the three scattering components were 0.2–0.4 H and 7–24° Alpha for the surface scattering component, 0.6–0.9 H and 22–50° Alpha for the volume scattering component, and other values for the dihedral scattering component. The SM data retrieved (using the X-Bragg model) on June 27, 2014, were better than those retrieved at other maize growth stages and were thus associated with the minimum root-mean-square error value (0.028). The satellite-evaluated SM contents were in broad agreement with data measured in situ. Our algorithm thus improves the accuracy of SM data retrieval from synthetic-aperture radar (SAR) images.

Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery

“…Compared with the inversion results of CP SAR, the inversion accuracy of Sentinel-1 data for crop canopy water content in our study is lower. It is because that the polarization index of Sentinel-1 data confined to dual-polarization is generally less correlated with soil moisture content than full-polarization SAR data in agricultural areas [49,50].…”

Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

“…To deal with this problem, the optical and SAR data are combined, and several algorithms have been developed for estimation SMC [22,23]. Generally, these algorithms can be divided into two main categories: (1) algorithms that removed vegetation impact based on a vegetation microwave scattering model [24][25][26][27]; (2) algorithms in which vegetation impact was represented by vegetation indexes or polarization decomposition features and then the inversion model was employed to couple them with SMC [19,28,29].…”

“…On the other hand, the phase information contained in SAR images was decomposed into polarimetric parameters to evaluate the vegetation impact. The H/A/ α polarimetric decomposition (i.e., Cloude decomposition) model has been successfully employed in previous SMC retrieval [ 19 , 29 ]. For example, Özerdem et al [ 19 ] employed the H/A/ α method to decompose Radarsata-2 data to obtain the polarization characteristics inputted to the generalized regression neural network (GRNN) algorithm to retrieve SMC.…”

“…The results indicated that entropy (H), anisotropy (A), and alpha angle ( α ) are helpful for improving the accuracy of SMC retrieval. Xie et al [ 29 ] combined the H/A/ α and Freeman Durden polarization decomposition methods for SMC retrieval from full-polarization Radarsat-2 data. These parameters are directly affected by soil moisture.…”

The Potential of L-Band UAVSAR Data for the Extraction of Mangrove Land Cover Using Entropy and Anisotropy Based Classification

“…The value of H approaching 1 represents that the random scattering is in play and for H = 1, all the three coherency matrix Eigenvalues are equal and non-zero. For most natural objects, H lies between 0 and 1 [21]. As it increases from 0 to 1, the system approaches from simple scattering to completely random scattering.…”