With the rapid development of remote sensing technology, synthetic aperture radar (SAR) is gradually widely used in snow depth (SD) estimation and serves as a useful complement to optical sensor and passive microwave (PM) sensor for snow remote sensing applications. The selection of parameters and models related to the characteristics of snow in forest is crucial for improving the accuracy of forest SD estimation. The purpose of this letter is to develop a forest SD estimation algorithm based on optimized feature filtering (FF) and deep neural network (DNN) using Sentinel-1 C-band data and other auxiliary data. First, the optimized features are selected from the input dataset using the three FF methods based on machine learning (ML), maximum mutual information coefficient (MMIC), and the proposed Pearson correlation coefficient (PCC). Then, a nonlinear regression method based on DNN was developed to retrieve SD with the optimized features. Comparing results obtained from random forest (RF) algorithm, XGBoost (XGB) algorithm, and recurrent neural network (RNN) algorithm against the meteorological stations and field measured SD data in the forests of Northeast China, the proposed method performs superior with reduced uncertainties. The mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination ( $R^{2}$ ) of the proposed SD estimation method are 2.98 cm, 4.30 cm, and 0.77 for the test dataset, respectively.

中文翻译：

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利用C波段SAR数据基于优化特征和DNN网络的森林积雪深度估计

随着遥感技术的快速发展，合成孔径雷达（SAR）逐渐广泛应用于雪深（SD）估计，并成为雪遥感应用中光学传感器和无源微波（PM）传感器的有益补充。与森林积雪特征相关的参数和模型的选择对于提高森林SD估算的准确性至关重要。这封信的目的是利用 Sentinel-1 C 波段数据和其他辅助数据，开发一种基于优化特征过滤（FF）和深度神经网络（DNN）的森林 SD 估计算法。首先，使用基于机器学习（ML）、最大互信息系数（MMIC）和提出的皮尔逊相关系数（PCC）的三种FF方法从输入数据集中选择优化特征。然后，开发了一种基于DNN的非线性回归方法来检索具有优化特征的SD。将随机森林（RF）算法、XGBoost（XGB）算法和循环神经网络（RNN）算法的结果与东北地区森林气象站和现场实测SD数据进行比较，该方法性能优越，不确定性降低。平均绝对误差 (MAE)、均方根误差 (RMSE) 和决定系数 ( $R^{2}$ 所提出的 SD 估计方法的测试数据集的 SD 估计值分别为 2.98 cm、4.30 cm 和 0.77。