Paper Info
Title
Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China
Abstract
Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth's surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms cannot capture snow thickness well. In this study, we present a new operational retrieval algorithm, hereafter referred to as the pixel-based method (0.25 degrees x 0.25 degrees grid-level), to provide more accurate and nearly real-time snow depth estimates. First, the reference snow depth was retrieved using a previously proposed model in which a microwave snow emission model was coupled with a machine learning (ML) approach. In this process, an effective grain size (effGS) value was optimized by utilizing the snow microwave emission model, and then the nonlinear relationship between snow depth and multiple predictive variables, e.g., effGS, longitude, elevation, and brightness temperature (Tb) gradients, was established with the ML technique to retrieve reference snow depth data. To select a robust and well-performing ML approach, we compared the performance of widely used support vector regression (SVR), artificial neural network (ANN) and random forest (RF) algorithms over China. The results show that the three ML models performed similarly in snow depth estimation, which was attributed to the inclusion of effGS in the training samples. In this study, the RF model was used to retrieve the snow depth reference dataset due to its slightly stronger robustness according to our comparison of results. Second, the pixel-based algorithm was built based on the retrieved reference snow depth dataset and satellite Tb observations (18.7 GHz and 36.5 GHz) from Advanced Microwave Scanning Radiometer 2 (AMSR2) during the 2012-2020 period. For the pixel-based algorithm, the fitting coefficients were achieved dynamically pixel by pixel, making it superior to the traditional static methods. Third, the built pixel-based algorithm was verified using ground-based observations and was compared to the AMSR2, GlobSnow-v3.0, and ERA5-land products during the 2012-2020 period. The pixel-based algorithm exhibited an overall unbiased root mean square error (unRMSE) and R-2 of 5.8 cm and 0.65, respectively, outperforming GlobSnow-v3.0, with unRMSE and R-2 values of 9.2 cm and 0.22, AMSR2, with unRMSE and R-2 values of 18.5 cm and 0.13, and ERA5-land, with unRMSE and R-2 values of 10.5 cm and 0.33, respectively. However, the pixel-based algorithm estimates were still challenged by the complex terrain, e.g., the unRMSE was up to 17.4 cm near the Tien Shan Mountains. The proposed pixel-based algorithm in this study is a simple and operational method that can retrieve accurate snow depths based solely on spaceborne PM data in comparatively flat areas.
Year
DOI
Venue
2022
10.3390/rs14122800
REMOTE SENSING
Keywords
DocType
Volume
passive microwave remote sensing, machine learning, snow depth, pixel-based algorithm
Journal
14
Issue
ISSN
Citations 
12
2072-4292
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Jianwei Yang15812.73
Lingmei Jiang2347.85
Jinmei Pan301.01
Jiancheng Shi4794172.89
Shengli Wu5219.34
Jian Wang67640.08
Fangbo Pan701.01