Towards a new methodology to determine nighttime Urban Heat Island

The study of urban heat island (UHI) is of great importance in the context of climate change (CC). 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 most evident due to the low cooling capacity of urban construction materials and it is during nighttime that the accumulated heat and high temperatures can generate greater risks to health, leading to aggravate the negative impacts on people's health and comfort, especially in extreme events such as heat waves.

Traditional methods for obtaining nocturnal UHI have been directed either to extrapolation of data from weather stations. 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.

The low spatial resolution of the air temperature information contrasts with the higher resolution of the thermal data of the land covers supplied by the satellite sensors. There is a high consensus that the temperature of the earth's surface (LST) plays a fundamental role in the generation of UHI, representing a determinant of surface radiation and energy exchange, as well as the control of the heat distribution between surface and atmosphere. However, the study of the nocturnal LST is still poorly developed due to structural problems related to the availability of detailed data on the LST at night. Most of the satellite sensors (Landsat, Aster, ...) allow to obtain daytime thermal images, but in a much more limited way nighttime thermal data. Only MODIS or Sentinel 3 provide abundant thermal night images, but the low resolution of these images (1 km / pixel) does not allow the construction of detailed models of the nocturnal UHI. For these reasons, estimating the nocturnal UHI remains a pending challenge.

This paper aims to develop a new methodology to determine nighttime LST using data from Landsat thermal bands and contrasting Landsat's very limited nighttime images with daytime ones. The contrast between the daytime and nighttime LST allows the construction of “cooling” models of the LST based on geographic characteristics and urban-spatial parameters, which could be extrapolated to different periods of time (during the same season).

However, the estimation of the LST from nighttime Landsat thermal bands is not a trivial question. The most used methodology to determine daytime LST is based on estimating the emissivity of the land from its degree of vegetation (NDVI threshold). But this method shows significant limitations at night. The NDVI overvalues vegetation when considering the canopy of trees. This overestimation may be correct during the day, when the shade of the trees limits the radiation incident on the ground. But it is critical at night.

For this reason, this paper seeks to develop a new methodology to estimate the degree of vegetation and soil moisture, and, based on it, determine the emissivity and, consequently, the nocturnal LST.

The case study is the Metropolitan Area of Barcelona (636 km², 3.3 million inhabitants).