Preprints
https://doi.org/10.5194/essd-2024-225
https://doi.org/10.5194/essd-2024-225
15 Aug 2024
 | 15 Aug 2024
Status: this preprint is currently under review for the journal ESSD.

A vegetation phenology dataset 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

Abstract. Global change has substantially shifted vegetation phenology, with important implications in the carbon and water cycles of terrestrial ecosystems. Various vegetation phenology datasets have been developed using remote sensing data; however, the significant uncertainties in these datasets limit our understanding of ecosystem dynamics in terms of phenology. It is therefore crucial to generate a reliable large-scale vegetation phenology dataset, by fusing various existing vegetation phenology datasets, to provide comprehensive and accurate estimation of vegetation phenology with fine spatiotemporal resolution. In this study, we merged four widely used vegetation phenology datasets to generate a new dataset using the Reliability Ensemble Averaging fusion method. The spatial resolution of the new dataset is 0.05° and its temporal scale spans 1982–2022. The evaluation using the ground-based PhenoCam dataset from 280 sites indicated that the accuracy of the newly merged dataset was improved substantially. The start of growing season and the end of growing season in the newly merged dataset had the largest correlation (0.84 and 0.71, respectively) and accuracy in terms of the root mean square error (12 and 17 d, respectively). Using the new dataset, we found that the start of growing season exhibits a significant (p < 0.01) advanced trend with a rate of approximately 0.24 d yr−1, and that the end of growing season exhibits a significant (p < 0.01) delayed trend with a rate of 0.16 d yr−1 over the period 1982–2022. This dataset offers a unique and novel source of vegetation phenology data for global ecology studies.

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-225', Anonymous Referee #1, 13 Sep 2024
  • RC2: 'Comment on essd-2024-225', Anonymous Referee #2, 17 Oct 2024
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.11127281

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

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Short summary
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycle. 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 to 2022 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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