Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach

ABSTRACT

Passive microwave (PM) remote sensing have been extensively used for snow depth (SD) estimation. However, current SD products from traditional PM data fail to capture the differentiation in mountainous and complex terrains with coarse resolution. Therefore, this study incorporates factors such as geographical location, topographic features, and land cover, along with various machine learning algorithms including Gaussian process regression (GPR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to construct and optimize SD estimation using enhanced-resolution PM data. The results demonstrate the following: (1)With the auxiliary variables, the SD product from LightGBM-based models exhibits the highest accuracy. (2)The performance of SD products from the LightGBM-based model varies monthly and annually, with shallow snow cover being slightly overestimated (30 cm). (3)The reliable SD product indicates spatial distribution characteristics in Xinjiang, with regions demonstrating no significant improvement being larger than those with no significant degradation. The above results illustrate the remarkable advantages of machine learning in capturing SD distribution and its spatio-temporal variation bolstered by enhanced PM data and auxiliary data.

KEYWORDS:

• Snow depth estimation
• machine learning
• brightness temperature
• enhanced resolution passive microwave data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

All co-authors of this manuscript significantly contributed to all phases of the investigation. They contributed equally to the preparation, analysis, review and editing of this manuscript.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (NSFC Grant 42371146, 42001061), the National Science and Technology Basic Resources Survey Program of China (2019FY100202), the Third Xinjiang Scientific Expedition Program (2022xjkk0602; 2021xjkk1400), the Youth Innovation Promotion Association, CAS (Y970000375) and the K.C.Wong Education Foundation (grant number: GJTD-2020-14).