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. ARTICLE HISTORY

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The present study evaluates the seasonal variation of estimated error in downscaled land surface temperatures (LST) over a heterogeneous urban land. Thermal sharpening (TsHARP) downscaling algorithm has been used with a separate combination of four selected remote sensing indices. This study assesses the capability of TsHARP technique over mixed land use/land covers (LULC) by analyzing the correlation between LST and remote sensing indices, namely, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized multi-band drought index (NMDI) and by determining the root mean square error (RMSE) and mean error (ME) produced by downscaled LST. Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images have been used for pre-monsoon, monsoon, post-monsoon, and winter seasons in 2014 covering the whole Raipur City, India. The RMSE of the downscaled LST decreases from 120 to 480 m spatial resolution in all the four seasons. It is concluded that NDBI is the most effective LULC index having the least error produced in TsHARP downscaling technique, irrespective of any season. Post-monsoon season reflects the most successful result followed by monsoon season. Even in the monsoon season of high vegetation coverage, NDBI presents a lower range of downscaled error compared to NDVI. This indicates better performance of NDBI in detecting the spatial and temporal distribution of mixed urban land.

The present study focuses on the estimating land surface temperature (LST) of Raipur City in India emphasizing the urban heat island (UHI) and non-UHI inside the city boundary and their relationship with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multi-band drought index. The entire study has been performed on four multi-date Landsat 8 Operational Land Imager and Thermal Infrared Sensor images taken from four different seasons; pre-monsoon, monsoon, post-monsoon, and winter in the same year. The UHI has mainly been developed along the northern and southern periphery of the city. The range of LST in the common UHI for four different seasons varies between 25.72°C and 35.69°C. The results present the strongest correlation between LST and the land use/land cover indices in monsoon and post-monsoon images while winter and pre-monsoon images show a comparatively weak correlation.

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Land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI) are significantly considered in environmental study. The aim of this study was to retrieve the LST of Raipur City of tropical India and to explore its seasonal relationships with NDVI. Landsat images of four specific seasons for three particular years with fourteen years time interval were analyzed. The result showed a gradual rising (3.63 °C during 1991-2004 and 1.54 °C during 2004-2018) of LST during the whole period of study. The mean LST value of three particular years was the lowest (27.21 °C) on green vegetation and the highest (29.81 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (− 0.63) and the least (− 0.17) correlation were noticed in the postmonsoon and winter seasons, respectively, whereas a moderate (− 0.45) correlation were found both in the monsoon and pre-monsoon seasons. This LST-NDVI correlation was strong negative (− 0.51) on vegetation surface, moderate positive on water bodies (0.45), and weak positive on the built-up area and bare land (0.14). In summary, the LST is greatly controlled by surface characteristics. This study can be used as a reference for land use and environmental planning in a tropical city.