Spatiotemporal Variation in Vegetation Precipitation Sensitivity and Influencing Factors in the Yellow River Basin from 2000 to 2020

The Yellow River Basin (YRB) serves as a vital ecological security barrier in northern China, and the stability of its ecosystems and the dynamics of its vegetation productivity have significant implications for national ecological strategies and regional sustainable development. This study utilized net primary productivity (NPP) and precipitation data, employing a linear regression method with a 7 × 7 pixel grid and a 4-year spatiotemporal window to quantify vegetation precipitation sensitivity (VPS) in the YRB from 2000 to 2020. Principal component regression was used to assess the relative contributions of environmental and anthropogenic factors to the interannual variability and long-term trends of VPS. The results indicate that during the 2000–2020 period, 19.27% of the YRB experienced significant changes in VPS, with the area showing a decrease (14.99%) far exceeding that showing an increase (4.28%). The downward trend was most pronounced in the midstream (24.2%). Spatially, VPS exhibited a distinct pattern of negative values in the south and positive values in the north, with 36° N serving as the boundary. Among vegetation types, desert vegetation exhibited the highest VPS, while forests and shrubs exhibited the lowest. GDP and temperature were identified as key factors influencing VPS changes. It should be noted that GDP, as a proxy for human activity, has certain limitations; future studies should incorporate more direct indicators of human activity for further validation. This study clarifies the spatiotemporal characteristics and key drivers of VPS in the YRB, providing a scientific basis for regional ecological conservation.

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Publication Details

Journal
Sustainability
Published
2026-04-27
DOI
https://doi.org/10.3390/su18094301
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal Variation in Vegetation Precipitation Sensitivity and Influencing Factors in the Yellow River Basin from 2000 to 2020

X.C. Li, Jiangbao Xia, Xu Xiaolong, Junxin Zhao et al.
Sustainability
Remote Sensing in Agriculture
article

Spatiotemporal Variation in Vegetation Precipitation Sensitivity and Influencing Factors in the Yellow River Basin from 2000 to 2020

X.C. Li, Jiangbao Xia, Xu Xiaolong, Junxin Zhao, Xiaolu Wang, Youheng Li, Fang Han
article en

Abstract

The Yellow River Basin (YRB) serves as a vital ecological security barrier in northern China, and the stability of its ecosystems and the dynamics of its vegetation productivity have significant implications for national ecological strategies and regional sustainable development. This study utilized net primary productivity (NPP) and precipitation data, employing a linear regression method with a 7 × 7 pixel grid and a 4-year spatiotemporal window to quantify vegetation precipitation sensitivity (VPS) in the YRB from 2000 to 2020. Principal component regression was used to assess the relative contributions of environmental and anthropogenic factors to the interannual variability and long-term trends of VPS. The results indicate that during the 2000–2020 period, 19.27% of the YRB experienced significant changes in VPS, with the area showing a decrease (14.99%) far exceeding that showing an increase (4.28%). The downward trend was most pronounced in the midstream (24.2%). Spatially, VPS exhibited a distinct pattern of negative values in the south and positive values in the north, with 36° N serving as the boundary. Among vegetation types, desert vegetation exhibited the highest VPS, while forests and shrubs exhibited the lowest. GDP and temperature were identified as key factors influencing VPS changes. It should be noted that GDP, as a proxy for human activity, has certain limitations; future studies should incorporate more direct indicators of human activity for further validation. This study clarifies the spatiotemporal characteristics and key drivers of VPS in the YRB, providing a scientific basis for regional ecological conservation.

SustainabilityVol. 18(9)
Shandong University of Technology (CN), Shandong University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 7%
Remote Sensing in Agriculture
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