ZHU Shanyou, LIU Yi, HUA Junwei, ZHANG Guixin, ZHOU Yang, XIANG Jiamin. Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data[J]. Chinese Geographical Science, 2018, 28(5): 744-757. doi: 10.1007/s11769-018-0989-8
Citation: ZHU Shanyou, LIU Yi, HUA Junwei, ZHANG Guixin, ZHOU Yang, XIANG Jiamin. Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data[J]. Chinese Geographical Science, 2018, 28(5): 744-757. doi: 10.1007/s11769-018-0989-8

Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data

doi: 10.1007/s11769-018-0989-8
Funds:  Under the auspices of the Natural Science Foundation of China (No. 41571418, 41401471), Qing Lan Project, the Priority Academic Program Development of Jiangsu Higher Education Institutions
More Information
  • Corresponding author: ZHU Shanyou. E-mail:zsyzgx@163.com
  • Received Date: 2017-12-22
  • Rev Recd Date: 2018-04-03
  • Publish Date: 2018-10-27
  • Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening (TsHARP) method and a binary linear model are compared to downscale the original daytime FengYun 2F (FY-2F) land surface temperature (LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land (SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error (RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data.
  • [1] Agam N, Kustas W P, Anderson M C et al., 2007. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment, 107(4):545-558. doi: 10.1016/j.rse.2006.10.006
    [2] Anderson M C, Allen R G, Morse A et al., 2012. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment, 122:50-65. doi: 10.1016/j.rse.2011.08.025
    [3] Dousset B, Gourmelon F, 2003. Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS Journal of Photogrammetry and Remote Sensing, 58(1-2):43-54. doi: 10.1016/S0924-2716(03)00016-9
    [4] Dousset B, Gourmelon F, Mauri E, 2007. Application of satellite remote sensing for urban risk analysis:a case study of the 2003 extreme heat wave in Paris. Proceedings of 2007 Urban Remote Sensing Joint Event. Paris, France:IEEE. doi: 10.1109/URS.2007.371849
    [5] Gong P, Liang S, Carlton E J et al., 2012. Urbanisation and health in China. Lancet, 379(9818):843-852. doi: 10.1016/S0140-6736(11)61878-3
    [6] Grimm N B, Faeth S H, Golubiewski N E et al., 2008. Global change and the ecology of cities. Science, 319(5864):756-760. doi: 10.1126/science.1150195
    [7] Jiang Y T, Fu P, Weng Q H, 2015. Downscaling GOES land surface temperature for assessing heat wave health risks. IEEE Geoscience and Remote Sensing Letters, 12(8):1605-1609. doi: 10.1109/LGRS.2015.2414897
    [8] Jiangsu Meteorological Bureau, 2013. Climate impact assessment of Jiangsu province in August 2013. Available at:http://www.jsmb.gov.cn/art/2013/9/4/art_69_12323.html. 2013-09-04. Cited 4 Sep 2013. (in Chinese)
    [9] Kim D W, Deo R C, Lee J S et al., 2017. Mapping heatwave vulnerability in Korea. Natural Hazards, 89(1):35-55. doi: 10.1007/s11069-017-2951-y
    [10] Kovats R S, Hajat S, 2008. Heat stress and public health:a critical review. Annual Review of Public Health, 29:41-55. doi: 10.1146/annurev.publhealth.29.020907.090843
    [11] Kustas W P, Norman J M, Anderson M C et al., 2003. Estimating sub-pixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Remote Sensing of Environment, 85(4):429-440. doi: 10.1016/S0034-4257(03)00036-1
    [12] Liang S L, 2001. Narrowband to broadband conversions of land surface albedo I:algorithms. Remote Sensing of Environment, 76(2):213-238. doi:10.1016/S0034-4257(00)00205-4
    [13] Liu G L, Zhang L C, He B et al., 2015. Temporal changes in extreme high temperature, heat waves and relevant disasters in Nanjing metropolitan region, China. Natural Hazards, 76(2):1415-1430. doi: 10.1007/s11069-014-1556-y
    [14] Liu Yonghong, Quan Wenjun, 2014. Research on high temperature indices of Beijing city and its spatiotemporal pattern based on satellite data. Climatic and Environmental Research, 19(3):332-342. (in Chinese)
    [15] Meehl G A, Tebaldi C, 2004. More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305 (5686):994-997. doi: 10.1126/science.1098704
    [16] Ngie A, Abutaleb K, Ahmed F et al., 2014. Assessment of urban heat island using satellite remotely sensed imagery:a review. South African Geographical Journal, 96(2):198-214. doi: 10.1080/03736245.2014.924864
    [17] Sandholt I, Rasmussen K, Andersen J, 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2-3):213-224. doi:10.1016/S 0034-4257(01)00274-7
    [18] Sobrino J A, Oltra-Carrió R, Sòria G et al., 2012. Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sensing of Environment, 117:50-56. doi: 10.1016/j.rse.2011.04.042
    [19] Stathopoulou M, Cartalis C, 2009. Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. Remote Sensing of Environment, 113(12):2592-2605. doi: 10.1016/j.rse.2009.07.017
    [20] Stisen S, Sandholt I, Nørgaard A et al., 2007. Estimation of diurnal air temperature using MSG SEVIRI data in West Africa. Remote Sensing of Environment, 110(2):262-274. doi: 10.1016/j.rse.2007.02.025
    [21] Sun Y J, Wang J F, Zhang R H et al., 2005. Air temperature retrieval from remote sensing data based on thermodynamics. Theoretical and Applied Climatology, 80(1):37-48. doi:10. 1007/s00704-004-0079-y
    [22] Tomlinson C J, Chapman L, Thornes J E et al., 2012. Derivation of Birmingham's summer surface urban heat island from MODIS satellite images. International Journal of Climatology, 32(2):214-224. doi: 10.1002/joc.2261
    [23] Wan Z M, Dozier J, 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4):892-905. doi: 10.1109/36.508406
    [24] Wan Z M, Zhang Y L, Zhang Q C et al., 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83(1-2):163-180. doi:10.1016/S 0034-4257(02) 00093-7
    [25] Weng Q H, Fu P, 2014. Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 97:78-88. doi: 10.1016/j.isprsjprs.2014.08.009
    [26] Weng Q H, Larson R C, 2005. Satellite remote sensing of urban heat islands:current practice and prospects. In:Jensen R R (eds). Geo-Spatial Technologies in Urban Environments. Berlin Heidelberg:Springer, 91-111. doi: 10.1007/3-540-26676-3_10
    [27] Wilson E H, Sader S A, 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3):385-396. doi: 10.1016/S0034-4257(01)00318-2
    [28] Xu X L, Cai H Y, Qiao Z et al., 2017. Impacts of park landscape structure on thermal environment using QuickBird and Landsat images. Chinese Geographical Science, 27(5):818-826. doi: 10.1007/s11769-017-0910-x
    [29] Zha Y, Gao J, Ni S, 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3):583-594. doi: 10.1080/01431160304987
    [30] Zhang K X, Wang R, Shen C C et al., 2010. Temporal and spatial characteristics of the urban heat island during rapid urbanization in Shanghai, China. Environmental Monitoring and Assessment, 169(1-4):101-112. doi: 10.1007/s10661-009-1154-8
    [31] Zhang W, Jiang J G, Zhu Y B, 2015. Change in urban wetlands and their cold island effects in response to rapid urbanization. Chinese Geographical Science, 25(4):462-471. doi:10.1007/s 11769-015-0764-z
    [32] Zhou W, Peng B, Shi J C et al., 2017. Estimating high resolution daily air temperature based on remote sensing products and climate reanalysis datasets over Glacierized Basins:a case study in the Langtang Valley, Nepal. Remote Sensing, 9(9):959. doi: 10.3390/rs9090959
    [33] Zhu S Y, Guan H D, Millington A C et al., 2013. Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area:a case study in Shanghai, China. International Journal of Remote Sensing, 34(5):1707-1723. doi: 10.1080/01431161.2012.725957
    [34] Zhu S Y, Zhou C X, Zhang G X et al., 2017. Preliminary verification of instantaneous air temperature estimation for clear sky conditions based on SEBAL. Meteorology and Atmospheric Physics, 129(1):71-81. doi: 10.1007/s00703-016-0451-3
    [35] Zoran M, Savastru D, Miclos S et al., 2011. Multisensor satellite remote sensing data for heat waves assessment in metropolitan region. Journal of Optoelectronics and Advanced Materials, 13(9):1159-1166.
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(178) PDF downloads(299) Cited by()

Proportional views
Related

Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data

doi: 10.1007/s11769-018-0989-8
Funds:  Under the auspices of the Natural Science Foundation of China (No. 41571418, 41401471), Qing Lan Project, the Priority Academic Program Development of Jiangsu Higher Education Institutions
    Corresponding author: ZHU Shanyou. E-mail:zsyzgx@163.com

Abstract: Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening (TsHARP) method and a binary linear model are compared to downscale the original daytime FengYun 2F (FY-2F) land surface temperature (LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land (SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error (RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data.

ZHU Shanyou, LIU Yi, HUA Junwei, ZHANG Guixin, ZHOU Yang, XIANG Jiamin. Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data[J]. Chinese Geographical Science, 2018, 28(5): 744-757. doi: 10.1007/s11769-018-0989-8
Citation: ZHU Shanyou, LIU Yi, HUA Junwei, ZHANG Guixin, ZHOU Yang, XIANG Jiamin. Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data[J]. Chinese Geographical Science, 2018, 28(5): 744-757. doi: 10.1007/s11769-018-0989-8
Reference (35)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return