Spatial-temporal variations of surface urban heat island intensity induced by different definitions of rural extents in China.

Surface urban heat island intensity (SUHII) widely acknowledged as the primary indicator has been extensively utilized in previous SUHI spatiotemporal research. However, diverse rural definitions reported in previous studies possibly affect rural temperature and consequently lead uncertainty and incomparability to SUHII dynamics. Despite of previous efforts, there are still some issues in systematic analysis of significance and spatiotemporal variations of SUHII interpretations resulting from these different rural extents. In order to address these issues, this paper quantitvely characterized the significance and spatiotemporal dynamics of RE-induced △SUHII (Rural-extent induced SUHII variations) in 100 major cities across China mainland. Major findings are summarized in the following three aspects. First, considerable SUHII variating with rural buffers extending and majority of △SUHII exceeding 0.5 K especially in the daytime illustrated that different rural definitions can induce significant uncertainty to SUHII studies. Second, RE-induced △SUHII is found to be unevenly distributed across the study area. Moreover, dense distributions of great △SUHII in city agglomerations suggest that city is an inappropriate study object for SUHII urban-cluster research since multi-influences from aggregated urban and rural areas without clear boundaries. Third, RE-induced △SUHII experiences evident seasonal variations especially in the daytime spring and summer, and spoon-shape (peaks in the begins and ends, and bottom in the middle) buffer variations with relatively low correlations among five subdivided buffer rings. Additionally, six potential factors are selected to discuss their possible effects on △SUHII spatiotemporal characters. On the whole, this paper illustrated that diverse rural extents do induce conspicuous uncertainty and incomparability to SUHII interpretations and highlight the necessity to develop uniform rural defining standards in the future SUHII research.

[1]  Benjamin Bechtel,et al.  A New Global Climatology of Annual Land Surface Temperature , 2015, Remote. Sens..

[2]  Yuyu Zhou,et al.  The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. , 2017, The Science of the total environment.

[3]  S. Liang,et al.  Urbanisation and health in China , 2010, The Lancet.

[4]  Bo Huang,et al.  Spatiotemporal Variation in Surface Urban Heat Island Intensity and Associated Determinants across Major Chinese Cities , 2015, Remote. Sens..

[5]  B. Stone Urban and rural temperature trends in proximity to large US cities: 1951–2000 , 2007 .

[6]  H. Akbari,et al.  Local climate change and urban heat island mitigation techniques – the state of the art , 2015 .

[7]  Z. Wan New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product , 2014 .

[8]  W. Emery,et al.  Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology , 1989 .

[9]  Nathaniel A. Brunsell,et al.  A new perspective to assess the urban heat island through remotely sensed atmospheric profiles , 2015 .

[10]  R. Yao,et al.  Interannual variations in surface urban heat island intensity and associated drivers in China. , 2018, Journal of environmental management.

[11]  D. Streutker,et al.  Satellite-measured growth of the urban heat island of Houston, Texas , 2003 .

[12]  Alexandru Dumitrescu,et al.  The July urban heat island of Bucharest as derived from modis images , 2009 .

[13]  A. Arnfield Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island , 2003 .

[14]  Neil Debbage,et al.  The urban heat island effect and city contiguity , 2015, Comput. Environ. Urban Syst..

[15]  Stefania Bonafoni,et al.  Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China. , 2018, The Science of the total environment.

[16]  APPLICATION OF THE SHUTTLE LASER ALTIMETER IN AN ACCURACY ASSESSMENT OF GTOPO 30 , A GLOBAL 1-KILOMETER DIGITAL ELEVATION MODEL , 2004 .

[17]  W. Stefanov,et al.  The Role of Rural Variability in Urban Heat Island Determination for Phoenix, Arizona , 2004 .

[18]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[19]  C. Tucker,et al.  Evidence for a significant urbanization effect on climate in China. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[20]  L. Bounoua,et al.  Remote sensing of the urban heat island effect across biomes in the continental USA , 2010 .

[21]  David Lister,et al.  Urbanization effects in large-scale temperature records, with an emphasis on China , 2008 .

[22]  K. Oleson,et al.  Strong contributions of local background climate to urban heat islands , 2014, Nature.

[23]  J. A. Quintanilha,et al.  DMSP/OLS night‐time light imagery for urban population estimates in the Brazilian Amazon , 2006 .

[24]  Jinfei Wang,et al.  Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands , 2016 .

[25]  Javier Martin-Vide,et al.  On the definition of urban heat island intensity: the “rural” reference , 2015, Front. Earth Sci..

[26]  J. D. Tarpley,et al.  Assessment of urban heat islands: a satellite perspective , 1995 .

[27]  Chandra Venkataraman,et al.  Flip flop of Day-night and Summer-Winter Surface Urban Heat Island Intensity in India , 2017, Scientific Reports.

[28]  G. Sun,et al.  Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China. , 2016, The Science of the total environment.

[29]  Decheng Zhou,et al.  Surface urban heat island in China's 32 major cities: Spatial patterns and drivers , 2014 .

[30]  Nina Schwarz,et al.  Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—An application for the city of Leipzig (Germany) , 2012 .

[31]  Yuyu Zhou,et al.  Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use , 2012 .

[32]  T. Oke,et al.  Thermal remote sensing of urban climates , 2003 .

[33]  N. Grimm,et al.  Global Change and the Ecology of Cities , 2008, Science.

[34]  Z. Wan New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products , 2008 .

[35]  A. Strahler,et al.  The footprint of urban climates on vegetation phenology , 2004 .

[36]  G. Carrus,et al.  Benefits and well-being perceived by people visiting green spaces in periods of heat stress. , 2009 .

[37]  T. Oke The urban energy balance , 1988 .

[38]  W. Tobler A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[39]  Xiaoling Chen,et al.  Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes , 2006 .

[40]  T. Pei,et al.  Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities , 2012 .

[41]  P. Ciais,et al.  Response to Comment on ``Surface Urban Heat Island Across 419 Global Big Cities'' , 2012 .

[42]  X. Lee,et al.  Interaction between urban heat island and urban pollution island during summer in Berlin. , 2018, The Science of the total environment.

[43]  L. Bounoua,et al.  Characterizing urban heat islands of global settlements using MODIS and nighttime lights products , 2010 .

[44]  P. Gong,et al.  MODIS detected surface urban heat islands and sinks: Global locations and controls , 2013 .

[45]  W. Zhan,et al.  Identification of typical diurnal patterns for clear-sky climatology of surface urban heat islands , 2018, Remote Sensing of Environment.

[46]  Shuguang Liu,et al.  Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. , 2016 .

[47]  Nina Schwarz,et al.  Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures , 2011 .

[48]  Jeff Dozier,et al.  A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..

[49]  Zhenhui Sun,et al.  Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China , 2018 .

[50]  T. Chakraborty,et al.  A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[51]  Ying Li,et al.  The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data , 2018, Remote. Sens..

[52]  R. Dickinson,et al.  The Footprint of Urban Areas on Global Climate as Characterized by MODIS , 2005 .

[53]  Qihao Weng,et al.  Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. , 2009 .

[54]  H. Akbari,et al.  Calculating energy-saving potentials of heat-island reduction strategies , 2005 .

[55]  E. Kalnay,et al.  Impact of urbanization and land-use change on climate , 2003, Nature.

[56]  J. Patz,et al.  Impact of regional climate change on human health , 2005, Nature.

[57]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[58]  Osamu Higashi,et al.  A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data , 2009 .

[59]  Christian Berger,et al.  Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives , 2018, Remote. Sens..

[60]  R. Yao,et al.  Less sensitive of urban surface to climate variability than rural in Northern China. , 2018, The Science of the total environment.

[61]  R. DeFries Terrestrial Vegetation in the Coupled Human-Earth System: Contributions of Remote Sensing , 2008 .

[62]  Yuyu Zhou,et al.  Urban mapping using DMSP/OLS stable night-time light: a review , 2017, Remote Sensing of Night-time Light.

[63]  Y. Yasuoka,et al.  Assessment with satellite data of the urban heat island effects in Asian mega cities , 2006 .

[64]  Xin Huang,et al.  The influence of different data and method on estimating the surface urban heat island intensity , 2018, Ecological Indicators.

[65]  T. Kershaw,et al.  Utilising green and bluespace to mitigate urban heat island intensity. , 2017, The Science of the total environment.

[66]  D. Streutker A remote sensing study of the urban heat island of Houston, Texas , 2002 .

[67]  Qiuhong Tang,et al.  The footprint of urban heat island effect in 302 Chinese cities: Temporal trends and associated factors. , 2019, The Science of the total environment.

[68]  R. Yao,et al.  Temporal trends of surface urban heat islands and associated determinants in major Chinese cities. , 2017, The Science of the total environment.

[69]  Weimin Ju,et al.  Does quality control matter? Surface urban heat island intensity variations estimated by satellite-derived land surface temperature products , 2018 .

[70]  Ji Zhou,et al.  Maximum Nighttime Urban Heat Island (UHI) Intensity Simulation by Integrating Remotely Sensed Data and Meteorological Observations , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[71]  C. Elvidge,et al.  Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .

[72]  G. Sun,et al.  The footprint of urban heat island effect in China , 2015, Scientific Reports.