## 3 Results and Discussion
### UHII Modeling  
The previously computed correlations (Table 6) were used to fit linear regression models that estimate Land Surface Temperature (LST) based on five key urban characterization parameters: Wind Corridor (log-transformed), Altitude, NDVI, NDWI, and Sky View Factor (SVF). Due to the heterogeneous influence of these variables across different SCS climate zones, a single global model was deemed inadequate to capture the localized thermal behavior. As a result, four zone-specific linear models were developed--one for each SCS category--to enhance predictive accuracy and contextual sensitivity.  
Each model was calibrated using a 70:30 split of the dataset into training and test sets, ensuring both robustness and generalizability. The resulting equations are presented below (Equations (5)-(8)), corresponding to SCS 1 through SCS 4.  
\[LST =19.72+6.98\cdot log(WC+1)+1.69\cdot Alt-3.60\cdot NDVI-0.09\cdot NDWI-0.66 \cdot SVF \tag{5}\] \[LST =25.38-2.84\cdot log(WC+1)-3.49\cdot Alt-1.60\cdot NDVI+0.83\cdot NDWI-1.32 \cdot SVF\] (6) \[LST =26.64+5.41\cdot log(WC+1)-1.29\cdot Alt-0.16\cdot NDVI+0.14\cdot NDWI-1.34 \cdot SVF\] (7) \[LST =25.77+3.37\cdot log(WC+1)-1.18\cdot Alt+2.09\cdot NDVI+0.69\cdot NDWI-0.83 \cdot SVF \tag{8}\]  
These equations highlight the differential influence of each variable depending on the climatic zone. For instance:  
* In SCS 1, Altitude contributes positively to LST, possibly due to orographic thermal retention in northern inland regions. In contrast, in all other zones, Altitude is negatively associated with LST, consistent with expected cooling effects at higher elevations.
* The coefficient of the NDVI is negative in SCS 1 to 3, reflecting the cooling effect of vegetation, but turns positive in SCS 4, suggesting that in hotter, arid cities, vegetation may retain heat at night through moisture.
* The effect of the NDWI varies, being slightly negative in SCS 1 and more strongly positive in SCS 2 and 4, where surface moisture may be more associated with heat retention.
* The SVF consistently shows a negative coefficient across all zones, reinforcing the role of reduced sky exposure in trapping heat, though its magnitude differs.
* Wind Corridor shows modest effects, but its sign changes between zones, indicating a complex relationship with urban ventilation dynamics and morphology.  
These models not only allow the estimation of LST based on accessible urban data but also offer valuable guidance for climate-sensitive urban planning. For instance, in zones where the NDVI and SVF show strong cooling effects, planning authorities could prioritize the integration of green infrastructure and optimize building morphology to mitigate heat accumulation. Previous studies [47; 48] have demonstrated the effectiveness of such interventions. Moreover, as shown in [49], building typology and layout can significantly influence microclimatic conditions.  
These insights are particularly relevant in the context of Spanish urban planning regulations, such as the Ley del Suelo and the PGOU [50] general urban development plans, which emphasize sustainable development and environmental integration. Incorporating parameters such as the SVF or NDVI into zoning decisions or development guidelines could help translate these findings into actionable policies.