Journal
GEOCARTO INTERNATIONAL
Volume 37, Issue 15, Pages 4292-4311Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2021.1886339
Keywords
LST; MNDWI; NDBaI; NDBI; NDVI; NDWI; NMDI
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The study found significant correlations between land surface temperature and remote sensing indices in Raipur City, with the relationship between LST and NDBI being the most consistent.
The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear correlation coefficient (r), the study finds that LST builds a strong positive correlation (r = 0.65) with NDBI, a moderate positive correlation (r = 0.30) with NDBaI, a weak positive correlation with NDWI (r = 0.19), a strong negative relation with NMDI (r = -0.54), and a moderate negative correlation (r = -0.38) with MNDWI and NDVI. These relationships were consistent and stronger in earlier years. The LST-NDBI correlation is the most consistent (CV = 9.09), while the LST-NDBaI correlation is the most variable (CV = 60.21).
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