Land surface temperature and spectral indices: A seasonal study of Raipur City

This case study evaluates the seasonal variability of the Pearson's linear correlation coefficient of land surface temperature (LST) with some spectral indices like NDVI, NDWI, NDBI, and NDBaI by using a series of Landsat images for 1991-92, 1995-96, 1999-00, 2004-05, 2009-10, 2014-15, and 2018-19. The results from the average correlation of the entire period of all-season show that the LST builds a positive correlation with NDBI (0.71) and NDBaI (0.52) while it builds a negative correlation with NDVI (-0.44). The LST-NDWI correlation is insignificant. The best correlation is noticed in the post-monsoon period, while the least correlation is observed in the winter season. This study can support the environmental planning to build a sustainable city under a similar environment. (c) 2021 Editorial office of Geodesy and Geodynamics. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study evaluates the seasonal variability of the correlation coefficient between land surface temperature and spectral indices. The results show that land surface temperature has a positive correlation with NDBI and NDBaI, and a negative correlation with NDVI. The strongest correlation is observed during the post-monsoon period, while the weakest correlation is observed in winter.