长三角城市群表面城市热岛日内逐时变化规律

doi:10.11867/j.issn.1001-8166.2017.02.0187

地球科学进展 ›› 2017, Vol. 32 ›› Issue (2): 187 -198. doi: 10.11867/j.issn.1001-8166.2017.02.0187

方迎波 ¹(

), 占文凤 ^1, ^2, ^*(

), 黄帆 ¹, 高伦 ¹, 全金玲 ³, 邹照旭 ¹

1.南京大学江苏省地理信息技术重点实验室/国际地球系统科学研究所,江苏南京 210023
2.江苏省地理信息资源开发与利用协同创新中心,江苏南京 210023
3.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101

收稿日期:2016-11-20 修回日期:2017-01-06 出版日期:2017-02-20
通讯作者: 占文凤 E-mail:fang.yingbo@foxmail.com;zhanwenfeng@nju.edu.cn

Hourly Variation of Surface Urban Heat Island over the Yangtze River Delta Urban Agglomeration

Yingbo Fang ¹( ), Wenfeng Zhan ^1, ^2, ^*( ), Fan Huang ¹, Lun Gao ¹, Jinling Quan ³, Zhaoxu Zou ¹

1.Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023,China
2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023,China
3.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101,China

Received:2016-11-20 Revised:2017-01-06 Online:2017-02-20 Published:2017-02-20
Contact: Wenfeng Zhan E-mail:fang.yingbo@foxmail.com;zhanwenfeng@nju.edu.cn
About author:
First author:Fang Yingbo (1991-), male, Shangqiu City, Henan Province, Master student. Research areas include remote sensing of urban climate.E-mail:fang.yingbo@foxmail.com
Supported by:
Project supported by the National Natural Science Foundation of China “Local climate zone mapping and its spatio-temporal patterns over urban agglomerations by incorporating surface thermal properties”(No.41671420);The National Science Foundation for Young Scientists of China “Reconstruction of high-resolution soil temperature over shallow layers using a four-time-scale model supported by thermal remote sensing”(No.41301360)

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表面城市热岛多时间尺度变化的遥感研究已取得了阶段性进展。然而,受限于热红外遥感模型与数据的不足,目前典型城市群表面城市热岛的日内逐时变化规律仍不清楚。以MODIS (Moderate-resolution Imaging Spectrometer)遥感影像为主要数据源,结合地表温度日内变化INA08模型,率先模拟并分析了长三角城市群在夏、冬两季的表面城市热岛空间格局与热岛强度的逐时变化特征。结果表明:在夏季,日内所有城市整体而言均呈现热岛效应,但由于城市植被或水体的降温作用,白天至上半夜(08:00~21:00)部分城市存在相对“冷点”,而这些“冷点”在21:00之后基本消失。此季节内城市热岛强度与地表温度日内逐时变化规律相似,两者均在上午迅速上升,并于12:00~14:00到达峰值,而后逐渐下降,并持续至次日日出前。在冬季,多数城市在白天出现了“城市冷岛”,但“冷岛”多在午后至傍晚(14:00~17:00)消失,此后所有城市均恢复为“热岛”。此季节内城市热岛强度与地表温度日内逐时变化规律区别较大,且以农田和森林为背景计算的城市热岛强度的逐时变化趋势存在明显差异,前者在日内到达峰值的时间(约18:00)显著晚于后者(约13:30)。

关键词: 长三角城市群, 表面城市热岛, 逐时变化, 日内尺度, MODIS

Great progress has been made on the remote investigation of Surface Urban Heat Island (SUHI) across multiple time scales. However, limited by the remote sensing models and resolution tradeoff of satellite-derived Land Surface Temperatures (LSTs), currently the hourly regimes of SUHI over typical urban agglomerations in typical seasons remain unclear. Using MODIS imageries as the main data source as well as by incorporating a diurnal temperature cycle model (i.e., INA08), this study, to our knowledge, firstly examined the hourly variations of the spatial pattern and intensity of the SUHIs for the Yangtze River Delta urban agglomeration in both the summer and winter. The results demonstrated that, in the summer, a general trend of ‘heat island’ was observed for every city during a diurnal cycle. ‘Cold spots’ also occur within most of the cities from around 08:00 to 21:00, mostly as a result of the cooling effect of urban vegetation or water body under strong solar insolation. However, these ‘cold spots’ disappear after 21:00. For this season, the hourly variations of the SUHI intensity are similar to those of the LSTs: They both rise rapidly in the morning, reach the maxima at around 12:00 to 14:00, then gradually decrease and continue until the sunrise of the next day. In the winter, surface urban cool islands (SUCIs) were observed for most of the cities, but these SUCIs mostly disappear during the afternoon to the early evening (around 14:00~17: 00), and then all the cities bounce back to exhibit heat islands. Within this season, the hourly variations of the SUHI intensity differ from those of the LST. There also exist large differences of the hourly variations of SUHI intensity between using the rural area and forest as the non-urban background for estimation of the intensity, with the former reaching its maximum (around 18:00) significantly later than the latter reaching its maximum (around 13:30).

Key words: Yangtze River Delta urban agglomeration, Surface urban heat island, Hourly variation, Diurnal scale, Moderate Resolution Imaging Spectroradiometer (MODIS).

中图分类号:

P467

图1 长三角地区土地利用/覆盖图

Fig.1 Land use/land cover maps over the Yangtze River Delta

图2 技术流程图

Fig.2 Flowchart of this study

图3 夏季长三角城市群日内6个典型时刻热岛强度的空间格局

Fig.3 Spatial patterns of the surface urban heat island intensity for the urban agglomeration over the Yangtze River Delta at six typical times of day during the summer

图4 冬季长三角城市群日内6个典型时刻热岛强度的空间格局

Fig.4 Spatial patterns of the surface urban heat island intensity for the urban agglomeration over the Yangtze River Delta at six typical times of day during the winter

图5 夏季长三角城市群城市热岛和地表温度的逐时变化规律
(a)为夏季长三角城市群热岛强度的逐时变化规律(分别以农田和以森林为背景);(b)为模型模拟的城区、森林与农田地表温度日内逐时变化情况; t _1,max与 t _2,max分别为以农田或者森林为背景时热岛强度达到峰值所对应的时间

Fig.5 Hourly variations of the Surface Urban Heat Islands (SUHIs) and Land Surface Temperatures (LSTs) for the urban agglomeration over the Yangtze River Delta during the summer
(a) Hourly SUHI variations using the cropland and forest as the rural background, respectively; (b) Hourly LST variations for the modelled urban, forest, and cropland surfaces; t _1,max, t _2,max are the times when the SUHI intensity reaches the maximum in a diurnal cycle using the cropland and forest as the rural background, respectively

图6 冬季长三角城市群城市热岛和地表温度的逐时变化规律
(a)为冬季长三角城市群热岛强度的逐时变化规律(分别以农田和森林为背景);(b)为模型模拟的城区、森林与农田地表温度日内逐时变化情况; t _1,max与 t _2,max分别为以农田或者森林为背景时热岛强度达到峰值所对应的时间

Fig.6 Hourly variations of the Surface Urban Heat Islands (SUHIs) and Land Surface Temperatures (LSTs) for the urban agglomeration over the Yangtze River Delta during the winter
(a) Hourly SUHI variations using the cropland and forest as the rural background, respectively; (b) Hourly LST variations for the modelled urban, forest, and cropland surfaces; t _1,max and t _2,max are the times when the SUHI intensity reaches the maximum in a diurnal cycle using the cropland and forest as the rural background, respectively

[1]	Oke T R.The energetic basis of the urban heat island[J]. Quarterly Journal of the Royal Meteorological Society, 1982,108(455): 1-24.
[2]	Kato S, Yamaguchi Y.Analysis of urban heat-island effect using ASTER and ETM+ Data: Separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux[J]. Remote Sensing of Environment, 2005, 99(1): 44-54.
[3]	Voogt J A, Oke T R.Thermal remote sensing of urban climates[J]. Remote Sensing of Environment, 2003, 86(3): 370-384.
[4]	Quan J L, Chen Y H, Zhan W F, et al.Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model[J].Remote Sensing of Environment, 2014,149: 33-46,doi:10.1016/j.rse.2014.03.037.
[5]	Gillies R R, Carlson T N.Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models[J]. Journal of Applied Meteorology,1995, 34(4): 745-756.
[6]	Quan Ling, Zhou Ji, Li Mingsong, et al.Island based on time series modeling and its application[J]. Advances in Earth Science,2014, 29(6): 727-733.
	[权凌, 周纪,李明松,等.基于时间序列建模的城市热岛时间尺度成分分离方法与应用[J]. 地球科学进展, 2014, 29(6): 727-733.]
[7]	Zhan W F, Ju W M, Hai S P, et al.Satellite-derived subsurface urban heat island[J]. Environmental Science Technology, 2014, 48(20): 12 134-12 140.
[8]	Zhou Hongmei, Zhou Chenghu, Ge Weiqiang, et al.The surveying on thermal distribution in urban based on GIS and remote sensing[J]. Acta Geographica Sinica, 2001, 56(2): 189-197.
	[周红妹, 周成虎, 葛伟强, 等. 基于遥感和GIS的城市热场分布规律研究[J]. 地理学报, 2001, 56(2): 189-197.]
[9]	Zhang Yong, Yu Tao, Gu Xingfa, et al.Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect[J]. Journal of Remote Sensing, 2006, 10(5):789-797.
	[张勇, 余涛, 顾行发, 等. CBERS-02 IRMSS热红外数据地表温度反演及其在城市热岛效应定量化分析中的应用[J]. 遥感学报, 2006, 10(5):789-797.]
[10]	Stathopoulou M, Cartalis C, Chrysoulakis N.Using midday surface temperature to estimate cooling degree-days from NOAA-AVHRR thermal infrared data: An application for Athens, Greece[J]. Solar Energy, 2006, 80(4): 414-422.
[11]	Wang Jiankai, Wang Kaicun, Wang Pucai.Urban heat (or cool) island over Beijing from MODIS land surface temperature[J]. Journal of Remote Sensing, 2007, 11(3): 330-339.
	[王建凯, 王开存, 王普才. 基于MODIS地表温度产品的北京城市热岛(冷岛)强度分析[J]. 遥感学报, 2007, 11(3): 330-339.]
[12]	Zhou Ji, Chen Yunhao, Li Jing, et al.A volume model for urban heat island based on remote sensing imagery and its application: A case study in Beijing[J]. Journal of Remote Sensing, 2008, 12(5): 734-742.
	[周纪, 陈云浩, 李京, 等. 基于遥感影像的城市热岛容量模型及其应用——以北京地区为例[J]. 遥感学报, 2008, 12(5): 734-742.]
[13]	Keramitsoglou I, Kiranoudis C T, Ceriola G, et al.Identification and analysis of urban surface temperature patterns in Greater Athens, Greece, using MODIS imagery[J]. Remote Sensing of Environment, 2011,115(12): 3 080-3 090.
[14]	Qiao Zhi, Tian Guangjin.Dynamic monitoring of the footprint and capacity for urban heat island in Beijing between 2001 and 2012 based on MODIS[J]. Journal of Remote Sensing, 2001, 19(3): 476-484.
	[乔治, 田光进. 基于MODIS的2001—2012年北京热岛足迹及容量动态监测[J]. 遥感学报, 2015, 19(3): 476-484.]
[15]	Huang F, Zhan W F, Voogt J, et al.Temporal upscaling of surface urban heat island by incorporating an annual temperature cycle model: A tale of two cities[J].Remote Sensing of Environment, 2016, 186: 1-12,doi:10.1016/j.rse.2016.08.009.
[16]	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): 2 638-2 657.
[17]	Tan J G, Zheng Y F, Xu T, et al.The urban heat island and its impact on heat waves and human health in Shanghai[J]. International Journal of Biometeorology, 2009, 54(1): 75-84.
[18]	Rinner C, Hussain M.Toronto’s urban heat island—Exploring the relationship between land use and surface temperature[J]. Remote Sensing, 2011,3(6): 1 251-1 265.
[19]	Kolokotroni M, Ren X, Davies M, et al.London’s urban heat island: Impact on current and future energy consumption in office buildings[J]. Energy and Buildings, 2012,47: 302-311,doi:10.1016/j.enbuild.2011.12.019.
[20]	Ge Weiqiang, Zhou Hongmei, Yang Hequn.Characteristics analysis on heat island effect in Yangtze delta urban agglomerations in recent 8 years by MODIS data[J]. Meteoroiogical Monthly, 2010, 36(1): 77-81.
	[葛伟强, 周红妹, 杨何群. 基于MODIS数据的近8年长三角城市群热岛特征及演变分析[J]. 气象, 2010, 36(1): 77-81.]
[21]	Zhang Jiahua, Hou Yingyu, Li Guicai, et al.Diurnal and seasonal variation of urban heat island over Beijing urban and surrounding region using satellite remote sensing and the analysis of its impact factors[J].Science in China (Series D), 2005,35(Suppl.1): 187-194.
	[张佳华, 候英雨, 李贵才,等. 北京城市及周边热岛日变化及季节特征的卫星遥感研究与影响因子分析[J].中国科学:D辑, 2005,35(增刊1): 187-194.]
[22]	Wang J, Huang B, Fu D, et al.Spatiotemporal variation in surface urban heat island intensity and associated determinants across major chinese cities[J]. Remote Sensing, 2015,7(4): 3 670-3 689.
[23]	Zhao L, Lee X H, Smith R, et al.Strong contributions of local background climate to urban heat islands[J]. Nature, 2014, 511(7 508): 216-219.
[24]	Zhan W F, Chen Y H, Zhou J, et al.Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats[J]. Remote Sensing of Environment, 2013, 131: 119-139,doi:10.1016/j.rse.2012.12.014.
[25]	Peng Zhixing, Zhou Ji, Li Mingsong.Review of methods for simulating land surface temperature at the pixel scale based on ground measurements over heterogeneous surface[J]. Advances in Earth Science, 2016, 31(5): 471-480.
	[彭志兴, 周纪, 李明松. 基于地面观测的异质性下垫面像元尺度地表温度模拟研究进展[J]. 地球科学进展, 2016, 31(5): 471-480.]
[26]	Zakšek K, Oětir K.Downscaling land surface temperature for urban heat island diurnal cycle analysis[J]. Remote Sensing of Environment, 2012, 117: 114-124,doi:19.1016/j.rse.2011.05.027.
[27]	Zhou J, Chen Y H, Zhan X, et al.Modelling the diurnal variations of urban heat islands with multi-source satellite data[J]. International Journal of Remote Sensing, 2013, 34(21): 7 568-7 588.
[28]	Sismanidis P, Keramitsoglou I, Kiranoudis C.A satellite—Based system for continuous monitoring of surface urban heat islands[J]. Urban Climate, 2015, 14: 141-153,doi:10.1016/j.uclim.2015.06.001.
[29]	Inamdar A K, French A, Hook S, et al.Land surface temperature retrieval at high spatial and temporal resolutions over the southwestern United States[J]. Journal of Geophysical Research:Atmospheres, 2008,113(D7): 1 829-1 836.
[30]	Gao J H, Sun Y Z, Liu Q Y, et al.Impact of extreme high temperature on mortality and regional level definition of heat wave: A multi-city study in China[J]. Science of the Total Environment, 2015, 505: 535-544,doi:10.1016/j.scitotenv.2014.10.028.
[31]	Quan J L, Chen Y H, Zhan W F, et al.A hybrid method combining neighborhood information from satellite data with modeled diurnal temperature cycles over consecutive days[J]. Remote Sensing of Environment, 2014, 155: 257-274,doi:10.1016/j.rse.2014.08.034.
[32]	Göttsche F, Olesen F S.Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data[J]. Remote Sensing of Environment, 2001, 76(3): 337-348.
[33]	Bergh V D, Van Wyk M A, Wyk B V. Comparison of data-driven and model-driven approaches to brightness temperature diurnal cycle interpolation[C]∥17th Annual Symposium of the Pattern Recognition Association of South Africa. Parys, South Africa, 2006.
[34]	Jiang G M, Li Z L, Nerry F.Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI[J].Remote Sensing of Environment, 2006, 105(4):326-340.
[35]	Göttsche F, Olesen F S.Modelling the effect of optical thickness on diurnal cycles of land surface temperature[J]. Remote Sensing of Environment, 2009, 113(11): 2 306-2 316,doi:10.1016/j.rse.2009.06.006.
[36]	Zhao Limin, Gu Xingfa, Yu Tao, et al.Modeling diurnal variation of ground thermal radiance images using energy balance model and endmember composing technique[J]. Scienti Sinica Technologica, 2012,42(11): 11.
	[赵利民, 顾行发, 余涛, 等. 基于能量平衡与端元合成技术的地表热辐射场景动态模拟[J]. 中国科学: 技术科学, 2012,42(11): 11.]
[37]	Duan S B, Li Z L, Ning W, et al.Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data[J]. Remote Sensing of Environment, 2012,124: 15-25,doi:10.1016/jrse.2012.04.016.
[38]	Schwarz N, Lautenbach S, Seppelt R.Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures[J]. Remote Sensing of Environment, 2011, 115(12): 3 175-3 186.
[39]	Anniballe R, Bonafoni S, Pichierri M.Spatial and temporal trends of the surface and air heat island over Milan using MODIS data[J]. Remote Sensing of Environment, 2014, 150: 163-171,doi:10.1016/j.rse.2014.05.005.
[40]	Sun H, Chen Y H, Zhan W F.Comparing surface-and canopy-layer urban heat islands over Beijing using MODIS data[J]. International Journal of Remote Sensing, 2015, 36(21): 5 448-5 465.
[41]	Zhu Jiaqi, Tang Xu, Jiang Hao.The air temperature variation and heat island effect in urban area of Shanghai City[J]. Plateau Meteorology, 2006, 25(6): 1 154-1 160.
	[朱家其, 汤绪, 江灏. 上海市城区气温变化及城市热岛[J]. 高原气象, 2006, 25(6): 1 154-1 160.]

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[6]	柴琳娜,屈永华,张立新,梁顺林,王锦地. 基于自回归神经网络的时间序列叶面积指数估算[J]. 地球科学进展, 2009, 24(7): 756-768.
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