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 Lin Xiao Jie Deng Xin Li 

机构地区：[1]Key Laboratory of Remote Sensing of Gansu Province,Heihe Remote Sensing Experimental Research Station,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou,China [2]College of Resources and Environment,University of Chinese Academy of Sciences,Beijing,China [3]State Key Laboratory of Tibetan Plateau Earth System,Resources and Environment(TPESRE),Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing,China [4]Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security,Institute of International Rivers and Eco-Security,Yunnan University,Kunming,China [5]National Forestry and Grassland Administration Key Laboratory of Forest Resource Conservation and Ecological Safety on the Upper Reaches of the Yangtze River,Sichuan Province Key Laboratory of Ecological Forestry Engineering on the Upper Reaches of the Yangtze River,College of Forestry,Sichuan Agricultural University,Chengdu,China

出　　处：《Big Earth Data》2024年第2期274-301,共28页地球大数据（英文）

基　　金：supported by the National Science Fund for Distinguished Young Scholars(no.42125604);the National Nature Science Foundation of China(no.41771389,no.42001289 and no.42201159);the CAS‘Light of West China’Program(E029070101).

摘　　要：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 reana-lysis 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,ver-sion 2(MERRA-2),incorporating geolocation(latitude and longitude),and topographic data(elevation),which were used as input indepen-dent 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 the fused snow depth and the original gridded snow depth products with 13,272 observation sites,showing an improved precision of our product.The evaluation indices of the fused(best original)dataset yielded a coeffi-cient of determination R2 of 0.81(0.23),Root Mean Squared Error(RMSE)of 7.69(15.86)cm,and Mean Absolute Error(MAE)of 2.74(6.14)cm.Most of the bias(88.31%)between the fused snow depth and in situ observations was in the range of−5 cm to 5 cm.The accuracy assessment of independent snow observation sites-Sodankylä(SOD),Old Aspen(OAS),Old Black Spruce(OBS

关 键 词：Snow depth datasets machine learning data fusion Northern Hemisphere 

分 类 号：O62[理学—有机化学]