Disentangling Climatic and Surface-Physical Drivers of the Urban Heat Island Using Explainable AI Across U.S. Cities

Urban Heat Islands (UHIs) are widely analyzed using Land Surface Temperature (LST), yet most studies remain limited to single cities, rely on a single machine-learning model, analyze LST alone, and use inconsistent Surface Urban Heat Island Intensity (SUHII) definitions, which restrict cross-city comparability and broader generalization. This study introduces an explainable artificial intelligence (XAI) framework implemented in Google Earth Engine (GEE) to analyze census-tract summer surface heat (2018–2024) across eight climatically contrasting U.S. cities. The main novelty is a standardized tract-scale cross-city framework that jointly models LST and SUHII using a consistent SUHII definition, a common physical predictor set, city-held-out nested cross-validation, and SHAP-based interpretation, allowing absolute surface heat to be distinguished from relative within-city heat anomaly; this combination is rarely implemented within a single urban heat study. Multiple machine-learning models were evaluated, with ensemble trees performing best: Extreme Gradient Boosting (XGBoost) best predicted SUHII (R 2 = 0.879; RMSE = 0.213), while Extra Trees best predicted LST (R 2 = 0.908; RMSE = 0.745 °C). SHapley Additive exPlanations (SHAP) indicate that SUHII is driven primarily by impervious surface fraction and surface moisture availability, whereas LST is structured by latitude and mean summer air temperature. Overall, the framework provides interpretable multi-city attribution of urban surface heat drivers with demonstrated cross-city generalization.