Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-4005-2025
© 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-17-4005-2025
© 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
| 
21 Aug 2025
Data description article |
| 21 Aug 2025
| 21 Aug 2025
A vegetation phenology dataset developed by integrating multiple sources using the reliability ensemble averaging method
Yishuo Cui
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Shouzhi Chen
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Yufeng Gong
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Mingwei Li
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Zitong Jia
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Yuyu Zhou
CORRESPONDING AUTHOR
Department of Geography and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China
Second correspondence author
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Plants and Ecosystems, Department of Biology, University of Antwerp, Antwerp, Belgium
First correspondence author
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Short summary
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycles. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982–2020 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon...
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