A Machine Learning Algorithm to Convert Geostationary Satellite LST to Air Temperature Using In Situ Measurements: A Case Study in Rome and High-Resolution Spatio-Temporal UHI Analysis

Air temperature (Ta) measurements are crucial for characterizing phenomena like the urban heat island (UHI), which can create critical conditions in cities during summer. This study aims to develop a machine learning-based model, namely gradient boosting, to estimate Ta from geostationary satellite LST data and to apply these estimates to investigate UHI dynamics. Using Rome, Italy, as a case study, the model was trained with Ta data from 15 weather stations, taking multi-temporal LST values (instantaneous and lagged up to 4 h) and additional predictors. The model achieved an overall RMSE of 0.9 °C. The resulting Ta fields, with a 3 km spatial and hourly temporal resolution, enabled a detailed analysis of UHI intensity and dynamics during the summers of 2019–2020, significantly enhancing the spatial and temporal detail compared to previous studies based solely on in situ data. The results also revealed a slightly higher nocturnal UHI intensity than previously reported, attributed to the inclusion of rural areas with near-zero imperviousness, thanks to the complete mapping of Ta across the domain now accessible.