Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, Key Laboratory for Land Satellite Remote Sensing
Applications of Ministry of Natural Resources, School of Geography and Ocean
Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, Key Laboratory for Land Satellite Remote Sensing
Applications of Ministry of Natural Resources, School of Geography and Ocean
Science, Nanjing University, Nanjing, Jiangsu 210023, China
Pengfeng Xiao
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, Key Laboratory for Land Satellite Remote Sensing
Applications of Ministry of Natural Resources, School of Geography and Ocean
Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing University, Nanjing, Jiangsu 210023,
China
Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote
Sensing Experimental Research Station, Cold and Arid Regions Environmental
and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou
730000, China
Zhaojun Zheng
The National Satellite Meteorological Center, Beijing
100081, China
Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote
Sensing Experimental Research Station, Cold and Arid Regions Environmental
and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou
730000, China
Wenbo Luan
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, Key Laboratory for Land Satellite Remote Sensing
Applications of Ministry of Natural Resources, School of Geography and Ocean
Science, Nanjing University, Nanjing, Jiangsu 210023, China
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Total article views: 3,186 (including HTML, PDF, and XML)
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Total article views: 2,106 (including HTML, PDF, and XML)
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Total article views: 1,080 (including HTML, PDF, and XML)
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The geographically and temporally weighted neural network (GTWNN) model is constructed for estimating large-scale daily snow density by integrating satellite, ground, and reanalysis data, which addresses the importance of spatiotemporal heterogeneity and a nonlinear relationship between snow density and impact variables, as well as allows us to understand the spatiotemporal pattern and heterogeneity of snow density in different snow periods and snow cover regions in China from 2013 to 2020.
The geographically and temporally weighted neural network (GTWNN) model is constructed for...
