Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index

Remote sensing technique often analyzes the thermal characteristics of any area. Our study focuses on estimating land surface temperature (LST) of Raipur City, emphasizing the urban heat island (UHI) and non-UHI inside the city boundary and the relationships of LST with four spectral indices (normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index). Mono-window algorithm is used as LST retrieval method on Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data, which needs spectral radiance and emissivity of TIRS bands. The entire study is performed on 11 multidate Landsat 8 OLI and TIRS images taken from four different seasons; premonsoon, monsoon, postmonsoon, and winter, in a single-year time period. The Landsat 8 data derived LST is validated significantly with Moderate Resolution Imaging Spectroradiometer (MOD11A1) data. The results show that the UHI zones are mainly developed along the northern and southern portions of the city. The common area of UHI for four different seasons is developed mainly in the northwestern parts of the city, and the value of LST in the common UHI area varies from 26.45 degrees C to 36.51 degrees C. Moreover, the strongest regression between LST and these spectral indices is observed in monsoon and postmonsoon seasons, whereas winter and premonsoon seasons revealed comparatively weak regression. The results also indicate that landscape heterogeneity reduces the reliability of the regression between LST with these spectral indices. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)