Soil moisture retrieval from Synthetic Aperture Radar (SAR) over vegetated terrains requires an isolation of soil and canopy signals from observed backscatter (σ°). This study develops a probabilistic soil moisture retrieval method from dual polarimetric C-band SAR Sentinel-1 (S-1) with uncertainty quantification at distinct vegetation covers (VCs). Both σ_VV° and σ_VH° were used to represent ground and volume scattering due to the high respective sensitivity to soil moisture and vegetation dynamics. A novel D-vine copula quantile regression (DVQR) was adopted to provide the soil moisture estimates based on modelling trivariate dependence of σ_VV°-σ_VH°-soil moisture anomalies (VV-VH-Mv), with a support from the innovative cosmic-ray soil moisture as ground-truth data. The feasibility of DVQR was underlined for: (1) multivariate nonlinear dependence structure modelling and (2) soil moisture retrieval with associated uncertainty. An inter-dependence analysis, which assesses the correlations among three major variables, indicated that the dependence between each pair of variables decreased as canopy density increases from herbs to forests, mainly due to the σ° attenuated by vegetation effect. The dependence structures simulated from the D-vine copula revealed highly nonlinear and asymmetric shapes with tail dependences occurred in most VCs, which can be well captured by different associated Archimedean copulas. Soil moisture anomaly (Mv) estimated using the DVQR and Multiple linear quantile regression (MLQR) were compared against ground-truth data for both in-sample and out-of-sample predictions. Superior performances of the DVQR in most VCs, with 10% and 16% improved in RMSE at grasslands and broadleaf forests, respectively, demonstrated the robustness of this method for S-1 soil moisture retrieval due to the highly nonlinear dependence structures captured by the D-vine models. Over VCs, better performances were obtained at low-canopy herbaceous regions (grasslands and croplands); whereas extremely dry conditions and complex structures in shrublands and dense forests resulted in inferior performances. A sensitivity analysis was then conducted to evaluate the change in Mv estimation accuracy given distinct VV and VH quantile levels. Result underlines that VV is the primary factor controlling the retrieval accuracy, but the increase in VH level also contributes to higher errors in soil moisture estimation, especially under wet conditions.

从植被地形上的合成孔径雷达（SAR）中获取土壤水分需要从观测到的反向散射（σ°）中隔离土壤和冠层信号。这项研究从双极化C波段SAR Sentinel-1（S-1）开发了一种概率土壤水分反演方法，该方法在不同植被覆盖度（VCs）下具有不确定性量化。两个σ _VV °和σ _VH °被用来表示地面和体积散射由于对土壤水分与植被动态高灵敏度相应。一种新颖的d-藤系词位数回归（DVQR）获得通过提供一种基于模型的三变量σ依赖土壤水分估计_VV °-σ _VH°土壤湿度异常（VV-VH-Mv），并以创新的宇宙射线土壤湿度作为地面真实数据的支持。强调了DVQR的可行性：（1）多元非线性依赖结构建模和（2）具有相关不确定性的土壤水分反演。相互依赖性分析评估了三个主要变量之间的相关性，结果表明，每对变量之间的依赖性随着冠层密度从草药到森林的增加而降低，这主要是由于植被效应减弱了σ°。从D-蔓科珀拉模拟的依赖结构显示出高度的非线性和不对称形状，并且在大多数VC中都出现了尾部依赖，可以通过不同的相关阿基米德科珀斯很好地捕获。对于样本内和样本外预测，将使用DVQR和多元线性分位数回归（MLQR）估算的土壤水分异常（Mv）与地面真实数据进行了比较。DVQR在大多数VC中具有出色的性能，草原和阔叶林的RMSE分别提高了10％和16％，这表明由于D捕获的高度非线性依赖性结构，该方法对于S-1土壤水分取回的鲁棒性藤模型。在VC上，低冠层牧草区（草地和农田）获得了更好的性能。而灌木丛和茂密森林中极端干燥的条件和复杂的结构导致其性能较差。然后，在不同的VV和VH分位数水平下，进行敏感性分析以评估Mv估算准确性的变化。