• 论文 •

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

ZHU Shanyou1, LIU Yi1, HUA Junwei2, ZHANG Guixin1, ZHOU Yang1, XIANG Jiamin1

1. 1. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. Chuzhou Meteorological Bureau, Chuzhou 239001, China
• 收稿日期:2017-12-22 修回日期:2018-04-03 出版日期:2018-10-27 发布日期:2018-09-04
• 通讯作者: ZHU Shanyou. E-mail:zsyzgx@163.com E-mail:zsyzgx@163.com
• 基金资助:

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

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

ZHU Shanyou1, LIU Yi1, HUA Junwei2, ZHANG Guixin1, ZHOU Yang1, XIANG Jiamin1

1. 1. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. Chuzhou Meteorological Bureau, Chuzhou 239001, China
• Received:2017-12-22 Revised:2018-04-03 Online:2018-10-27 Published:2018-09-04
• Contact: ZHU Shanyou. E-mail:zsyzgx@163.com E-mail:zsyzgx@163.com
• Supported by:

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

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.

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.