Estimating the seasonal relationship between land surface temperature and normalized difference bareness index using Landsat data series

The present study analyzes the seasonal variability of the relationship between the land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India by using sixty-five Landsat images of four seasons (pre-monsoon, monsoon, post-monsoon, and winter) of 1991-1992, 1995-1996, 1999-2000, 2004-2005, 2009-2010, 2014-2015, and 2018-2019. The mono-window algorithm was used to retrieve LST and Pearson's correlation coefficient was used to generate the LST-NDBaI relationship. The post-monsoon season builds the best correlation (0.59) among the four seasons. The water bodies builds a moderate to strong positive correlation (>0.50) in all the four seasons. On green vegetation, this correlation is moderate to strong positive (>0.54) in the three seasons, except the pre-monsoon season. The built-up area and bare land generate a moderate positive correlation (>0.34) in all the four seasons. Among the four seasons, the post-monsoon season builds the best correlation for all LULC types, whereas the pre-monsoon season has the least correlation. This research work is useful for environmental planning of other citieswith similar climatic conditions.

Automated summary

This study analyzes the seasonal variability of the relationship between land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India. The research finds that the correlation is strongest in the post-monsoon season, and water bodies and green vegetation show moderate to strong positive correlations in all seasons. The built-up area and bare land have moderate positive correlations in all seasons.