Please wait a minute...
img

官方微信

遥感技术与应用  2023, Vol. 38 Issue (4): 842-854    DOI: 10.11873/j.issn.1004-0323.2023.4.0842
热红外遥感专栏     
基于温度日内循环模型的全球主要城市地表热岛面积时空格局遥感研究
张先冉1(),占文凤1,2(),缪诗祺1,杜惠琳1,王晨光1,江斯达1
1.南京大学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
Spatiotemporal Patterns of Surface Urban Heat Island Area Across Global Major Cities based on Diurnal Temperature Cycle Model
Xianran ZHANG1(),Wenfeng ZHAN1,2(),Shiqi MIAO1,Huilin DU1,Chenguang WANG1,Sida JIANG1
1.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
 全文: PDF(5638 KB)   HTML
摘要:

随着全球城市化进程的不断推进,地表城市热岛(Surface Urban Heat Island, SUHI)效应日益加剧。厘清SUHI面积时空格局对于全面了解SUHI效应时空变化规律至关重要。基于MODIS (Moderate-resolution Imaging Spectroradiometer)地表温度数据,结合高斯模型和地表温度日内循环模型(Diurnal Temperature Cycle, DTC)计算了全球504个主要城市2000~2019年的SUHI面积比例(SUHI Ratio, IR)——即SUHI面积与城区面积的比值,并在不同时间尺度上(日内逐时、季节对比、年际变化)分析了全球及不同气候区城市的IR变化特征。结果显示:就空间特征而言,全球城市多年IR均值在白天和夜间分别为0.85和0.75,其中寒带气候区城市的多年平均IR(日夜分别为0.94和0.86)显著大于干旱、热带和温带气候区城市。就时间特征而言,日内尺度上全球及各气候区城市IR的变化趋势呈现相同规律,均在日出后先下降后上升,分别于日出后3 h和7 h达到最小和最大值,而后波动下降并渐趋稳定;季节尺度上全球城市夏季IR(日夜分别为0.86和0.76)略高于冬季(日夜分别为0.81和 0.72),干旱、寒带和温带气候区IR季节变化与此一致,热带气候区则呈现相反规律;年际尺度上白天54%的城市年均IR呈增大趋势,夜晚则有62%的城市年均IR呈减小趋势。本研究填补了对全球尺度下SUHI面积时空格局的认识,详细揭示了SUHI面积比例在不同时间尺度和不同气候区之间的变化特征,研究结果有助于加深对SUHI效应的理解。

关键词: 地表城市热岛(SUHI)热红外遥感地表城市热岛面积地表温度时空格局    
Abstract:

In the context of global warming and urbanization, the recent decades have been witnessing intensifying Surface Urban Heat Island (SUHI) effect. Investigations on the spatiotemporal patterns of SUHI area (SUHIA) are crucial for better understanding the SUHI effect. By combining MODIS (Moderate-resolution Imaging Spectroradiometer) land surface temperature data, Gaussian model, and Diurnal Temperature Cycle (DTC) model, here we calculated the ratios of SUHI area to urban area (IR) of 504 global major cities during 2000~2019. We further analyzed the hourly, seasonal, and inter-annual variations in IR across different climate zones. The results show that: (1) In terms of the spatial patterns, the multi-year average daytime and nighttime IR of global major cities are 0.85 and 0.75, respectively, with a significantly larger IR in snow climate zone (0.94 and 0.86 for daytime and nighttime, respectively) than in arid, equatorial and warm climate zones. (2) On the hourly time-scale, the IR patterns are very similar across different climate zones. The IR firstly decreases and then increases after sunrise, reaching the minimum and maximum at 3 hours and 7 hours after sunrise, respectively; and it then decreases in volatility and finally becomes stable. (3) On the seasonal scale, the global mean IR is larger in summer (0.86 and 0.76 for day and night, respectively) than in winter (0.81 and 0.72 for day and night, respectively). The seasonal variations of IR in arid, snow and warm climate zones are similar to those on a global scale, while the situation is reversed in equatorial climate zone. (4) On the inter-annual scale, the annual mean IR shows an increasing trend in 54% of global cities during the daytime, while it shows a decreasing trend in 62% of global cities at night. This study reveals the spatial patterns of SUHI area at multiple time scales, and compares these temporal variations among different climate zones. Our findings contribute to a better understanding of the spatiotemporal patterns of SUHI effect.

Key words: Surface Urban Heat Island(SUHI)    Thermal remote sensing    SUHI area    Land surface temperature    Spatiotemporal patterns
收稿日期: 2022-05-04 出版日期: 2023-09-11
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(42171306)
通讯作者: 占文凤     E-mail: xianranzhang@foxmail.com;zhanwenfeng@nju.edu.cn
作者简介: 张先冉(1996-),男,河南信阳人,硕士研究生,主要从事城市热岛遥感研究。E-mail:xianranzhang@foxmail.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
张先冉
占文凤
缪诗祺
杜惠琳
王晨光
江斯达

引用本文:

张先冉,占文凤,缪诗祺,杜惠琳,王晨光,江斯达. 基于温度日内循环模型的全球主要城市地表热岛面积时空格局遥感研究[J]. 遥感技术与应用, 2023, 38(4): 842-854.

Xianran ZHANG,Wenfeng ZHAN,Shiqi MIAO,Huilin DU,Chenguang WANG,Sida JIANG. Spatiotemporal Patterns of Surface Urban Heat Island Area Across Global Major Cities based on Diurnal Temperature Cycle Model. Remote Sensing Technology and Application, 2023, 38(4): 842-854.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.4.0842        http://www.rsta.ac.cn/CN/Y2023/V38/I4/842

图1  全球504个主要城市空间分布图及其气候区分类
数据类型数据名称空间分辨率时间分辨率所用数据时间

MODIS数据

地表覆盖类型数据(MCD12Q1)500 m逐年2001~2019年

地表温度产品(MOD11A1)

地表温度产品(MYD11A1)

1 000 m

1 000 m

逐日

逐日

2000~2019年

2002~2019年

辅助数据

城市边界数据30 m/2000、2005、2010、2015、2018年
数字高程数据7.5弧秒/2010年
全球人口数据30弧秒(~1 000 m)/2000、2005、2010、2015、2018年
Koppen-Geiger气候分类数据///
表1  本研究所使用的数据信息
图2  高斯模型示意图[36]
图3  全球城市2000~2019年全年、夏季和冬季日夜平均IR分布图
图4  全球及不同气候区城市2000~2019年全年、夏季和冬季日夜平均IR
图5  全球及各个气候区城市2000~2019年全年、夏季和冬季平均日内逐时IR变化趋势
图6  全球及各个气候区2000~2019年间表现为4种IR年际变化趋势类型的城市所占百分比(其中,WD为全球,AD为干旱气候区,ER为热带气候区,SW为寒带气候区,WM为温带气候区)
图7  全球IR显著增大和减小城市的IR年际变化速度与其2000~2019年间平均IR的关系
图8  IR显著增大城市2000~2020年城区人口数量变化趋势
1 WU J G. Urban ecology and sustainability: The state-of-the-science and future directions [J]. Landscape and Urban Planning, 2014, 125: 209-221.
2 SETO K C, FRAGKIAS M, GUNERALP B, et al. A meta-analysis of global urban land expansion [J]. Public Library of Science One, 2011, 6(8): 23777.DOI: 10.1371/journal.pone.0023777
doi: 10.1371/journal.pone.0023777
3 WU X J, WANG G X, YAO R, et al. Investigating surface urban heat islands in south america based on MODIS data from 2003-2016 [J]. Remote Sensing, 2019, 11(10): 1212. DOI: 10.3390/rs11101212
doi: 10.3390/rs11101212
4 SOUCH C, GRIMMOND S. Applied climatology: Urban climate [J]. Progress in Physical Geography: Earth and Environment, 2006, 30(2): 270-279.
5 OKE T R, MILLS G, CHRISTEN A, et al. Urban climates [M]. Cambridge: Cambridge University Press, 2017.
6 PATZ J A, CAMPBELL-LENDRUM D, HOLLOWAY T, et al. Impact of regional climate change on human health [J]. Nature, 2005, 438(7066): 310-317.
7 GRIMM N B, FAETH S H, GOLUBIEWSKI N E, et al. Global change and the ecology of cities [J]. Science, 2008, 319(5864): 756-760.
8 SANTAMOURIS M, CARTALIS C, SYNNEFA A, et al. On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—A review [J]. Energy and Buildings, 2015, 98: 119-124.DOI: 10.1016/j.enbuild.2014.09.052
doi: 10.1016/j.enbuild.2014.09.052
9 MORA C, DOUSSET B, CALDWELL I R, et al. Global risk of deadly heat [J]. Nature Climate Change, 2017, 7(7): 501-506.DOI: 10.1038/nclimate3322
doi: 10.1038/nclimate3322
10 SANTAMOURIS M. Recent progress on urban overheating and heat island research. integrated assessment of the energy, environmental, vulnerability and health impact. synergies with the global climate change [J]. Energy and Buildings, 2020, 207: 109482.DOI: 10.1016/j.enbuild.2019.109482
doi: 10.1016/j.enbuild.2019.109482
11 LI Z L, SI M L, LENG P. A review of remotely sensed surface urban heat islands from the fresh perspective of comparisons among different regions (invited review) [J]. Progress in Electromagnetics Research C, 2020, 102: 31-46.DOI: 10. 2528/PIERC20020403
doi: 10. 2528/PIERC20020403
12 IMHOFF M L, ZHANG P, WOLFE R E, et al. Remote sensing of the urban heat island effect across biomes in the continental USA [J]. Remote Sensing of Environment, 2010, 114(3): 504-513.
13 PENG S S, PIAO S L, CIAIS P, et al. Surface urban heat island across 419 global big cities [J]. Environmental Science & Technology, 2012, 46(2): 696-703.
14 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.DOI: 10.1016/j.jag.2018.09.015
doi: 10.1016/j.jag.2018.09.015
15 LIU X, ZHOU Y Y, YUE W Z, et al. Spatiotemporal patterns of summer urban heat island in Beijing,China using an improved land surface temperature [J]. Journal of Cleaner Production,2020,257:120529. DOI:10.1016/j.jclepro.2020. 120529
doi: 10.1016/j.jclepro.2020. 120529
16 YANG Q Q, HUANG X, TANG Q H. The footprint of urban heat island effect in 302 chinese Cities: Temporal trends and associated factors [J]. Science of the Total Environment, 2019, 655: 652-662.
17 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.
18 STREUTKER D R. A remote sensing study of the urban heat island of houston, texas [J]. International Journal of Remote Sensing, 2002, 23(13): 2595-2608.
19 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
doi: 10.1016/j.rse.2014.05.005
20 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): 3080-3090.
21 PENG J, HU Y X, DONG J Q, et al. Quantifying spatial morphology and connectivity of urban heat islands in a megacity: A radius approach [J]. Science of the Total Environment, 2020, 714: 136792.DOI: 10.1016/j.scitotenv.2020.136792
doi: 10.1016/j.scitotenv.2020.136792
22 QIAO Z, WU C, ZHAO D Q, et al. Determining the boundary and probability of surface urban heat island footprint based on a Logistic model [J]. Remote Sensing, 2019, 11(11): 1368. DOI: 10.3390/rs11111368
doi: 10.3390/rs11111368
23 XIE M M, FU M C. The temporal dynamics of urban heat islands derived from thermal remote sensing data by local indicator of spatial association in Shenzhen, China[C]∥ PIAGENG 2010: Photonics and Imaging for Agricultural Engineering. International Society for Optics and Photonics, 2011, 7752: 775217.
24 HU J, YANG Y B, ZHOU Y Y, et al. Spatial patterns and temporal variations of footprint and intensity of surface urban heat island in 141 China cities[J]. Sustainable Cities and Society, 2022, 77: 103585.
25 SUN Minhuai. Modeling urban heat island footprints using machine learning and surface temperature data[D]. Lanzhou: Lanzhou University, 2020.
25 孙敏淮. 使用机器学习和地表温度数据模拟城市热岛足迹[D]. 兰州: 兰州大学, 2020.
26 LIN Zhongli, XU Hanqiu. Comparative study on the urban heat island effect in “Stove Cities” during the last 20 years [J]. Remote Sensing Technology and Application, 2019, 34(3): 521-530.
26 林中立, 徐涵秋. 近 20 年来新旧“火炉城市”热岛状况对比研究[J]. 遥感技术与应用,2019,34(3):521-530.
27 ZHANG Chungui, PAN Weihua, JI Qing. Dynamic monitoring and spatial-temporal analysis of urban heat island based on MODIS data[J]. Journal of Tropical Meteorology, 2011, 27(3): 396-402.
27 张春桂, 潘卫华, 季青. 基于MODIS数据的城市热岛动态监测及时空变化分析[J]. 热带气象学报, 2011, 27(3): 396-402.
28 ZHANG Shuo, LIU Yonghong, HUANG Hongtao. Research on quantitative evaluations and spatial and temporal distribution of heat islands for the pearl river delta agglomeration [J]. Ecology and Environmental Sciences, 2017, 26(7): 1157-1166.
28 张硕, 刘勇洪, 黄宏涛. 珠三角城市群热岛时空分布及定量评估研究[J]. 生态环境学报, 2017, 26(7): 1157-1166.
29 PAN Zhanghu, HAN Wenchao. Urban expansion and its heat island response in Lanzhou city based on remote sensing analysis [J].Chinese Journal of Ecology, 2011, 30(11): 2597-2603.
29 潘竟虎, 韩文超. 兰州中心城区用地扩展及其热岛响应的遥感分析[J]. 生态学杂志, 2011, 30(11): 2597-2603.
30 SU Yali, ZHANG Yanfang. Spatio-temporal characteristics of urban heat island effect of Xi’an city based on Landsat TM/ETM+ [J]. Bulletin of Soil and Water Conservation, 2011, 31(5): 230-234.
30 苏雅丽, 张艳芳. 基于Landsat TM/ETM+的西安市城市热岛效应时空演变[J]. 水土保持通报, 2011, 31(5): 230-234.
31 QIAO Z, TIAN G J, ZHANG L X, et al. Influences of urban expansion on urban heat island in Beijing during 1989–2010 [J]. Advances in Meteorology, 2014, 2014: 1-11.DOI: 10.1155/2014/187169
doi: 10.1155/2014/187169
32 XU Hanqiu, CHEN Benqing. An image processing technique for the study of urban heat island changes using different seasonal remote sensing data [J]. Remote Sensing Technology and Application, 2003, 18(3): 128-133.
32 徐涵秋, 陈本清. 不同时相的遥感热红外图像在研究城市热岛变化中的处理方法[J]. 遥感技术与应用, 2003, 18(3): 128-133.
33 ZHANG Hao, XU Hanqiu, LI Le, et al. Analysis of the relationship between urban heat island effect and urban expansion in Chengdu, China [J]. Journal of Geo-information Science, 2014, 16(1): 70-78.
33 张好, 徐涵秋, 李乐, 等. 成都市热岛效应与城市空间发展关系分析[J]. 地球信息科学学报, 2014, 16(1): 70-78.
34 WANG Jinshu, LI Guicai, LIU Yujie, et al. Spatial characteristics of land surface temperature in Beijing area[J]. Science of Surveying and Mapping, 2009, 34(6): 218-220.
34 王今殊, 李贵才, 刘玉洁, 等. 北京地区陆表温度空间分布特征[J]. 测绘科学, 2009, 34(6): 218-220.
35 CAO S S, CAI Y L, DU M Y,et al. Seasonal and diurnal surface urban heat islands in China: An investigation of driving factors with Three-dimensional urban morphological parameters[J]. GIScience & Remote Sensing,2022,59(1):1121-1142.
36 KOTTEK M J, GRIESER C,BECK,et al. World map of the Köppen-geiger climate classification updated [J]. Meteorol Z, 2006, 15: 259-263.DOI: 10.1127/0941-2948/2006/0130
doi: 10.1127/0941-2948/2006/0130
37 LAI J M, ZHAN W F, 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. DOI: 10.1016/j.isprsjprs.2018.03.012
doi: 10.1016/j.isprsjprs.2018.03.012
38 ZHOU D C, ZHAO S Q, LIU S G, et al. Surface urban heat island in China's 32 major cities: Spatial patterns and drivers [J]. Remote Sensing of Environment,2014,152:51-61. DOI: 10.1016/j.rse.2014.05.017
doi: 10.1016/j.rse.2014.05.017
39 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
doi: 10.1016/j.rse.2014. 03.037
40 HONG Falu, ZHAN W F, F-M GÖTTSCHE,et al.Comprehensive assessment of four-parameter diurnal land surface temperature cycle models under clear-sky[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2018,142:190-204. DOI:10.1016/j.isprsjprs.2018.06.008
doi: 10.1016/j.isprsjprs.2018.06.008
41 GÖTTSCHE F M, OLESEN F S. Modelling the effect of optical thickness on diurnal cycles of land surface temperature [J]. Remote Sensing of Environment,2009,113(11):2306-2316.
42 STREUTKER D R. Satellite-measured growth of the urban heat island of Houston, Texas [J]. Remote Sensing of Environment, 2003, 85(3): 282-289.
43 FERNANDES R, LEBLANC S G. Parametric (modified least squares) and non-parametric (theil–sen) linear regressions for predicting biophysical parameters in the presence of measurement errors [J]. Remote Sensing of Environment, 2005, 95 (3): 303-316.
44 PLANQUE C, CARRER D, ROUJEAN J L. Analysis of modis Albedo changes over steady woody covers in france during the period of 2001–2013 [J]. Remote Sensing of Environment, 2017, 191: 13-29.DOI: 10.1016/j.rse.2016.12.019
doi: 10.1016/j.rse.2016.12.019
45 MONDAL A, KHARE D, KUNDU S. Spatial and temporal analysis of rainfall and temperature trend of India[J]. Theoretical and Applied Climatology, 2015, 122(1): 143-158.
46 THOMPSON J A, PAULL D J. Assessing spatial and temporal patterns in land surface phenology for the Australian Alps(2000-2014) [J]. Remote Sensing of Environment, 2017, 199: 1-13.DOI: 10.1016/j.rse.2017.06.032
doi: 10.1016/j.rse.2017.06.032
47 ZHOU D C, XIAO J F, BONAFONI S, et al. Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives [J]. Remote Sensing 2018, 11 (1): 48. DOI: 10.3390/rs11010048
doi: 10.3390/rs11010048
48 REN Jiayi. Study on the characteristics and driving factors of global urban heat island daily cycle[D]. Dalian: Liaoling Normal University, 2022.
48 任嘉义. 全球城市热岛日循环特征及驱动因素研究[D]. 大连: 辽宁师范大学, 2022.
49 SANTAMOURIS M. Analyzing the heat island magnitude and characteristics in one hundred asian and Australian cities and regions[J]. Science of the Total Environment, 2015, 512: 582-598. DOI: 10.1016/j.scitotenv.2015.01.060
doi: 10.1016/j.scitotenv.2015.01.060
50 LAZZARINI M, MOLINI A, MARPU P R, et al. Urban climate modifications in hot desert cities: The role of land cover, local climate, and seasonality [J]. Geophysical Research Letters, 2015, 42 (22): 9980-9989.
51 YAO R, WANG L C, HUANG X, et al. Temporal trends of surface urban heat islands and associated determinants in major Chinese cities[J]. Science of the Total Environment, 2017, 609: 742-754. DOI: 10.1016/j.scitotenv.2017.07.217
doi: 10.1016/j.scitotenv.2017.07.217
52 FAN Zhiyu, ZHAN Qingming, LIU Huimin, et al. Spatial-temporal distribution of urban heat island and the heating effect of impervious surface in summer in Wuhan[J]. Journal of Geo-information Science, 2019, 21(2): 226-235.
52 樊智宇, 詹庆明, 刘慧民, 等. 武汉市夏季城市热岛与不透水面增温强度时空分布[J]. 地球信息科学学报, 2019, 21(2): 226-235.
53 WANG J, CHEN Y, LIAO W L, et al. Anthropogenic emissions and urbanization increase risk of compound hot extremes in cities[J].Nature Climate Change,2021,11(12):1084-1089.
54 LIU X P, HUANG Y H, XU X C, et al. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015 [J]. Nature Sustainability, 2020, 3(7): 564-570.
55 ZHOU Yandi. Representation of urban heat island and spatial evolution based on global location grid[D] Beijing: Beijing University of Civil Engineering and Architecture, 2022.周燕迪. 基于全球位置网格的城市热岛及时空演变的表达研究[D].北京: 北京建筑大学, 2022.
56 JIANG Sun, PENG Jian, DONG Jianquan,et al.Conceptual con-notation and quantitative description of surface urban heat island effect[J]. Journal of Geographical Sciences,2022,77(9):2249-2265.
56 江颂,彭建,董建权,等.地表城市热岛效应的概念内涵与定量刻画[J].地理学报,2022,77(9):2249-2265.
[1] 韩鹏,郭桂祯,李鑫磊,刘菁菁. 基于地理探测器的福建省台风灾情影响因素分析[J]. 遥感技术与应用, 2023, 38(2): 487-495.
[2] 晏红波,李浩,卢献健,王佳华. 基于LST-VI特征空间的喀斯特地区土壤水分时空变化研究[J]. 遥感技术与应用, 2022, 37(6): 1460-1471.
[3] 谭磊琪,周亮,李丽,袁博,胡凤宁. 基于梯度视角的城市建筑形态对地表温度的影响[J]. 遥感技术与应用, 2022, 37(6): 1492-1503.
[4] 王永琳,迟永刚,周蕾. 2007~2018年中国陆地植被总初级生产力与日光诱导叶绿素荧光的时空格局及其气候调控[J]. 遥感技术与应用, 2022, 37(3): 692-701.
[5] 沈贝贝,张景,李明,丁蕾,王旭,辛晓平. 内蒙古草原叶面积指数时空格局与水热影响[J]. 遥感技术与应用, 2022, 37(1): 253-261.
[6] 陈喆,董庆,陈建平,赵文博,蒋良文,张广泽,冯涛,王栋,毕晓佳,边民,张权平,孟德利. 基于热红外遥感的川藏铁路昌都—林芝段地热异常区定量预测评价研究[J]. 遥感技术与应用, 2021, 36(6): 1368-1378.
[7] 郭俊钰,戴礼云,梁继,王琼. 典型地表对长沙主城区地表温度的影响分析[J]. 遥感技术与应用, 2021, 36(5): 1209-1222.
[8] 肖尧,马明国,闻建光,于文凭. 复杂地表地表温度反演研究进展[J]. 遥感技术与应用, 2021, 36(1): 33-43.
[9] 杨玉婷,汤家法,边金虎,李爱农,雷光斌,黄平,蒋梓淳. 加尔各答市地表温度与不透水面比例季相相关性研究[J]. 遥感技术与应用, 2021, 36(1): 79-89.
[10] 程雨婷,刘昭华,鹿琳琳,刘士彪,李庆亭. 一带一路沿海超大城市热岛时空特征遥感分析[J]. 遥感技术与应用, 2020, 35(5): 1197-1205.
[11] 刘美,杜国明,于凤荣,匡文慧. 哈尔滨城乡梯度建设用地结构变化及不透水面遥感监测分析[J]. 遥感技术与应用, 2020, 35(5): 1206-1217.
[12] 史姝姝,匡文慧,董斯齐. 21世纪以来西安城乡梯度土地覆盖变化及对城市热岛影响时空特征[J]. 遥感技术与应用, 2020, 35(3): 537-547.
[13] 张新平,乔治,李皓,闫杰,张芳芳,赵栋锋,王得祥,康海斌,杨航,冯扬. 基于Landsat影像和不规则梯形方法遥感反演延安城市森林表层土壤水分[J]. 遥感技术与应用, 2020, 35(1): 120-131.
[14] 田慧慧, 冯 莉, 赵璊璊, 郭 松, 董继伟. 无人机热红外城市地表温度精细特征研究 [J]. 遥感技术与应用, 2019, 34(3): 553-563.
[15] 张博, 吴立宗. 基于Spark的分布式青藏高原MODIS LST插值方法实现研究[J]. 遥感技术与应用, 2018, 33(6): 1178-1185.