A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
Falu Hong,Wenfeng Zhan,Frank-M. Göttsche,Zihan Liu,Pan Dong,Huyan Fu,Fan Huang,and Xiaodong Zhang
Falu Hong
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, International Institute for Earth System Science, Nanjing
University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, International Institute for Earth System Science, Nanjing
University, Nanjing, Jiangsu 210023, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing 210023, China
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz
1, 76344 Eggenstein-Leopoldshafen, Germany
Zihan Liu
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, International Institute for Earth System Science, Nanjing
University, Nanjing, Jiangsu 210023, China
Pan Dong
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, International Institute for Earth System Science, Nanjing
University, Nanjing, Jiangsu 210023, China
Huyan Fu
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, International Institute for Earth System Science, Nanjing
University, Nanjing, Jiangsu 210023, China
Fan Huang
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, International Institute for Earth System Science, Nanjing
University, Nanjing, Jiangsu 210023, China
Xiaodong Zhang
Shanghai Spaceflight Institute of TT&C and Telecommunication,
Shanghai, 201109, China
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Total article views: 4,352 (including HTML, PDF, and XML)
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Total article views: 3,305 (including HTML, PDF, and XML)
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Total article views: 1,047 (including HTML, PDF, and XML)
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Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for...
