## 5 Conclusions  
This study presents a robust and scalable methodology for modeling nighttime Urban Heat Island (UHI) intensity across different climatic contexts, using a combination of morphological and environmental urban parameters. Leveraging open-access geospatial data and a custom QGIS plugin, the method enables high-resolution characterization of urban thermal behavior through indicators such as Wind Corridor, Altitude, NDVI, NDWI, and Sky View Factor (SVF). Statistical analysis confirmed that all parameters exhibited significant differences across climatic zones and strong correlations with nighttime Land Surface Temperature (LST), although the strength and direction of these relationships varied notably by zone. Accordingly, four zone-specific linear models were developed (based on Summer Climate Severity categories), showing good predictive accuracy (RMSE between 1.40 \({}^{\circ}\)C and 1.59 \({}^{\circ}\)C on average). Their generalizability was validated using four additional cities representative of each zone. These findings highlight the context-dependent role of urban parameters. Vegetation (NDVI) showed a strong cooling effect, especially in cooler zones, whereas Altitude had contrasting impacts depending on the climatic context. The NDWI and SVF also played relevant but variable roles. These insights provide valuable guidance for localized urban planning and UHI mitigation strategies. In terms of contributions, this study offers the following:  
* A transferable and replicable framework for UHI modeling based on standardized, open geospatial inputs. * A demonstration of how climatic zoning enhances model performance and interpretability. * Practical tools for city planners to evaluate microclimatic conditions at the street level. Figure 6: Modeled nighttime Land Surface Temperature (LST) distribution in the city of Jaén (SCS 4): (**a**) estimated LST at 50 m road segment resolution; (**b**) spatially interpolated LST surface for continuous visualization. From a planning perspective, the results can inform decision-making under current Spanish urban legislation. Instruments such as the Ley del Suelo and the PGOU general urban development plans offer a regulatory basis for integrating environmental indicators into zoning and building design. The LST predictors identified (vegetation, morphology, and moisture) can support strategies to mitigate urban heat. Moreover, alignment with climate adaptation frameworks like Sustainable Energy and Climate Action Plans (SECAPs) enhance the practical relevance of this methodology for municipal action. Limitations of the study include the scarcity of validation data due to the limited availability of nighttime LST imagery from the ASTER sensor, and the reliance on climatic classification tailored to the Iberian Peninsula. This restricts applicability to the Mediterranean context, requiring further testing in other regions. Future research could build upon this work as follows:  
* Incorporating additional parameters such as wind direction, surface roughness, and solar radiation to better capture microclimatic interactions. * Exploring the influence of coastal proximity and differentiating between sea types (e.g., Mediterranean vs. Atlantic). * Applying artificial intelligence techniques to automate climate zone classification and improve predictive capacity beyond linear assumptions. * Extending the model temporally to assess seasonal and diurnal UHI variations. * Developing city-specific models to reflect local thermal behavior and improve generalizability. * Enhancing spatial validation through mobile data collection or dense urban climate sensor networks. Overall, the proposed methodology represents a significant step toward a context-aware, data-driven strategy for urban climate management, enabling cities to better understand and mitigate the localized effects of heat accumulation under varying climatic pressures. Conceptualization, G.H.-H. and S.M.-L.; methodology, G.H.-H.; software, G.H.-H. and J.A.M.-J. ; validation, G.H.-H., S.L.