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

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

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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 monitor-ing the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the ther-mal 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 computa-tion 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 co℃ m-pared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78 . The spatial and temp℃ o-ral distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indi-cates 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.

extreme heat eventsland surface temperatureair temperaturedownscaleremote sensing

ZHU Shanyou、LIU Yi、HUA Junwei、ZHANG Guixin、ZHOU Yang、XIANG Jiamin

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School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Chuzhou Meteorological Bureau, Chuzhou 239001, China

Under the auspices of the Natural Science Foundation of ChinaUnder the auspices of the Natural Science Foundation of ChinaQing Lan Project, the Priority Academic Program Development of Jiangsu Higher Education Institutions

4157141841401471

2018

中国地理科学(英文版)
中国科学院长春地理研究所

中国地理科学(英文版)

CSTPCDCSCDSCI
影响因子：0.754
ISSN：1002-0063
年,卷(期)：2018.28(5)
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