Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Sci Total Environ. 2023 Dec 20;905:167090. doi: 10.1016/j.scitotenv.2023.167090. Epub 2023 Sep 15.
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 (S). This indicates the increasing dependence of urban vegetation ecosystems on vegetation water resources. Moreover, evident spatial heterogeneity was observed in both S and the trends of S, and spatial heterogeneity in S and the trends of S was also present among identical vegetation types within the same city. Additionally, both S of vegetation growth and greenness and the trend of S showed obvious spatial distribution differences (e.g., standard deviations of trends in S of open evergreen needle-leaved forest of GPP is 14.36 × 10 and standard deviations of trends in S of open evergreen needle-leaved forest of EVI is 10.16 × 10), closely associated with factors such as vegetation type, climatic conditions, and anthropogenic influences.
理解植被生长和绿色度对植被含水量变化的敏感性对于阐明陆地生态系统对气候变化引起的水分可利用性变化的响应机制至关重要。然而，在人类活动影响下，在不同气候背景下的城市中，我们对这些方面的了解仍然有限。在这项研究中，我们利用谷歌地球引擎（GEE）、遥感卫星图像、气象数据和植被光合作用模型（VPM），从 2001 年到 2020 年，探讨了位于中国东部的三个特大城市（北京、上海和广州）的植被生长和绿色度对植被含水量的时空变化模式。我们发现，城市植被生长和绿色度对植被含水量的敏感性（S）显著增加（斜率>0，p<0.05）。这表明城市植被生态系统对植被水资源的依赖程度不断增加。此外，S 和 S 的趋势在空间上存在明显的异质性，并且在同一城市的相同植被类型中也存在 S 和 S 的趋势的空间异质性。此外，植被生长和绿色度的 S 以及 S 的趋势都表现出明显的空间分布差异（例如，GPP 的开放常绿针叶林的 S 趋势的标准偏差为 14.36×10，EVI 的开放常绿针叶林的 S 趋势的标准偏差为 10.16×10），这与植被类型、气候条件和人为影响等因素密切相关。
