Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy
of Sciences, Lanzhou 730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Xin Li
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan
Plateau Earth System, Environment and Resources, Institute of Tibetan
Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy
of Sciences, Lanzhou 730000, China
Institute of Urban Study, Shanghai Normal University, Shanghai 200234,
China
Jingxin Che
School of Science, Nanchang Institute of Technology, Nanchang 330099, China
Juha Aalto
Department of Geosciences and Geography, University of Helsinki, P.O.
Box 64, Gustaf Hällströmin katu 2a, 00014 Helsinki, Finland
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Department of Geosciences and Geography, University of Helsinki, P.O.
Box 64, Gustaf Hällströmin katu 2a, 00014 Helsinki, Finland
Huijun Jin
Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy
of Sciences, Lanzhou 730000, China
Institute of Cold Regions Science and Engineering and School of Civil
Engineering, Northeast Forestry University, Harbin 150040, China
Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy
of Sciences, Lanzhou 730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Xiaoli Chang
School of Resource and Environment and Safety Engineering, Hunan
University of Science and Technology, Xiangtan 411201, China
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279
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6,845
1,943
140
8,928
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246
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BibTeX: 158
EndNote: 246
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1,371
987
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2,402
23
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Total article views: 11,330 (including HTML, PDF, and XML)
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Total article views: 8,928 (including HTML, PDF, and XML)
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Datasets including ground temperature, active layer thickness, the probability of permafrost occurrence, and the zonation of hydrothermal condition with a 1 km resolution were released by integrating unprecedentedly large amounts of field data and multisource remote sensing data using multi-statistical\machine-learning models. It updates the understanding of the current thermal state and distribution for permafrost in the Northern Hemisphere.
Datasets including ground temperature, active layer thickness, the probability of permafrost...
