Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-4005-2025
https://doi.org/10.5194/essd-17-4005-2025
Data description paper
 | 
21 Aug 2025
Data description paper |  | 21 Aug 2025

A vegetation phenology dataset developed by integrating multiple sources using the reliability ensemble averaging method

Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, and Yongshuo H. Fu

Data sets

A vegetation phenology dataset by integrating multiple sources using the Reliability Ensemble Averaging method Yishuo Cui and Yongshuo Fu https://doi.org/10.5281/zenodo.15165681

MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V061 M. Friedl et al. https://doi.org/10.5067/MODIS/MCD12Q2.061

NASA MEaSUREs Vegetation Index and Phenology (VIP) Phenology NDVI Yearly Global 0.05Deg CMG K. Didan and A. Barreto https://doi.org/10.5067/MEaSUREs/VIP/VIPPHEN_NDVI.004

No trends in spring and autumn phenology during the global warming hiatus (http://data.globalecology.unh.edu/data/GIMMS_NDVI3g_Phenology/) X. Wang et al. https://doi.org/10.1038/s41467-019-10235-8

Vegetation phenology data based on GIMMS4g NDVI from 1982 to 2020 S. Chen and Y. Fu https://doi.org/10.5281/zenodo.11136967

MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 M. Friedl and D. Sulla-Menashe https://doi.org/10.5067/MODIS/MCD12Q1.061

Vegetation CollectionPhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000–2018 B. Seyednasrollah et al. https://doi.org/10.3334/ORNLDAAC/1674

Model code and software

Reliability ensemble averaging method Yishuo Cui and Yongshuo Fu https://github.com/PRqA642/REA

REA: A codebase for generating REA phenology datasets (Version 1.0) Y. Cui https://doi.org/10.5281/zenodo.16419015

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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.
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