Spatiotemporal Variations and Seasonal Climatic Driving Factors of Stable Vegetation Phenology Across China over the Past Two Decades

作者：Xingqiang Lü, Xiaobo Wu, Yisen Gao, Yufei Cai, Li Yang, Yijun Xiong, Qingchun Yang, Jiaxin Liu, Yijin Li, Zhiyong Deng, Qīng Wáng, Bing Li · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17203467 · 被引用次数：4 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Land Use and Ecosystem Services

Vegetation phenology (VP) is a crucial biological indicator for monitoring terrestrial ecosystems and global climate change. However, VP monitoring using traditional remote sensing vegetation indices has significant limitations in precise analysis. Furthermore, most studies have overlooked the distinction between stable and short-term VP in relation to climate change and have failed to clearly identify the seasonal variation in the impact of climatic factors on stable VP (SVP). This study compared the accuracy of solar-induced chlorophyll fluorescence (SIF) and three traditional vegetation indices (e.g., Normalized Difference Vegetation Index) for estimating SVP in China, using ground-based data for validation. Additionally, this study employs Sen’s slope, the Mann–Kendall (MK) test, and the Hurst index to reveal the spatiotemporal evolution of the Start of Season (SOS), End of Season (EOS), and Length of Growing Season (LOS) over the past two decades. Partial correlation analysis and random forest importance evaluation are used to accurately identify the key climatic drivers of SVP across different climate zones and to assess the seasonal contributions of climate to SVP. The results indicate that (1) phenological metrics derived from SIF data showed the strongest correlation coefficients with ground-based observations, with all correlation coefficients (R) exceeding 0.69 and an average of 0.75. (2) The spatial distribution of SVP in China has revealed three primary spatial p...