The European Space Agency recently launched the Sentinel mission to perform terrestrial observations in support of tasks such as monitoring forests, detecting land-cover changes, and managing natural disasters. The resolution of these satellite images can be as high as 10 m depending on the bands. In this study, we used the red and near-infrared bands in 10-m resolution from Sentinel-2 images to calculate the Normalized Difference Vegetation Index (NDVI) and estimate of the green vegetation fraction in urban areas within the Pearl River Delta region (PRD). We used vegetation coverage obtained from high-resolution Google satellite images as a reference to validate the vegetation estimates derived from the Sentinel-2 images, and found the correlation between the two to be as high as 0.97. As such, information from the Sentinel-2 imagery can supplement the urban canopy parameters (UCPs) derived from the World Urban Database and Access Portal Tools (WUDAPT) level-0 dataset, which is used in urban meteorological models. The rapid retrieval and open-source nature of the methodology supports high-resolution urban climate modeling studies.

中文翻译：

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基于Sentinel-2 NDVI的珠江三角洲城市植被分数的高分辨率计算，用于城市气候模型参数化

欧洲航天局最近启动了前哨任务，以执行地面观测，以支持诸如监测森林，发现土地覆盖变化和管理自然灾害等任务。这些卫星图像的分辨率取决于波段，可能高达10 m。在这项研究中，我们使用了Sentinel-2图像中10米分辨率的红色和近红外波段来计算归一化植被指数（NDVI）和估算珠江三角洲地区城市地区的绿色植被比例（珠三角）。我们使用从高分辨率Google卫星图像获得的植被覆盖率作为参考，以验证从Sentinel-2图像得出的植被估计，并且发现两者之间的相关性高达0.97。因此，来自Sentinel-2图像的信息可以补充从世界城市数据库和访问门户工具（WUDAPT）的0级数据集得出的城市冠层参数（UCP），该数据集用于城市气象模型。该方法的快速检索和开源性质支持高分辨率的城市气候建模研究。