SOIL MOISTURE RETRIEVAL OVER CROP REGION USING TIME-SERIES HIGH-RESOLUTION RCM DATA

Synthetic aperture radar (SAR), as an active microwave sensor, has proven to be effective in retrieving soil moisture (SM) over the past few decades. However, accurately estimating SM over agricultural regions is challenging due to the complex interactions between SM, soil roughness, and vegetation, resulting in mixed backscattering signals. The change detection (CD) method eliminates the influence of soil roughness by employing the ratio of two consecutive SAR images. However, the volume scattering caused by the crop canopy still affects SM estimation. To mitigate this limitation, we propose an advanced change detection method for SM retrieval using the random volume over ground (RVoG) decomposition on time-series compact-polarization SAR data. Experimental results using high-resolution time-series RCM in corn and soybean fields show promising performance, with root-mean-square-error (RMSE) values of 10.34 Vol.% and 7.41 Vol.% for RCH and RCV polarization in the corn field and 6.47 Vol.% and 5.03 Vol.% in the soybean field, respectively. The proposed method outperforms the original CD method, highlighting its potential as a reliable alternative for consistent SM retrieval from the RCM.