Snow depth inversion and mapping at 500 m resolution from 1980 to 2020 in Northeast China using radiative transfer model and machine learning

Abstract

Accurate snow cover parameter assessment and mapping at a fine resolution can have profound implications for our understanding of the planet’s water balance and energy dynamics. Passive microwave (PMW) remote sensing is among the most effective methods for snow depth (SD) retrieval. However, significant uncertainties persist in SD retrieval and mapping due to snow characteristic variations, forest canopy interference, and low spatial resolution of PMW. To overcome these limitations, a novel method considering multiple influencing factors was proposed by integration a radiation transfer model with a machine learning model for SD retrieval, and a 500 m resolution SD dataset (NCSD) was generated for 1980 − 2020 in Northeast China by combining downscaling model. The validation against independent ground observations revealed that the MAE, RMSE, and R values for NCSD were 4.39 cm, 6.65 cm and 0.77, respectively. Compared to existing SD products, NCSD data effectively avoid mixed pixel issues, improved SD retrieval performance, and reveal more refined snow cover spatiotemporal patterns. Additionally, the NCSD results indicated that the annual average SD in Northeast China exhibited an increasing trend from 1980 to 2020 (0.26 cm/10a, p > 0.05). However, a notable inflection point occurred in 2000, and a subsequent decreasing trend occurred from 2000 to 2020 (0.49 cm/10a, p > 0.05). Overall, the creation of NCSD effectively filled the gap related to high-resolution SD data, and facilitated the development of hydrological studies and climate change at the basin scale.