Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model

Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h Ta observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban Ta at 1-km resolution. This approach leverages a limited number of diurnal Ta observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km Ta into estimating 1-km DTC model parameters, primarily daily maximum and minimum Ta values. This method capitalizes on MODIS LST's ability to estimate daily Ta extremes and requires only four diurnal Ta observations within a daily cycle to estimate monthly mean hourly 1-km Ta. Station-based five-fold crossvalidation yields overall RMSE values consistently below 1.0 degrees C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal Ta observations rivals that obtained using continuous 24-h Ta observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 degrees C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km Ta under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km Ta, which can be readily applied to other cities and holds potential for various applications.