Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-3835-2025
© Author(s) 2025. 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-17-3835-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
08 Aug 2025
Data description article |
| 08 Aug 2025
| 08 Aug 2025
A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022
Wangyipu Li
Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Beijing Key Laboratory of Spatial Information Integration and Its Applications, Beijing 100871, China
Zhaoyuan Yao
Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Beijing Key Laboratory of Spatial Information Integration and Its Applications, Beijing 100871, China
Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Beijing Key Laboratory of Spatial Information Integration and Its Applications, Beijing 100871, China
Hanbo Yang
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Yang Song
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Lisheng Song
Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze–Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241000, China
Lifeng Wu
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yaokui Cui
CORRESPONDING AUTHOR
Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Beijing Key Laboratory of Spatial Information Integration and Its Applications, Beijing 100871, China
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Short summary
Due to shortcomings such as extensive data gaps and limited observation durations in current ground-based latent heat flux (LE) datasets, we developed a novel gap-filling and prolongation framework for ground-based LE observations, establishing a benchmark dataset for global evapotranspiration (ET) estimation from 2000 to 2022 across 64 sites at various timescales. This comprehensive dataset can strongly support ET modeling, water–carbon cycle monitoring, and long-term climate change analysis.
Due to shortcomings such as extensive data gaps and limited observation durations in current...
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