Estimation of land surface temperature using different retrieval methods for studying the spatiotemporal variations of surface urban heat and cold islands in Indian Punjab

Spatial patterns of land surface temperature (LST), surface urban heat island (SUHI), surface urban cold island (SUCI), and their seasonal variations during January (winter) and September (summer) were analyzed over the three cities of Indian Punjab (Balachaur, Ludhiana and Bathinda) using Landsat 5, 7 and 8 satellite data of the years 1991, 2001, 2011, and 2018. Urban hot spots and Urban Thermal Field Variance Index (UTFVI) were used to measure the ecological environment of these cities. Land surface temperature was retrieved from Landsat satellite data using Plank equation, mono-window algorithm (MWA), single-channel algorithm (SCA), and radiative transfer equation. The LST derived using these algorithms was validated with MODIS-LST product. The relationship between LST derived from Landsat 5, 7 and 8 using the four methods and MODIS-LST product was higher with the SCA algorithm ( R 2 > 0.75). Land surface temperature was significantly positively correlated with built-up but significantly negatively correlated with vegetation. The surface urban heat intensity was higher during September than January, and it was higher in Ludhiana followed by Bathinda and Balachaur, irrespective of the season. Besides built-up area and population density, soil moisture availability in surrounding rural areas has significant impact on increasing surface urban heat intensity during September than January. The SUCIs were formed in the center of Bathinda city during January 1991, but these were in Ludhiana and Balachaur cities during January 2011. The most critical areas for ecological environment based on UTFVI were identified and the critical UTFVI values (> 0.020) were highest in Bathinda city followed by Balachaur and Ludhiana cities. These results suggest that SUHIs and SUCIs are influenced by seasons and the mitigating plans to counteract the overheating of urban areas should be formulated taking into account soil moisture availability in surrounding rural areas, landscape pattern, seasonal variations, local climatic conditions, urban growth, and development plan etc.