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

Abstract

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 <2 % of observed variation in PET, whereas the GAM with all the 6 predictor variables had R² = 0.43. LCZ and solar energy explained most of the variability of PET across the city. PET values were lower in the densely built city centre than in the peri-urban environment. LST is poorly correlated with air temperature and PET in urban settings, and thus should not be used alone to predict outdoor thermal discomfort.