Measuring night-time urban heat island. Still a pending issue

The study of urban heat island (UHI) is of great relevance in the context of climate change (CC) and global warming. Cities accumulate heat in urban land covers as well as in built infrastructures, representing true islands of heat in relation to their rural environment, less urbanized. The literature on urban climate has highlighted the singular importance of night UHI phenomenon. It is during the night that the effects of UHI become more apparent, due to the low cooling capacity of urban construction materials and is during nighttime that temperatures can cause higher health risks, leading to the aggravation of negative impacts on people’s health and comfort in extreme events such as heat waves becoming more and more frequent and lasting longer. However, the study of nocturnal UHIs is still poorly developed, due to the structural problems regarding the availability of land surface and air temperature data for night time.

Traditional methods for obtaining nocturnal UHI have been directed either to extrapolation of data from weather stations, or obtaining air temperatures through urban transects. In the first case, the lack of weather stations in urban landscapes makes it extremely difficult to obtain data to extrapolate and propose models at a detailed resolution scale. In the second case, there is a manifest difficulty in obtaining data simultaneously and significantly representative of urban and rural zones. Another used methodology for measuring the nocturnal UHI is remote sensing from MODIS images, but the greatest limitation about this method is the low resolution, therefore it is clear the need for open source databases with better or higher resolution to quantify the night surface temperature.

This paper aims to develop a model for nocturnal UHI analysing several areas of Alta and Baja California as well as in the Mediterranean Coast, using data from the Landsat thermal bands (with an spatial resolution of 30 square meters per pixel) and contrasting Landsat's very limited nighttime images with daytime ones. The contrast allows the construction of “cooling” models of the LST based on geographical characteristics (longitude, latitude, distance to the sea, DTM, slope, orientation, etc.) and urban-spatial parameters (land uses and land covers), which are likely to be extrapolated to different time periods.