Beyond land surface temperature: Identifying areas of daytime thermal discomfort in cities by combining remote sensing and field measurements

Satellite images of land surface temperatures (LST) are commonly used to identify areas within cities most prone to diurnal thermal discomfort, but they may not reflect the experiences of pedestrians. Here, we developed predictive statistical models for Physiological Equivalent Temperature (PET), an indicator of thermal discomfort, with easily accessible spatial predictors. For this, we measured PET (n = 4472) along eight transects (range: 700–5000 m) using a multi-sensor instrument in the urban fabric of Geneva, Switzerland during periods of summer heat. We parametrised generalised additive models (GAM) and linear mixed models (LMM) with six commonly available predictor variables [solar energy, Local Climate Zone (LCZ), albedo, LST, Normalized Difference Moisture Index (NDMI) and canopy cover]. We found that LST, alone, explained