Articles | Volume 18, issue 7
https://doi.org/10.5194/essd-18-5663-2026
© 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-18-5663-2026
© 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
| 
31 Jul 2026
Data description article |
| 31 Jul 2026
| 31 Jul 2026
Extended global terrestrial evapotranspiration and gross primary production dataset from 1982 to near present
Zhenwu Xu
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Dongdong Kong
Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Xuanze Zhang
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
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Short summary
The Penman–Monteith–Leuning Version 2.2 dataset provides remote sensing-based coupled estimates of terrestrial evapotranspiration and gross primary production from 1982 to near present. Calibrated at 208 flux stations and validated against water balances in 152 river basins, this extended record reveals significant increases in global evapotranspiration, vegetation productivity, and water use efficiency, supporting diverse studies in hydrology, ecology, and Earth sciences.
The Penman–Monteith–Leuning Version 2.2 dataset provides remote sensing-based coupled estimates...
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