Understanding the sensitivity of vegetation growth and greenness to vegetation water content change is crucial for elucidating the mechanism of terrestrial ecosystems response to water availability change caused by climate change. Nevertheless, we still have limited knowledge of such aspects in urban in different climatic contexts under the influence of human activities. In this study, we employed Google Earth Engine (GEE), remote sensing satellite imagery, meteorological data, and Vegetation Photosynthesis Model (VPM) to explore the spatiotemporal pattern of vegetation growth and greenness sensitivity to vegetation water content in three megacities (Beijing, Shanghai, and Guangzhou) located in eastern China from 2001 to 2020. We found a significant increase (slope > 0, p < 0.05) in the sensitivity of urban vegetation growth and greenness to vegetation water content (SLSWI). This indicates the increasing dependence of urban vegetation ecosystems on vegetation water resources. Moreover, evident spatial heterogeneity was observed in both SLSWI and the trends of SLSWI, and spatial heterogeneity in SLSWI and the trends of SLSWI was also present among identical vegetation types within the same city. Additionally, both SLSWI of vegetation growth and greenness and the trend of SLSWI showed obvious spatial distribution differences (e.g., standard deviations of trends in SLSWI of open evergreen needle-leaved forest of GPP is 14.36 × 10-2 and standard deviations of trends in SLSWI of open evergreen needle-leaved forest of EVI is 10.16 × 10-2), closely associated with factors such as vegetation type, climatic conditions, and anthropogenic influences.
Keywords: Enhanced vegetation index (EVI); Gross primary production (GPP); Land surface water index (LSWI); Sensitivity; Urban vegetation.
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