Surface urban heat island intensity (SUHII) is a key indicator of thermal patterns in urban environments, determined by complex interactions between land use, morphology and climate conditions. This study presents and applies a predictive framework to assess SUHII in four Italian cities (Lecce, Bari, Milan and Turin) using satellite data of land surface temperature (LST), urban morphological parameters and advanced statistical modelling. More than 1000 Sentinel-3 images of summer periods (June, July and August) for the years 2022 and 2023 were processed to assess daytime and nighttime SUHII. Multiple linear regression analyses, supported by variance inflation factor (VIF) filtering and residuals diagnostics, revealed that impervious surface fraction (ISF), leaf area index (LAI) and albedo were the most influential predictors. The results showed significant spatial and temporal variability in SUHII, with Milan and Turin showing pronounced heat accumulation, especially at night, while Lecce and Bari showed lower or negative diurnal SUHII. Generalized regression models were developed based on multidimensional scaling (MDS) and SIMPER analysis. Scenario testing in Milan indicated that increasing LAI and surface albedo can reduce SUHII by up to 1 degrees C. The proposed statistical model is scalable, computationally efficient and well-suited for urban-scale applications, making it a practical tool for mapping and analyzing SUHII patterns in diverse city contexts.
A multi-city statistical modelling of surface urban heat island: Application to Italian cities
Esposito, AntonioPrimo
;Pappaccogli, Gianluca
;Bozzeda, Fabio;Buccolieri, Riccardo
2025-01-01
Abstract
Surface urban heat island intensity (SUHII) is a key indicator of thermal patterns in urban environments, determined by complex interactions between land use, morphology and climate conditions. This study presents and applies a predictive framework to assess SUHII in four Italian cities (Lecce, Bari, Milan and Turin) using satellite data of land surface temperature (LST), urban morphological parameters and advanced statistical modelling. More than 1000 Sentinel-3 images of summer periods (June, July and August) for the years 2022 and 2023 were processed to assess daytime and nighttime SUHII. Multiple linear regression analyses, supported by variance inflation factor (VIF) filtering and residuals diagnostics, revealed that impervious surface fraction (ISF), leaf area index (LAI) and albedo were the most influential predictors. The results showed significant spatial and temporal variability in SUHII, with Milan and Turin showing pronounced heat accumulation, especially at night, while Lecce and Bari showed lower or negative diurnal SUHII. Generalized regression models were developed based on multidimensional scaling (MDS) and SIMPER analysis. Scenario testing in Milan indicated that increasing LAI and surface albedo can reduce SUHII by up to 1 degrees C. The proposed statistical model is scalable, computationally efficient and well-suited for urban-scale applications, making it a practical tool for mapping and analyzing SUHII patterns in diverse city contexts.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
