Estimation of Urban Air Temperature From a Rural Station Using Remotely Sensed Thermal Infrared Data.

Growing urbanization over the past decades has increased energy consumption and vehicle usage across the world, which in turn has contributed to the phenomenon called urban heat island (UHI) effect. The most important variable to characterize UHI is the urban-rural air temperature differential. This study aims, generally, at deriving a correlation between air temperatures measured at ground weather stations and land surface temperatures estimated using remotely sensed thermal infrared data. Alternatively, we correlate the air temperature directly to the infrared data. Artificial neural network modelling is shown to be superior to other approaches. While weather stations can be costly to install and maintain, satellite images have become more accessible with technological advances and offer greater land coverage. It is therefore relevant to find the most accurate correlation in order to enable future studies to access air temperature values without the need for ground stations. Another important contribution is the assessment of the conditions of portability of the correlation model derived for one geographical location to another nearby location. This will enable to cover a large range of land with inputs from the same 'reference' ground station. The application of this correlation has demonstrated to be valid for high temperatures, which justifies its applicability in the Middle East region, and more so in Abu Dhabi where automobile and air conditioning usage is high, increasing the effects of UHI. [ABSTRACT FROM AUTHOR]