Soil moisture is vital for the crop growth and directly affects the crop yield. The conventional synthetic aperture radar (SAR) based soil moisture monitoring is often influenced by vegetation cover and surface roughness. The machine-learning methods are not constrained by physical parameters and have high nonlinear fitting capabilities. In this study, machine-learning methods were applied to estimate soil moisture over winter wheat fields during its growing season. RADARSAT-2 data with quad polarizations and 240 sample plots in the study area were acquired and collected, respectively. In addition to the four linear polarization channels, polarimetric decomposition parameters were extracted to expand the SAR feature space. Three advanced machine-learning models were selected and compared, which were support vector regression, random forests (RF), and gradient boosting regression tree. To improve the performances of the models, three feature-selection methods were compared, which were based on Pearson correlation, support vector machine recursive feature elimination, and RF, respectively. The coefficient of determination ( R ² ) and root-mean-square error (RMSE) were used to compare and assess the performances of those models. The results revealed that polarimetric decomposition parameters were effective in estimating soil moisture, and RF model obtained the highest prediction accuracy (training set: RMSE = 2.44 vol.% and R ² = 0.94; and validation set: RMSE = 4.03 vol.%, and R ² = 0.79). This study finally concluded that using polarimetric decomposition parameters combined with machine-learning and feature-selection methods could effectively estimate soil moisture at a high accuracy, which helps monitor soil moisture across the agricultural field during its growing season.

中文翻译：

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利用机器学习方法估算冬小麦生长季土壤水分

土壤水分对于农作物的生长至关重要，直接影响农作物的产量。基于常规合成孔径雷达（SAR）的土壤湿度监测通常受植被覆盖和表面粗糙度的影响。机器学习方法不受物理参数的限制，具有很高的非线性拟合能力。在这项研究中，应用了机器学习方法来估算冬小麦田在生长季节的土壤湿度。分别采集和收集了研究区域内具有四极极化的RADARSAT-2数据和240个样本图。除了四个线性极化通道外，还提取了极化分解参数以扩展SAR特征空间。选择并比较了三种先进的机器学习模型，分别是支持向量回归，随机森林（RF），和梯度增强回归树。为了提高模型的性能，比较了三种特征选择方法，分别基于Pearson相关，支持向量机递归特征消除和RF。确定系数（ [R ² ）和均方根误差（RMSE）用于比较和评估这些模型的性能。结果表明，极化分解参数可有效估算土壤水分，RF模型获得了最高的预测精度（训练集：RMSE = 2.44 vol。％和[R ² = 0.94；和验证集：RMSE = 4.03 vol。％，以及[R ² = 0.79）。这项研究最终得出结论，将极化分解参数与机器学习和特征选择方法相结合，可以高效，高效地估算土壤水分，从而有助于监测整个生长季农田中的土壤水分。