A long-term monthly analytical study on the relationship of LST with normalized difference spectral indices

ABSTRACT This study analyzes the long-term monthly variation of land surface temperature (LST) and its relationship with normalized difference spectral indices in the Raipur City of India using one hundred and 23 Landsat images from 1988 to 2020. In specific, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and normalized difference bareness index (NDBaI) were used to show the relationship between LST and land surface materials. In terms of LST, the warmest month is April (38.49°C) and the coldest month is January (23.04°C). The standard deviation in LST is noticed as 1.1022°C throughout the period. The growth pattern of LST is increasing in the earlier stage while it is steady and decreasing in the later stage. The linear regression method is used to correlate LST with the spectral indices. The mean regression coefficients for LST-NDVI is −0.42, LST-NDBI are 0.68, LST-NDWI is 0.27, and LST-NDBaI is 0.32. It indicates that the high ratio of green vegetation and water bodies resist the raise of LST, whereas the bare rock surface and built-up land accelerate the LST. The value of the spectral indices and LST varies with the change of month due to the physical change of the land surface materials. Hence, the study will be an effective one for the town and country planners for their future estimation of land conversion.

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