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

作者：Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, Yongshuo H. Fu · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-4005-2025 · 被引用次数：4 · 研究领域：Remote Sensing in Agriculture、Species Distribution and Climate Change、Remote Sensing and LiDAR Applications

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 a comprehensive and accurate estimation of vegetation phenology with a fine spatiotemporal resolution. In this study, we merged four widely used vegetation phenology datasets to generate a new dataset using the reliability ensemble averaging (REA) fusion method. The new dataset has a spatial resolution of 0.05° and covers the period from 1982 to 2020, with geographic coverage extending above 30° N in the Northern Hemisphere. The evaluation using ground-based phenocam data from 280 sites indicated that the accuracy of the newly merged dataset was substantially improved compared to the four original datasets. The start and end of the growing season (SOS and EOS) in the newly merged dataset showed the highest correlation with ground-based phenocam observations, compared to the original datasets (0.84 and 0.71, respectively) and accuracy in terms of the root mean square error (RMSE) between phenocam data and merged datasets (12 and 17 d, respectively). Using the ...