Modeling of mean radiant temperature based on comparison of airborne remote sensing data with surface measured data

Assessment of outdoor thermal comfort is becoming increasingly important due to the urban heat island effect, which strongly affects the urban thermal environment. The mean radiant temperature (Tmrt) quantifies the effect of the radiation environment on humans, but it can only be estimated based on influencing parameters and factors. Knowledge of Tmrt is important for quantifying the heat load on human beings, especially during heat waves. This study estimates Tmrt using several methods, which are based on climatic data from a traditional weather station, microscale ground surface measurements, land surface temperature (LST) and light detection and ranging (LIDAR) data measured using airborne devices. Analytical results reveal that the best means of estimating Tmrt combines information about LST and surface elevation information with meteorological data from the closest weather station. The application in this method can eliminate the inconvenience of executing a wide range ground surface measurement, the insufficient resolution of satellite data and the incomplete data of current urban built environments. This method can be used to map a whole city to identify hot spots, and can be contributed to understanding human biometeorological conditions quickly and accurately.