## 3 Results and Discussion
### Correlation Analysis  
To show a quantitative relationship between all parameters, all datasets and different types of correlations were tested. The results are shown in Table 5. In the case of Wind Corridor, Altitude, NDVI, NDWI and SVF, [PERSON]'s correlation is the statistic applied because the parameters are quantitative. In the case of Buildings on Both Sides, the statistical applied is [PERSON]'s correlation, which is considered appropriate since it is a qualitative parameter. Due to a strong correlation between several parameters, some variables were excluded from the modeling process to avoid redundancy and multicollinearity, as follows:  
Wind Corridor and Buildings on Both Sides showed a substantial correlation (\(r=0.531\)). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Parameter** & **SCS 1 vs. 2** & **SCS 1 vs. 3** & **SCS 1 vs. 4** & **SCS 2 vs. 3** & **SCS 2 vs. 4** & **SCS 3 vs. 4** \\ \hline WC & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Altitude & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline NDVI & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline NDWI & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline BBS & ✓ & ✓ & ✓ & � & ✓ & ✓ \\ \hline SVF & � & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline DGS & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Coastal & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular} \(\mathcal{J}\) (green tick) indicates significant difference (\(p<0.003\)); \(\kappa\) (red cross) indicates no significant difference.  
\end{table} 
Table 4: Holm post hoc test \(p\)-value/FPR. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **WC \({}^{*}\)** & **Alt. \({}^{*}\)** & **NDVI** & **NDWI** & **SVF** & **DIS \({}^{*}\)** & **Coastal** & **BBS \({}^{*}\)** \\ \hline WC \({}^{*}\) & 1 & \( \begin{array}{c}0.001\;\epsilon\\ \left[-0.004,0006\right]\end{array} \) & \(-0.020\;\epsilon\left[-0.025\right]\) & \(-0.001\;\epsilon\left[-0.006\  
* Distance to the Sea and Altitude were highly correlated (r = 0.848). * Coastal and Altitude also exhibited a strong inverse correlation (r = -0.601). Based on these results, the variables Buildings on Both Sides, Distance to the Sea, and Coastal were excluded from the remaining analysis to simplify the final models and avoid collinearity effects. The subsequent step involved assessing the relationship between the remaining parameters and Land Surface Temperature (LST) for each SCS climate zone; Wind Corridor was included under a logarithmic function due to its better fit, as stated in [6]. As shown in Table 6, all retained parameters exhibit statistically significant correlations with LST (p < 0.003; FPR < 5%) across all climate zones, although the magnitude and direction of these correlations vary notably depending on the zone. The correlation analysis between LST and the selected characterization parameters (Log (WC + 1), Altitude, NDVI, NDWI, and SVF) revealed substantial variability in both magnitude and direction across the four SCS climate zones (Table 6). * Wind Corridor (Log (WC + 1)) exhibits weak positive correlations with LST across all zones, with the highest value in SCS 3 (r = 0.133). While the correlations are statistically significant, their low magnitude suggests a limited direct influence on nighttime temperature when considered independently. * Altitude shows the strongest contrast among zones: It is positively correlated with LST in SCS 1 (r = 0.451), yet negatively correlated in the other three zones, particularly in SCS 2 (r = -0.700). This inversion highlights a possible interaction between elevation and broader climatic conditions (e.g., coastal vs. inland environments). * The NDVI has a consistent negative correlation with LST in SCS 1 to 3, most notably in SCS 1 (r = -0.495), suggesting that vegetation plays a strong cooling role in these zones. Interestingly, in SCS 4, the correlation becomes slightly positive (r = 0.124), potentially reflecting the role of irrigated or denser vegetation in hotter regions retaining moisture and heat at night.