Agathangelidis I, Cartalis C and Santamouris M
Changing air temperature trends within urban regions deserve careful monitoring as they may reflect modifications in the thermal environment, including the development of an urban heat island. Air temperature fields need to be dense in order the state of the thermal environment to be adequately assessed; yet in most cases, the networks of ground measuring stations are sparse. This paper attempts to define the relationship between downscaled land surface temperature (LST) at resolution 1 km as deduced from MSG-SEVIRI satellite images, and air temperature (Tair) in the urban agglomeration of Athens, for varying land cover types. Polynomial regression and artificial neural networks are used to estimate Tair from LST at a particular time, whereas the LST values for several hours before are also used. In this way, the “memory” of the surface materials is taken into consideration, practically reflecting the thermal inertia associated with land cover. For urban stations, an average R2 of 0.85 and an RMSE of 1.0-1.2˚C was achieved for the majority of the examined time period, an indication of both the capacity of the methodology to define Tair fields in the area under consideration as well as of the fact that LST is the controlling parameter for Tair. The parametric relations as extracted from the above methodology are in principle applicable for a specific station, as they depend on the land cover and the associated land surface characteristics. They may be also used for stations in areas with similar land cover and in the same climatic zone.
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