Soil Moisture Estimation based on the Distributed Scatterers Adaptive Filter over the QTP Permafrost Region using Sentinel-1 and High-resolution TerraSAR-X Data

Abstract

ABSTRACT Synthetic aperture radar (SAR) data have been widely and primarily used for soil-moisture estimation. The original radar backscattering coefficients show great noise and temporal-spatial decorrelation due to complex surface features and seasonal vegetation changes, which increase the difficulty of soil-moisture estimation over the Qinghai-Tibet Plateau (QTP). To overcome these limitations, a soil-moisture retrieval method based on the Distributed Scatterers Adaptive Filter (DSAF) is proposed and applied in this paper. First, the statistically homogeneous pixels (SHP) with similar scatter behaviour are identified using the Anderson-Darling (AD) test method. Then, the Distributed Scatterers (DSs) are selected according to the SHPs threshold, and the backscattering coefficients of DSs are filtered using the SHPs distribution and coherences. In order to eliminate the negative influence of vegetation and roughness, the filtered backscattering coefficients are further processed by coupling the Water Cloud Model (WCM) and the images acquired in the winter season. The response of soil moisture to backscattering coefficients is greatly improved. Finally, the backscatter coefficients () are transformed into soil-moisture units by using the Least Square Regression (LSR) model and multi-temporal Change Detection (CD) method, respectively. The 15 scenes of TerraSAR-X images and 18 scenes of Sentinel-1 images in Northern Piedmont river, Northern Tibet, were used for experimental study. The results reveal that the response of the filtered backscattering coefficients to in-situ soil moisture is greatly improved. The soil-moisture content estimated by LSR and CD model using TerraSAR-X data is consistent with in-situ measurements, with coefficient of determination (R 2) of 0.52 and 0.56, Root Mean Square Error (RMSE) of 0.08 and 0.06, respectively. The results show that the proposed method can improve the accuracy of soil-moisture estimation, especially over the complex surface features of the QTP permafrost region.