Mapping maximum urban air temperature on hot summer days

Air temperature is an essential component in microclimate and environmental health research, but difficult to map in urban environments because of strong temperature gradients. We introduce a spatial regression approach to map the peak daytime air temperature relative to a reference station on typical hot summer days using Vancouver, Canada as a case study. Three regression models, ordinary least squares regression, support vector machine, and random forest, were all calibrated using Landsat TM/ETM + data and field observations from two sources: Environment Canada and the Weather Underground. Results based on cross-validation indicate that the random forest model produced the lowest prediction errors (RMSE = 2.31 °C). Some weather stations were consistently cooler/hotter than the reference station and were predicted well, while other stations, particularly those close to the ocean, showed greater temperature variability and were predicted with greater errors. A few stations, most of which were from the Weather Underground data set, were very poorly predicted and possibly unrepresentative of air temperature in the area. The random forest model generally produced a sensible map of temperature distribution in the area. The spatial regression approach appears useful for mapping intra-urban air temperature variability and can easily be applied to other cities.