[PERSON] et al. [34] also recorded highest LST outside the LCZ1 with densely packed high rise buildings but in the compact midrise (LCZ2) in the Arabian Gulf. In Delhi, India, [PERSON] et al. [35] observed that LSTs skewed negatively from the mean in LCZs A, C, D, F and 9 with natural pervious surfaces. In Budapest, Hungary, highest LST was recorded in the city center where building density was high and intensities were higher in summer than winter [36]. While inter-LCZ LST differences have been explored, variations within LCZs require attention. Intra-LCZ dynamics are valuable in generating LCZ-specific heat mitigation approaches, especially in built-up areas. Analysis of intra-LCZ thermal conditions can show variability in LST within each category. Understanding variations of LST in each LCZ requires in-depth and detailed analysis of the contribution of urban structure and surface cover properties. Surface temperatures are known to vary with the extent of urbanization/built-up, vegetation health and density, and surface water content [37; 38; 39; 40]. Indices such as the Normalized Difference Vegetation Index (NDVI) [41] provide quantitative measures of urban properties which affect LST distribution between and within LCZs. NDVI signifies vegetation distribution and health, making use of the absorption of red radiation by chlorophyll and maximum reflection of near-infrared (NIR) by leaf cells [41; 42]. It can be used to assess whether an area targeted by remote sensing contains live green vegetation [43]. Vegetation cover such as forests influence surface properties, as well as their monitoring using remote sensing [44; 45]. On the other hand, the varying extent of built-up can be mapped using the Normalized Difference Bareness Index [46]. The Normalized Difference Bareness Index (NDBaI) can be further included to separate barren from built-up and other categories, which cannot be adequately done using NDBI alone. [PERSON] [47] developed the Modified Normalized Difference Water Index (MNDWI), with very high capabilities to map surface water by removing noise from built-up, vegetation, and water backgrounds. The MNDWI also reflects the degree of purity due to its wide dynamic range; [PERSON] et al. [48]. The Urbanization Index (UI) also relates to the extent of built-up and urban fabric in an area [49; 50]. Using Landsat data, these indices are retrieved at 30 m resolution, which can be used for detailed analysis of variations in urban structures and forms with LCZs. The NDVI, NDBI, NDBaI, MNDWI, and UI have been used to quantify the relationships between LST and the respective surface properties they represent. Although at various strength of correlation, most studies have indicated a negative correlation between LST with NDVI and MNDWI, and a positive correlation with NDBaI, NDBI, and UI [37; 42; 51; 52; 53]. [PERSON] et al. [54] found a negative correlation between LST and NDVI, NDWI and NDBaI, and a positive correlation with NDBI in the Pearl River Delta, Guangdong, China. In Guangzhou, China, the correlation of LST with NDBI and NDBaI was found to be positive [48]. In addition to the indices, [PERSON] et al. [48] also included elevation from the Digital Elevation Model (DEM), for which the correlation with LST was negative. According to [PERSON] et al. (2016) [51], a positive correlation between LST and NDBI (R = 0.037) showed the effects of UHI in areas occupied by industrial land use and man-made coatings in the Saranda forest of Jharkhand. Variations in the indices can evidently be used to determine LST drivers at a range of spatial and temporal scales. This will enhance approaches in determining area-specific heat mitigation strategies and recommendations for future development trajectories based on drivers of LST variations. For example, in Delhi, since LCZ3 and LCZ8 were the most heat loaded, [PERSON] et al. [35] recommended that urban expansion in the city should be restricted to LCZ5 and LCZ6.