The present study provided the first-time comprehensive evaluation of 12 advanced statistical and machine learning (ML) algorithms for the Soil Moisture (SM) estimation from dual polarimetric Sentinel-1 radar backscatter. The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel’s (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. Extensive field samplings were performed for collection of in-situ SM data and other parameters from the selected sites for seven different dates and at two different locations (Varanasi and Guntur District, India), concurrent to Sentinel-1 overpasses. The backscattering coefficients were considered as input variables and SM as output variable for the training, validation and testing of the ML algorithms. The site at Varanasi was used for the training, validation and testing of the models. On the other hand, the Guntur site was used as an independent site for checking the model performance, before finalizing the algorithms. The performances of different trained algorithms were evaluated in terms of correlation coefficient (r), root mean square error (RMSE) (in m³/m³) and bias (in m³/m³). The study identified the RF, SBC and ANFIS as the top three best performing models with comparable and promising SM estimation. In order to test the robustness of these best models (RF, SBC and ANFIS), further performance analysis was performed to the independent datasets of the Varanasi and Guntur test sites, which indicates that the performance of these three models were consistent and SBC can be recommended as the best among all for SM estimation.

本研究首次全面评估了 12 种先进的统计和机器学习 (ML) 算法，用于从双极化 Sentinel-1 雷达反向散射估计土壤水分 (SM)。ML 算法即支持向量机 (SVM)，具有线性、多项式、径向和 sigmoid 内核、随机森林 (RF)、多层感知器 (MLP)、径向基函数 (RBF)、Wang 和孟德尔 (WM)、减法聚类(SBC)、自适应神经模糊推理系统 (ANFIS)、混合模糊干扰系统 (HyFIS) 和动态进化神经模糊推理系统 (DENFIS)。进行了广泛的现场采样，以从选定地点的七个不同日期和两个不同地点（印度瓦拉纳西和贡图尔区）收集原位 SM 数据和其他参数，与 Sentinel-1 立交桥并行。后向散射系数被视为输入变量，SM 被视为输出变量，用于 ML 算法的训练、验证和测试。瓦拉纳西的站点用于模型的训练、验证和测试。另一方面，在最终确定算法之前，Guntur 站点被用作检查模型性能的独立站点。不同训练算法的性能根据相关系数（在最终确定算法之前，Guntur 站点被用作检查模型性能的独立站点。不同训练算法的性能根据相关系数（在最终确定算法之前，Guntur 站点被用作检查模型性能的独立站点。不同训练算法的性能根据相关系数（r )、均方根误差 (RMSE)（以 m ³ /m ³为单位）和偏差（以 m ³ /m ³为单位）。该研究将 RF、SBC 和 ANFIS 确定为具有可比和有希望的 SM 估计的前三个最佳性能模型。为了测试这些最佳模型（RF、SBC 和 ANFIS）的稳健性，对 Varanasi 和 Guntur 测试站点的独立数据集进行了进一步的性能分析，这表明这三个模型的性能是一致的，SBC 可以推荐作为 SM 估计中最好的。