*3.3. Individual Land Use Coverage versus LST*

Results from the regression analysis show that there exist high correlations between the percentage coverage of the individual land-use types (%U, %V, and %W) and the mean LSTs (μLSTs) for all window sizes considered in the research. However, in our study area, we detect that smaller window sizes (less than 300 × 300 m) do not properly represent a heterogeneous mixture of the land-use composition. In most cases, one or two components of the land-use composition (%W, %U, and %V) are absent. Therefore, we present the analysis results pertaining to window sizes of larger than 300 × 300 m. On the other hand, the correlations between the percentage coverage of the land use types (%U, %V, and %W) and μLSTs are much stronger for large than for small window sizes.

Figure 8 demonstrates the relationships between the percentage coverage of the land-use types (%U, %V, and %W) and μLSTs in the case of 510 × 510 m window size on the hottest day of 2017. The regression coefficients are found to be the highest for the water coverage (%W) with R2 = 0.70, followed by urban built-up cover (%U), with R<sup>2</sup> = 0.67, and then vegetation cover (%V), with R2 = 0.43. Note that water (%W) and vegetation (%V) coverages display negative correlations with μLST, as they play a known cooling effect on the SUHI intensity, whereas urban built-up coverage (%V) is positively correlated with μLST to enhance the UHI phenomena.

**Figure 8.** Relationships between percentage coverage of land-use types (%U, %V, and %W) and mean LSTs (μLST) on the hottest day of 2017 (4 June): (**a**) Urban coverage (%U); (**b**) Vegetation coverage (%V); and (**c**) Water coverage (%W) versus μLST.
