Modelling the diurnal variations of urban heat islands with multi-source satellite data

Abstract

Examination of the diurnal variations in surface urban heat islands (UHIs) has been hindered by incompatible spatial and temporal resolutions of satellite data. In this study, a diurnal temperature cycle genetic algorithm (DTC-GA) approach was used to generate the hourly 1 km land-surface temperature (LST) by integrating multi-source satellite data. Diurnal variations of the UHI in ‘ideal’ weather conditions in the city of Beijing were examined. Results show that the DTC-GA approach was applicable for generating the hourly 1 km LSTs. In the summer diurnal cycle, the city experienced a weak UHI effect in the early morning and a significant UHI effect from morning to night. In the diurnal cycles of the other seasons, the city showed transitions between a significant UHI effect and weak UHI or urban heat sink effects. In all diurnal cycles, daytime UHIs varied significantly but night-time UHIs were stable. Heating/cooling rates, surface energy balance, and local land use and land cover contributed to the diurnal variations in UHI. Partial analysis shows that diurnal temperature range had the most significant influence on UHI, while strong negative correlations were found between UHI signature and urban and rural differences in the normalized difference vegetation index, albedo, and normalized difference water index. Different contributions of surface characteristics suggest that various strategies should be used to mitigate the UHI effect in different seasons.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under grants 41101380 and 41071258, by the Chinese State Key Basic Research Project under grant 2013CB733406, by the Postdoctoral Science Foundation of China under grant 2011M500145, and by the Fundamental Research Funds for the Central Universities of China under grant ZYGX2010J087. We thank Dr Yuanyuan Wang from the National Satellite Meteorological Centre, China Meteorological Administration, and Professor Jinfei Wang from the University of Western Ontario for improving this article. We also wish to thank the anonymous referees for their constructive criticism and comments.