Modeling Urban Microclimates for High-Resolution Prediction of Land Surface Temperature Using Statistical Models and Surface Characteristics

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R2 = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R2 = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R2 ranging from 0.79 to 0.95.