Snow Depth Estimation Based on Parameter Combinations Selection and Machine Learning Algorithm Using C-Band SAR Data in Northeast China

Abstract

Radar images with high spatial resolution are not affected by illumination or meteorological conditions, which effectively compensate for the shortcomings of optical images and passive microwave images. Thus, active microwave remote sensing technology has advantages in snow depth (SD) research. Machine learning algorithms (MLAs), which do not need to consider complex physical models, have increasingly been applied to SD research. Considering the snow conditions of different underlying surfaces, it is very important to select the appropriate parameter combinations (PC) reflecting the SD information for MLAs. In this study, C-band SAR data with 20 m spatial resolution, groundbased SD observation data from meteorological stations, and field measurement data in Northeast China were used to construct and validate the SD estimation method. Two parameter selection methods including the correlation coefficient method and the machine learning (ML) fusion method were proposed to discuss the influence of different PC on SD estimation. Then, XGBoost, random forest (RF), linear support vector regression (LSVR), and kernel support vector regression (KSVR) were applied to estimate SD based on the selected PC, and evaluate the accuracies of SD estimation using different MLAs. The results demonstrated that the PC selected using the correlation coefficient method and XGBoost algorithm could achieve the best SD results in the study area. Combining RPC-C with XGBoost algorithm, in cropland areas, the average values of mean absolute error (MAE) and root mean squared error (RMSE) were 1.75 cm and 2.58 cm, respectively. Combining RPC-F with XGBoost algorithm, in forest areas, the average values of MAE and RMSE were 3.12 cm and 5.07 cm, respectively. The research of this paper can select the optimal PC for MLAs and effectively improve the accuracy of SD estimation using C-band SAR data.