Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3091-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3091-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
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
Frank-M. Göttsche
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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Satellite land surface temperature (LST) data are vital for earth system studies, but current LST products face limitations: the impact of angular effect, lacking coverage under clouds, and the coarse resolution. This study innovatively generated hourly, angular-normalized, all-weather, high-resolution (0.01°) LST data from 2018 to 2023 using FY-4A official data, which support better LST-related studies of earth system science.
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This paper reported a daily 1 km all-weather land surface temperature (LST) dataset for Chinese land mass and surrounding areas – TRIMS LST. The results of a comprehensive evaluation show that TRIMS LST has the following special features: the longest time coverage in its class, high image quality, and good accuracy. TRIMS LST has already been released to the scientific community, and a series of its applications have been reported by the literature.
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Short summary
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...
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