A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

作者：Yanxing Hu, Tao Che, Liyun Dai, Yu Zhu, Linhui Xiao, Jie Deng, Xin Li · 发表于：Big Earth Data · 年份：2023 · DOI：10.1080/20964471.2023.2177435 · 被引用次数：22 · 研究领域：Cryospheric studies and observations、Climate variability and models、Meteorological Phenomena and Simulations

A high-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth products and reanalysis snow depth products. However, existing gridded snow depth products have some shortcomings. Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth, while reanalysis snow depth products have coarse spatial resolutions and great uncertainties. To overcome these problems, in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), the Northern Hemisphere Snow Depth (NHSD), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), incorporating geolocation (latitude and longitude), and topographic data (elevation), which were used as input independent variables. More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time periods. This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°. Here, we compared ...