%0 Dataset
%T Daily Snow Depth Dataset for the Three-River Source Region (1980–2020)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/8095027c-03a7-4c6c-9a46-21e024e45374
%W NCDC
%R 10.12072/ncdc.nieer-snow.db6816.2025
%A Zhao Zisheng
%A Xiaohua Hao 
%A Li Hangxuan
%A Zhong Xinyue
%A Wu Xiaodong
%K Deep learning;passive microwave;snow depth;long time series
%X High-spatial-resolution snow depth data are critical for hydrological, ecological, and disaster research. However, passive microwave snow depth products (10/25 km) no longer meet modern demands for high precision and resolution. This study integrates newly calibrated Enhanced-Resolution Brightness Temperature (CETB) with optical snow cover fraction (SCF) and snow-covered days (SCD) data. Using a deep learning FT-Transformer model, we inverted daily snow depth data at 5 km spatial resolution during snow seasons (October–April) for the Three-River Source region. Compared to China’s long-term snow depth data (25 km), the 5 km snow depth product demonstrates superior accuracy, with RMSE typically below 8.5 cm, providing a reliable foundation for snow resource monitoring in the region.