以南京市为例,利用陆地卫星和夜间灯光卫星的光学遥感信息并结合地理信息,构建了气象观测站局地(周边2 km缓冲区)探测环境表征参数:人为活动影响参数(人口密度和人为热通量)、几何参数(土地利用类型的面积占比和海拔)和物理光学参数(不透水表面面积占比和植被指数),研究了它们对气温观测的影响。结果表明,在相同的天气背景下,当不透水表面面积占比和建成区面积占比减少,植被指数和水体面积占比增加时,气温下降。建立了气象观测站局地的植被指数、不透水表面面积占比和水体面积占比与气温关系的定量回归模型,该模型通过了99.9%的置信度检验。所提方法可用于定量评估某一区域高空间分辨率气温的分布。

Taking Nanjing as an example, using the geo-social data combined with optical remote sensing information from the Landsat8 and nighttime light satellites, we design the characterization parameters of the local detection environment within the 2 km buffer around the observation site, such as the anthropogenic factors (population density and anthropogenic heat flux), geometric parameters (area ratio of land use type and altitude), and physical optical parameters (area ratio of impervious surface and vegetation index), and investigate their impacts on the air temperature (AT) observations. The results indicate that under the same weather background, when the vegetation index and area ratio of water body increase and the area ratio of impervious surface decreases, the AT decreases. The quantitative regression model between the vegetation index, area ratio of impervious surface, and area ratio of water body around the observation site and AT is given, and passes over the 99.9% significance level. The proposed method can be used to quantitatively evaluate the air temperature distribution at some region with high resolution.