Abstract
Abstract The present study assesses the monthly variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) in Raipur City of India using one hundred and eighteen Landsat images from 1988 to 2019. The results show that a monthly variation is observed in the mean LST. The highest mean LST is found in April (38.79oC), followed by May (36.64oC), June (34.56oC), and March (32.11oC).The lowest mean LST is observed in January (23.01oC), followed by December (23.76oC), and November (25.83oC). A moderate range of mean LST is noticed in September (27.18oC), October (27.22oC), and February (27.88oC). Pearson's linear correlation method is used to correlate LST with NDVI. The LST-NDVI correlation is strong negative in October (-0.62), September (-0.55), and April (-0.51). The moderate negative correlation is developed in March (-0.40), May (-0.44), June (-0.47), and November (-0.39). A weak negative correlation is observed in December (-0.21), January (-0.24), and February (-0.29). The change in weather elements and variation in land surface characteristics contribute to the monthly fluctuation of mean LST and LST-NDVI correlation. The study will be an effective one for the town and country planners for their future estimation of land conversion.
Highlights
The thermal infrared (TIR) region of the electromagnetic spectrum has a huge potential in determining the nature and characteristics of land surface dynamics in any natural environment along with the visible and nearinfrared (VNIR) and shortwave infrared (SWIR) regions (Chen et al, 2006; Ghobadi et al, 2014; Guha et al, 2018; Guha & Govil, 2020; Guha & Govil, 2021a; Guha & Govil, 2019; Alexander, 2020)
The scenario was completely different from October to February, where that the mean Land surface temperature (LST) of the city was below 28oC LST
February (27.88oC mean LST), October (27.23oC mean LST), September (27.18oC mean LST), and November (25.83oC mean LST) - these four months have an average value of 25-28oC mean LST throughout the entire time
Summary
The thermal infrared (TIR) region of the electromagnetic spectrum has a huge potential in determining the nature and characteristics of land surface dynamics in any natural environment along with the visible and nearinfrared (VNIR) and shortwave infrared (SWIR) regions (Chen et al, 2006; Ghobadi et al, 2014; Guha et al, 2018; Guha & Govil, 2020; Guha & Govil, 2021a; Guha & Govil, 2019; Alexander, 2020). Land surface temperature (LST) is a major factor to assess the biogeochemical actions in the formation of land surface materials and it is the most essential parameter to evaluate the ecological condition of rural or urban areas (Tomlinson et al, 2011; Hao et al, 2016). Green vegetation and water bodies present low LST, whereas a built-up area, bare rock surface, or dry soil reflects high LST (Guha et al, 2019). Urban heat islands and urban hot spots are very common term in an urban environment and are indicated by the zone of very high LST inside the urban bodies (Guha et al, 2017). NDVI is directly used in the determination of land surface emissivity and is a significant factor for LST estimation (Sobrino et al, 2004; Carlson & Ripley, 1997)
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