Estimating volumetric soil moisture (Mv) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate Mv and S over vegetated agricultural areas. The method was based on applying the machine learning inversion approach along with SAR data to invert a combination of the parameterized water cloud model (PWCM) and the calibrated integral equation model (CIEM). The soil backscattered component in water cloud model (WCM) was generated by CIEM to be applied to the WCM parameterization and dataset simulation. Three machine learning algorithms, including the support vector regression (SVR), multi-output SVR (MSVR), and artificial neural network (ANN), were employed to model the relationship between the simulated dataset variables. The genetic algorithm was also applied to optimize the models’ parameters. The inversion technique results demonstrated that the MSVR and ANN had the highest accuracy in estimating Mv and S due to their better structures. The SMAPVEX-16 in situ dataset, along with three Sentinel-1 images, was applied to evaluate the accuracy of the WCM parameterization and the proposed method for Mv and S estimation. The accuracies of the PWCM in the VV and VH polarizations of Sentinel-1 C-band data were reasonable for VWC < 2.5 kg / m2 [root-mean-square error (RMSE) = 1.44 and 1.77 dB, respectively]. Additionally, it was observed that the trained SVR, MSVR, and ANN had similar results for different VWC values. In summary, the proposed method had high potential in vegetated agricultural areas with VWC < 2.5 kg / m2, for which the RMSEs were 4 to 7 vol. % and 0.35 to 0.46 cm depending on the VWC values in retrieving Mv and S, respectively.

中文翻译：

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结合水云模型和校正积分方程模型的植被覆盖农业地区土壤参数估计的机器学习反演方法

估算土壤含水量（Mv）和表面粗糙度（S）是许多农业和水文应用的关键参数。尽管可以从合成孔径雷达（SAR）数据中有效地检索这两个参数，但是植被的存在会对结果产生负面影响。提出了一种方法来准确估算植被覆盖农业地区的Mv和S。该方法基于将机器学习反演方法与SAR数据一起应用，以对参数化水云模型（PWCM）和校准积分方程模型（CIEM）的组合进行反演。CIEM生成了水云模型（WCM）中的土壤后向散射分量，并将其应用于WCM参数化和数据集模拟。三种机器学习算法，包括支持向量回归（SVR），采用多输出SVR（MSVR）和人工神经网络（ANN）对模拟数据集变量之间的关系进行建模。遗传算法也被用于优化模型的参数。反演技术结果表明，MSVR和ANN具有较好的结构，在估计Mv和S方面具有最高的准确度。SMAPVEX-16原位数据集与三张Sentinel-1图像一起用于评估WCM参数化的准确性以及所提出的Mv和S估计方法。对于VWC <2.5 kg / m2 [均方根误差（RMSE）分别为1.44和1.77 dB]，Sentinel-1 C波段数据的VV和VH极化中PWCM的精度是合理的。此外，可以观察到，对于不同的VWC值，受过训练的SVR，MSVR和ANN的结果相似。总而言之，该方法在VWC <2.5 kg / m2的植被农业地区具有很高的潜力，其RMSE为4至7 vol。取Mv和S时，取决于VWC值分别为％和0.35至0.46 cm。