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Comparison of the urban heat island intensity quantified by using air temperature and Landsat land surface temperature in Hangzhou,China
Institution:1. College of Urban and Environmental Sciences, Peking University, Laboratory for Earth Surface Processes, Ministry of Education, Beijing 100871, China;2. Key Laboratory for Environmental and Urban Sciences, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518005, China;1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei 430079, China;2. The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China;3. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Abstract:This study compared different measures of urban heat island (UHI) intensity, which were calculated using both air temperature (Tair) at a height of 1.5 m and Landsat land surface temperature (LST) in Hangzhou, China. Two UHI-driven indicators (range and magnitude) and two land-cover-driven indicators (urban-rural and urban-agriculture) were calculated to quantify the UHI intensity based on hourly Tair from five stations and fifteen Landsat 5 LST images. Pearson correlation testing and a moving average times series of the previous 30 days were used to investigate the relationship between UHI intensities calculated by different indicators and data. The results indicate that the land-cover-driven indicators explain UHI better than the UHI-driven indicators, while the calculated values of UHI intensity using Landsat LST and hourly Tair are not comparable. We also investigated the influence of weather conditions on UHI intensity. Generally, Landsat-LST-based UHI performs best on hot sunny days, while Tair-based UHI has a better chance during the nighttime following a dry sunny day. This study suggests that the value of UHI intensity can be influenced by the selected indicators, the data used, the acquisition time and the weather conditions. Thus, these factors should be considered when comparing UHI intensity between different cities or quantifying their influences (e.g., population size, land use and land cover change) on UHI intensity.
Keywords:Urban heat island  Intensity  Indicator  Landsat LST  Hourly air temperature
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