The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU–LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone.

The urban landscape is considered the most complex and heterogeneous landscape among the different land surface features. It rises the land surface temperature (LST) to a large extent compared to the surrounding rural body. This investigation deals with the seasonal variability between LST and normalized difference water index (NDWI) on the different land surfaces in Raipur, India by using sixty-four Landsat images from 1991–92 to 2018–19. The results show that the post-monsoon season indicates the best correlation (0.42) between LST and NDWI, followed by the monsoon (0.34), pre-monsoon (0.25) and winter (0.04). The water bodies reflect a moderate negative correlation of LST-NDWI in all the four seasons (−0.49 in pre-monsoon, −0.33 in monsoon, −0.31 in post-monsoon and −0.45 in winter). On green vegetation, this LST-NDWI correlation is strongly positive in pre-monsoon (0.67) season, moderate positive in monsoon (0.43) and post-monsoon (0.50) seasons, and weak negative in winter (0.25) season. The built-up area and bare lands build a weak positive correlation of LST-NDWI in all the four seasons (0.24 in pre-monsoon, 0.21 in monsoon, 0.27 in post-monsoon and 0.15 in winter). This study can be beneficial for land use planning and management of any city under a similar physical environment.