Articles | Volume 18, issue 8
https://doi.org/10.5194/essd-18-6139-2026
© Author(s) 2026. 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-18-6139-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
26 Aug 2026
Data description article |
| 26 Aug 2026
| 26 Aug 2026
Generation of angular-normalized, cloud-filled, 0.01°-downscaled land surface temperature from 2018 to 2023 based on official FY-4A dataset
National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100101, China
Biao Cao
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing and Digital Earth, the Advanced Interdisciplinary Institute of Satellite Applications, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Boxiong Qin
Guangdong Provincial Key Laboratory of Applied Botany & Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
Department of Environment Research and Innovation, Luxembourg Institute of Science and Technology, 4362 Belvaux, Luxembourg
Hua Li
National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100101, China
Lixin Dong
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Huanyu Zhang
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Wenfeng Zhan
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, China
Qinhuo Liu
National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100101, China
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Hu Zhang, Jing Li, Chenpeng Gu, Li Guan, Xiaohan Wang, Faisal Mumtaz, Yadong Dong, Jing Zhao, Qinhuo Liu, Shangrong Lin, and Wentao Yu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-42, https://doi.org/10.5194/essd-2025-42, 2025
Manuscript not accepted for further review
Short summary
Short summary
The 10m-resolution MuSyQ Global LCC has high accuracy in the validation using the ground measurements and can describe more spatial details compared with the current low-resolution LCC product, suggesting its potential in crop and forestry monitoring. We developed an online application that allows users to independently select regions of interest, time ranges, and spatial-temporal resolutions to generate their customized LCC products.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
Short summary
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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Short summary
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.
Satellite land surface temperature (LST) data are vital for earth system studies, but current...
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