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<p>Addressing the backdrop of global climatic changes and focusing on how vegetation reacts to these shifts,this article acting a imperative significant.This article investigates the administrative jurisdictions in the eastern of lower of the Yellow River.It selects MODIS NDVI remote sensing data spanning from 2001 to 2021 and employs different analysis methods including simple correlation, partial correlation, and multiple regression to quantitatively assess the response of NDVI during the vegetation growth season to climate variations, analyzing it on a grid by grid basis within the research region. Studies have found that: (1) the NDVI’s responsiveness to temperature and precipitation during the peak growth season exhibited a certain delay; A temperature response lag showed one or two months, while a precipitation response lag showed one or three months.(2) Partial correlation analysis demonstrated that NDVI was negatively correlated with temperature, while NDVI was positively correlated precipitation. Moreover, during the growing season, NDVI across various vegetation types revealed a stronger association with precipitation compared to temperature.(3) The cultivated vegetation, coniferous forest, and swamp vegetation exhibit significant responses to climatic changes.(4) When accounting for time-lag effects, it has increased by 128.98% for the capacity of climate variables to interpret vegetation dynamics compared to simultaneous assessments.</p>

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Identifying time-lag effects of temperature and precipitation on vegetation growth variation in the lower Yellow River of east China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月7日

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  • Xinying Lu,
  • Yan Xiao,
  • Yifang Duan,
  • Nianci Duan

摘要

Addressing the backdrop of global climatic changes and focusing on how vegetation reacts to these shifts,this article acting a imperative significant.This article investigates the administrative jurisdictions in the eastern of lower of the Yellow River.It selects MODIS NDVI remote sensing data spanning from 2001 to 2021 and employs different analysis methods including simple correlation, partial correlation, and multiple regression to quantitatively assess the response of NDVI during the vegetation growth season to climate variations, analyzing it on a grid by grid basis within the research region. Studies have found that: (1) the NDVI’s responsiveness to temperature and precipitation during the peak growth season exhibited a certain delay; A temperature response lag showed one or two months, while a precipitation response lag showed one or three months.(2) Partial correlation analysis demonstrated that NDVI was negatively correlated with temperature, while NDVI was positively correlated precipitation. Moreover, during the growing season, NDVI across various vegetation types revealed a stronger association with precipitation compared to temperature.(3) The cultivated vegetation, coniferous forest, and swamp vegetation exhibit significant responses to climatic changes.(4) When accounting for time-lag effects, it has increased by 128.98% for the capacity of climate variables to interpret vegetation dynamics compared to simultaneous assessments.