Abstract
The near-surface air temperature is an important factor affecting everyday life and be used in many environmental applications and climate change research. This study aimed to propose a simple methodology for estimating daytime near-surface air temperature (DNSAT) from satellite data. On the one hand, the split-window technique has been used to extract the surface brightness temperature at ground level (SBTG) from thermal infrared data. In this framework, we have used the data of the two channels IR10.8 and IR12 of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first Meteosat Second Generation satellite (MSG1). On the other hand, a regression-based approach has been used for 5210 samples under clear-sky conditions, in which we found that the relationship between DNSAT and SBTG can be considered as a third order polynomial formula. The comparison between the estimated DNSAT in this work and the in-situ DNSAT showed that the suggested methodology can be used for estimating DNSAT with reasonable accuracy, which can be used for climate research and environmental studies.
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