Predicting Air Temperature Patterns in Milan Using Crowdsourced Measurements and Earth Observation Data

High-resolution air temperature (AT) data is essential for understanding urban heat dynamics, particularly in urban areas characterised by complex microclimates. However, AT is rarely available in such detail, emphasising the need for its modelling. This study employs a Random Forest regression framework to predict 20 m resolution AT maps across Milan, Italy, for 2022. We focus on seasonal heatwave periods, identified from a long-term climate reanalysis dataset, and multiple diurnal and nocturnal phases that reflect the daily evolution of AT. We predict AT from a high-quality dataset of 97 authoritative and crowdsourced stations, incorporating predictors derived from geospatial and Earth Observation data, including Sentinel-2 indices and urban morphology metrics. Model performance is highest during the late afternoon and nighttime, with an average R2 between 0.33 and 0.37, and an RMSE between 0.7 and 1.4 °C. This indicates modest, yet reasonable agreement with observations, given the challenges of high-resolution AT mapping. Daytime predictions prove more challenging, as noted in previous studies using similar methods. Furthermore, we explore the potential of hyperspectral (HS) data to estimate surface material abundances through spectral unmixing and assess their influence on AT. Results highlight the added value of HS-derived material abundance maps for insights into urban thermal properties and their relationship with AT patterns. The produced maps are useful for identifying intra-urban AT variability during extreme heat conditions and can support numerical model validation and city-scale heat mitigation planning.