Assessment of Surface Urban Heat Islands over Three Megacities in East Asia Using Land Surface Temperature Data Retrieved from COMS

Choi, Youn-Young; Suh, Myoung‐Seok; Park, Ki-Hong

doi:10.3390/rs6065852

Cited by 51 publications

(42 citation statements)

References 29 publications

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“…Feng et al [] found that urban growth has little impact on surface UHIs. Choi et al [] showed that surface UHIs over Beijing had a strong negative correlation with vegetation coverage during daytime, but such a relation was not observed during nighttime. Kuang et al [] studied the relationship between surface UHIs and vegetation coverage over Beijing and argued that the city should upgrade the traditional low‐rise residential areas to high‐rise residential areas to reduce UHI intensity.…”

Section: Introductionmentioning

confidence: 99%

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

et al. 2017

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This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (Ta) collected at 65 out of 262 stations in Beijing and land surface temperature (Ts) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013–2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime Ts in winter and a lower nighttime Ta in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear‐sky conditions, which was not apparent under cloudy‐sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%–54% of the nighttime atmospheric UHI variability and 31%–38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.

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Section: Introductionmentioning

confidence: 99%

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

et al. 2017

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“…In addition, for Tokyo, a significant correlation was observed for spring and autumn UHI intensity. As developed megacities of East Asia, several previous studies identified the UHI phenomenon in Seoul and Tokyo [14,43,44]. Specifically, in Tokyo, widespread urban warming, known as the extended heat island, appeared in summer because of enhanced surface heating over a large urban area and reduced sea-breeze penetration caused by the increased surface convergence ( Table 2) [45].…”

Section: Correlation Analysismentioning

confidence: 99%

Trend Analysis of Urban Heat Island Intensity According to Urban Area Change in Asian Mega Cities

et al. 2019

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Urban heat island (UHI) is a phenomenon that occurs in cities worldwide. Therefore, there is an increasing need for studies on the changes in UHI intensity and long-term trends based on macroscopic characteristics related to urbanization. In this study, changes in seasonal UHI intensity based on urban area were analyzed for eight Asian mega cities from 1992-2012. The results indicate that the change in pattern of UHI intensity varies for different cities and seasons. UHI intensity increased as the urban area size increased. Furthermore, the dependency of UHI intensity on the economic situation was also demonstrated. With respect to the seasons, significantly increasing trends appeared during the summer. Moreover, depending on urban characteristics such as geography and climate, increasing trends appeared during other seasons. Population was also found to affect UHI intensity by generating anthropogenic heat; however, its effect as an individual factor appeared to be insignificant. This is a macroscale study that analyzes the effect of urban area size on UHI intensity. Future studies on urbanization factors and levels influencing the UHI intensity using higher resolution materials are required Sustainability 2020, 12, 112 2 of 11 characteristics or fundamental differences among them. The number of systematic empirical studies of the latter type has increased because of the accessibility of remote sensing with global coverage [9], and our study falls into this category. Several studies have indicated that continuing urbanization progressively increases the UHI intensity, and several others have demonstrated that the urban impact could have an effect on recent warming trends [10][11][12]. Hence, comparative studies on UHI changes for cities with different climatic and socio-economic situations, and on urban characteristics, are essential for researchers and decision-makers [6,[13][14][15][16].Several existing studies that focus on UHI at a macroscopic level were examined. Karl et al.[17] classified urbanization in cities of the United States based on population, and performed a time-series analysis for each season. The UHI intensity was defined as the temperature differences between urban and suburban areas with regard to the degree of urbanization. This was done by using the monthly temperature data from 1901-1984 obtained from 1219 observation stations of the historical climatology network. Although differences in urbanization and seasonality were confirmed in their study, there were limitations in the variability based on the location. Moreover, there was a difference in the degree of data construction between the eastern and western regions.Jones et al. [13] attempted to identify the effect of temperature increase due to urbanization by using the rural-station temperature data for three regions, namely the western Soviet Union, eastern Australia, and eastern China. However, there was a limitation in the study due to the varying numbers of observation stations and analysis periods; furthermore, significant trends...

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“…(Moran and Jackson, 1991;Kustas and Norman, 1996;Benali et al, 2012;Choi et al, 2014). 다양한 응용분야에서 지표면온도의 중 요성이 부각됨에 따라 시·공간적으로 높은 해상도로 관측된 정량적인 지표면온도의 필요성이 증대되고 있 다 (Wan and Dozier, 1996;Li et al, 2013).…”

Section: 서 론unclassified

Improvement of COMS land surface temperature retrieval algorithm by considering diurnal variation of air temperature

Choi¹,

Suh²

2016

Korean Journal of Remote Sensing

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: Land Surface Temperature (LST) has been operationally retrieved from the Communication, Ocean, and Meteorological Satellite (COMS) data by the spilt-window method (CSW_v2.0) developed by Cho et al. (2015). Although the CSW_v2.0 retrieved the LST with a reasonable quality compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data, it showed a relatively poor performance for the strong inversion and lapse rate conditions. To solve this problem, the LST retrieval algorithm (CSW_v2.0) was updated using the simulation results of radiative transfer model (MODTRAN 4.0) by considering the diurnal variations of air temperature. In general, the upgraded version, CSW_v3.0 showed a similar correlation coefficient between the prescribed LSTs and retrieved LSTs (0.99), the relatively smaller bias (from -0.03 K to-0.012 K) and the Root Mean Square Error (RMSE) (from 1.39 K to 1.138 K). Particularly, CSW_v3.0 improved the systematic problems of CSW_v2.0 that were encountered when temperature differences between LST and air temperature are very large and/or small (inversion layers and superadiabatic lapse rates), and when the brightness temperature differences and surface emissivity differences were large. The bias and RMSE of CSW_v2.0 were reduced by 10-30% in CSW_v3.0. The indirect validation results using the MODIS LST data showed that CSW_3.0 improved the retrieval accuracy of LST in terms of bias (from -0.629 K to -0.049 K) and RMSE (from 2.537 K to 2.502 K) compared to the CSW_v2.0.

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Assessment of Surface Urban Heat Islands over Three Megacities in East Asia Using Land Surface Temperature Data Retrieved from COMS

Cited by 51 publications

References 29 publications

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

Trend Analysis of Urban Heat Island Intensity According to Urban Area Change in Asian Mega Cities

Improvement of COMS land surface temperature retrieval algorithm by considering diurnal variation of air temperature

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