Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-443-2026
© 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-18-443-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Energy-conservation datasets of global land surface radiation and heat fluxes from 2000–2020 generated by CoSEB
Junrui Wang
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
Ronglin Tang
CORRESPONDING AUTHOR
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
Meng Liu
State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Zhao-Liang Li
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
State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Yizhe Wang, Ronglin Tang, Meng Liu, Lingxiao Huang, and Zhao-Liang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-272, https://doi.org/10.5194/essd-2025-272, 2025
Preprint under review for ESSD
Short summary
Short summary
We developed a new global daily dataset of turbulent heat exchanges between the ocean and atmosphere from 1993 to 2017. Utilizing a novel approach that combines machine learning with physical constraints, our model generates more accurate and physically reasonable estimates compared to existing datasets. This advancement enables improved understanding of ocean-atmosphere interactions, which are crucial for monitoring Earth's energy and water cycles and enhancing climate change projections.
Jia-Hao Li, Zhao-Liang Li, Xiangyang Liu, and Si-Bo Duan
Earth Syst. Sci. Data, 15, 2189–2212, https://doi.org/10.5194/essd-15-2189-2023, https://doi.org/10.5194/essd-15-2189-2023, 2023
Short summary
Short summary
The Advanced Very High Resolution Radiometer (AVHRR) is the only sensor that has the advantages of frequent revisits (twice per day), relatively high spatial resolution (4 km at the nadir), global coverage, and easy access prior to 2000. This study developed a global historical twice-daily LST product for 1981–2021 based on AVHRR GAC data. The product is suitable for detecting and analyzing climate changes over the past 4 decades.
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Short summary
Existing remote sensing datasets could not provide all land-atmosphere radiation/heat flux components while satisfying energy balances. This study generates the first data-driven datasets (2000–2020) based on our renewed Coordinated estimates of land Surface Energy Balance model, providing all high-accuracy components with perfect energy balance. This advancement enhances the study of Earth’s surface energy dynamics, enables better water management, and improves renewable energy planning.
Existing remote sensing datasets could not provide all land-atmosphere radiation/heat flux...
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