Maximum Nighttime Urban Heat Island (UHI) Intensity Simulation by Integrating Remotely Sensed Data and Meteorological Observations
Maximum Nighttime Urban Heat Island (UHI) Intensity Simulation by Integrating Remotely Sensed Data and Meteorological Observations
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DOI：
10.1109/jstars.2010.2070871
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发表时间：
2011-03
影响因子：
5.5
通讯作者：
Ji Zhou;Yunhao Chen;Jinfei Wang;W. Zhan
中科院分区：
文献类型：
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作者：
Ji Zhou;Yunhao Chen;Jinfei Wang;W. Zhan
城市热岛效应的遥感研究主要是通过热岛空间变化与地表特征的简单相关和回归来实现的。很少有研究从时间角度考察地表热岛指数并将其与气候和气象因素联系起来。以中国北京市为研究区，探讨基于MODIS陆地产品与气象观测相结合的支持向量机（SVM）技术在夜间日最大热岛强度（MNUHII）建模中的适用性和可行性。首先，采用高斯曲面模型计算城市的MNUHIIs。然后，利用归一化植被指数（NDVI）、地表反照率、大气气溶胶光学深度（AOD）、相对湿度（RH）、日照时数（SH）和降水量（PREP）建立支持向量机回归模型，对MNUHII进行预测。结果表明，SVM回归预测MNUHII的精度在0.8 ~ 1.3℃之间；此外，支持向量机回归优于多元线性回归和反向传播人工神经网络。情景分析表明，MNUHII及其影响因子的关系随时间和季节而变化，并受前期降水的影响。RH和AOD是影响MNUHII的最重要因素。此外，以往降水对MNUHII有显著的缓解作用。结果表明，未来对地表热岛效应的研究除考虑地表特征外，还应考虑气候和气象条件。
Remote sensing of the urban heat island (UHI) effect has been conducted largely through simple correlation and regression between the UHI's spatial variations and surface characteristics. Few studies have examined the surface UHI from a temporal perspective and related it with climatic and meteorological factors. By selecting the city of Beijing, China, as the study area, the purpose of this research was to evaluate the applicability and feasibility of the support vector machine (SVM) technique to model the daily maximum nighttime UHI intensity (MNUHII) based on integration of MODIS land products and meteorological observations. First, a Gaussian surface model was used to calculate the city's MNUHIIs. Then, SVM regression models were developed to predict the MNUHII from the following variables: the normalized difference vegetation index (NDVI), surface albedo, atmospheric aerosol optical depth (AOD), relative humidity (RH), sunshine hour (SH), and precipitation (PREP). Results demonstrate that the accuracy of the SVM regression in predicting the MNUHII was around 0.8°C to 1.3°C; in addition, the SVM regression outperformed the multiple linear regression and the artificial neural network with backpropagation. A scenario analysis indicates that the relationships between the MNUHII and its influencing factors varied with time and season and were impacted by previous precipitation. The RH and AOD were the most important factors that influenced the MNUHII. In addition, previous precipitation could significantly mitigate the MNUHII. The results suggest that future investigations on the surface UHI effect should consider the climatic and meteorological conditions in addition to the surface characteristics.