%0 Dataset
%T Daily snow depth dataset on the Arctic (1980-2019)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/cecc11cd-9b6d-4abb-9bca-6b078a366aec
%W NCDC
%R 10.12072/ncdc.arctic-change.db7130.2026
%A WU Tonghua
%K Snow depth;long term;Random forest;data fusion;accuracy assessment
%X Snow Depth (SD) is a key parameter for characterizing snow thickness, playing a significant role in understanding regional water cycles, energy balance, and the impacts of climate change. To address the substantial uncertainties in existing remote sensing, reanalysis, and simulated snow depth products, as well as their insufficient accuracy in complex terrain regions, this project employs a random forest algorithm. It integrates snow depth products such as AMSR-E, AMSR2, NHSD, and GlobSnow, along with reanalysis datasets like ERA-Interim and MERRA2, and relevant environmental variables. Using ground-based observational snow depth data for model training and validation, a daily-scale SD product with a spatial resolution of 0.25° for the Arctic (north of 66°34'N) from 1980 to 2019 was generated through data fusion. Validation with measured snow depth data shows a correlation coefficient (R²) of 0.79, with a root mean square error (RMSE) of 8.5 cm and a mean absolute error (MAE) of 3.5 cm. This dataset provides crucial data support for hydrological modeling and data assimilation in land surface process models in the Arctic.