1. School of Public Administration and Policy, Renmin University of China, Beijing 100872, China; 2. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China; 4. Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 5. School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Abstract：Based on satellite remote sensing data acquired through Moderate Resolution Imaging Spectrometer (MODIS), not only was the annual mean surface urban heat island intensity of 284prefecture-level cities in 2018 figured out, but spatial distribution patterns and spatial agglomeration models of surface urban heat islands in China were analyzed. Combining multivariate remote sensing data, meteorological data and socioeconomic statistics, a geographically weighted regression model was utilized to analyze spatial heterogeneity in main drivers for surface urban heat island intensity during daytime and nighttime. As demonstrated by relevant results, an obvious spatial autocorrelation existed in spatial distribution of China's surface urban heat island intensity. Compared with the traditional global ordinary least squares (OLS) model, interpretation of the drivers was significantly improved according to the geographically weighted regression model. Moreover, determination coefficients for daytime and nighttime increased from 0.651 and 0.189 in the OLS model to 0.876 and 0.659 respectively. In addition, both the residual sum of squares and the Akaike information criterion were calculated to be lower by the geographically weighted regression model. In terms of the drivers, vegetation placed a significantly negative influence on surface urban heat island intensity during the daytime, while structural differences were proved to exist in directions of influence that was applied by other factors along with geographic position changes. On the whole, surface urban heat island intensity was most significantly affected by differences in urban and rural vegetation in daytime; but at night, it was susceptible to socio-economic factors.
Howard L. Climate of London deduced from meteorological observation[J]. Harvey and Darton, 1833,1(3):1-24.
[2]
Lai J, Zhan W, Huang F, et al. Identification of typical diurnal patterns for clear-sky climatology of surface urban heat islands[J]. Remote Sensing of Environment, 2018,217:203-220.
[3]
Schwarz N, Manceur A M. Analyzing the influence of urban forms on surface urban heat islands in Europe[J]. Journal of Urban Planning and Development, 2015,141(3):A4014003.
[4]
Clinton N, Gong P. MODIS detected surface urban heat islands and sinks:Global locations and controls[J]. Remote Sensing of Environment, 2013,134:294-304.
[5]
Zhou D C, Zhao S Q, Zhang L X, et al. The footprint of urban heat island effect in China[J]. Scientific Reports, 2015,5(1):1-11.
[6]
Quan J L, Zhan W F, Chen Y H, et al. Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands[J]. Journal of Geophysical Research-Atmospheres, 2016,121(6):2638-2657.
[7]
Chakraborty T, Lee X. A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability[J]. International Journal of Applied Earth Observation and Geoinformation, 2019,74:269-280.
[8]
赵安周,裴韬,曹森,等.京津冀城市扩张对植被和地表城市热岛的影响[J]. 中国环境科学, 2020,40(4):1825-1833. Zhao A, Pei T, Cao S, et al. Impacts of urbanization on vegetation growth and surface urban heat island intensity in the Beijing-Tianjin-Hebei[J]. China Environmental Science, 2020,40(4):1825-1833.
[9]
胡李发,谢元礼,崔思颖,等.关中平原城市群夏季城市热岛特征及驱动力分析[J]. 中国环境科学, 2021,21(3):45-57. Hu L F, Xie Y L, Cui S Y, et al. Characteristics and driving forces analysis of urban heatisland in Guanzhong Plain urban agglomeration in summer[J]. China Environmental Science, 2021,21(3):45-57.
[10]
雷金睿,陈宗铸,吴庭天,等.1989~2015年海口城市热环境与景观格局的时空演变及其相互关系[J]. 中国环境科学, 2019,39(4):1734-1743. Lei J R, Chen Z Z, WU T T, et al. Spatio-temporal evolution and interrelationship between thermal environment and landscape patterns of Haikou City, 1989~2015[J]. China Environmental Sciencece, 2019, 39(4):1734-1743.
[11]
Peng S, Piao S, Ciais P, et al. Surface urban heat island across 419global big cities[J]. Environmental Science & Technology, 2012, 46(2):696-703.
[12]
Zhou D, Zhao S, Zhang L, et al. Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32major cities[J]. Remote Sensing of Environment, 2016,176:272-281.
[13]
Manoli G, Fatichi S, Schläpfer M, et al. Magnitude of urban heat islands largely explained by climate and population[J]. Nature, 2019,573(7772):55-60.
[14]
Li D, Liao W, Rigden A J, et al. Urban heat island:Aerodynamics or imperviousness?[J]. Science Advances, 2019,5(4):eaau4299.
[15]
Wan Z M. New refinements and validation of the collection-6MODIS land-surface temperature/emissivity product[J]. Remote Sensing of Environment, 2014,140:36-45.
[16]
Wei J, Li Z, Lyapustin A, et al. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China:spatiotemporal variations and policy implications[J]. Remote Sensing of Environment, 2021,252:112136.
[17]
Peng S, Ding Y, Liu W, et al. 1km monthly temperature and precipitation dataset for China from 1901 to 2017[J]. Earth System Science Data, 2019,11(4):1931-1946.
[18]
Li X, Zhou Y, Zhao M, et al. A harmonized global nighttime light dataset 1992~2018[J]. Scientific Data, 2020,7(1):1-9.
[19]
中国统计出版社《中国城市统计年鉴》[M]. 北京:中国统计出版社, 2018. China Statistics Press. China city statistical year book[M]. Beijing:China Statistics Press, 2018.
[20]
Niu L, Tang R, Jiang Y, et al. Spatiotemporal patterns and drivers of the surface urban heat island in 36major cities in China:A comparison of two different methods for delineating rural areas[J]. Sustainability, 2020,12(2):478.
[21]
Lai J, Zhan W, Huang F, et al. Does quality control matter? Surface urban heat island intensity variations estimated by satellite-derived land surface temperature products[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018,139:212-227.
[22]
Moran P A. Notes on continuous stochastic phenomena[J]. Biometrika, 1950,37(1/2):17-23.
[23]
Fotheringham A S, Brunsdon C, Charlton M. Geographically weighted regression:The analysis of spatially varying relationships[M]. John Wiley & Sons, 2003.
[24]
Li Y, Sun Y, Li J, et al. Socioeconomic drivers of urban heat island effect:Empirical evidence from major Chinese cities[J]. Sustainable Cities and Society, 2020,63:102425.
