Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3091-2022
https://doi.org/10.5194/essd-14-3091-2022
Data description paper
 | 
08 Jul 2022
Data description paper |  | 08 Jul 2022

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

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Cited articles

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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.
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