## 3 Results and Discussions
### Comparison of LST Drivers at City and Intra-LCZ Scales  
At the city scale, Aspect and Elevation had very small and negative correlations with LST (correlation coefficients of about \(-0.15\)). This pattern was replicated even at the intra-LCZ scale. The magnitude of LST correlations with NDBI, NDVI, NDBaI, MNDWI, and UI exceeded 0.6 at the city scale. The importance of these factors in explaining LST variations with LCZs was variable. As such, the correlation of LST with UI was high (correlation coefficient > 0.6) only in LCZ5, LCZ6, LCZA, and LCZD. Discussion of Findings  
The LCZs were mapped with high accuracy using Landsat 8 multispectral data using the WUDAPT approach. The high accuracy of LCZ mapping can be attributed to Landsat 8's high quality data [66; 67; 68; 69], as well as Random Forest classifier's superior performance in mapping land surface characteristics [70; 71; 72]. The LULC types and spatial structures were largely distinct, resulting in adequate differentiation between the different LCZ categories. The built-up LCZs were also adequately distinguishable. This is consistent with [PERSON] et al. (2014) [58], who noted that Harare had built-up categories which differed in terms of the density of buildings, the sizes of land units, and the distribution of vegetation within the built-up categories. The LCZ map emphasized the differences in built-up densities between the northern and southern areas with spaced out homes and abundant vegetation in the northern half [57; 58]. In agreement with [PERSON] et al. (2013) [57], historical growth-induced LULC changes have pushed unbuilt areas towards the margins of the city. The compact midrise and compact lowrise occupy less area than the other built-up LCZs. This can be attributed to high cost and intensive use of land, as these areas correspond to the CBD with more vertical developments and closely packed business units. There was an inverse relationship between NDBaI, NDBI, and UI with NDWI and NDVI. The increase in LST with the decrease in NDVI concurs with [PERSON] et al. [73], that changes in vegetation distribution affect climate regulation services offered by vegetation cover areas, such as forests. As an area experiences an increase in urban fabrics, the temperature moderation value of vegetation diminishes, which explains surface warming with the increase in NDBI and UI. The inverse relationship between LST and NDWI explains  
Figure 8: Significance of the correlations of factors with LST per LCZ. the implication of vegetation health on their heat mitigation value. Where vegetation is healthy, the plant cells will be turgid and able to transpire, resulting in heat reduction by latent heat transfer [74; 75; 76]. Additionally, surface wetness and abundance of water of any land surface has a marked temperature reduction effect [40; 77]. Our findings indicated that surface wetness and abundance of healthy vegetation were the most pronounced surface heat mitigation measures. On the other hand, the increase in built-up area and urban fabric were the most significant causes of surface warming. However, the increase in bareness also had some warming effects, while the increase in Aspect and Elevation had some cooling effect on LSTs. Generally, built-up density, surface water and vegetation quality, and abundance were the major controlling factors of LST at the city scale in Harare. Buildings and other urban fabrics absorb a lot of heat during the day, and their effect increases with the density of the buildings and the proportion of urban fabrics, such as pavements and tarmacs in a unit area [78; 79; 80; 81]. A comparison of thermal characteristics between LCZs showed that LSTs were highest in areas corresponding to LCZ2 and LCZ3. Pockets of areas with very high LSTs of the same order as those observed in very densely built LCZs were also observed in other areas, such as in low plants LCZ.