LAND SURFACE TEMPERATURE EXTRACTION USING LANDSAT DATA OVER DEHRADUN, INDIA: ASSESSMENT OF VARIOUS RETRIEVAL ALGORITHMS AND EMISSIVITY MODELS
Land Surface Temperature Extraction from Landsat Data
DOI:
https://doi.org/10.55766/sujst-2023-06-e02912Keywords:
Landsat, land surface temperature (LST), land surface emissivity (LSE), mono window algorithm, radiative transfer equation, single channel algorithmAbstract
Land Surface Temperature (LST) is an important parameter with widespread ecological implications due to its substantial influence on the dynamic interactions between the Earth’s surface and its atmosphere. LST retrieval is helpful for the evaluation of urban environments; to identify the Urban Hot Spots (UHS) within a city and suggest mitigating plans to counteract the over-heating of urban areas. This study focuses on the extraction of LST data over Dehradun during the month of May (summer) for the years 1993, 2003, 2013, and 2023. The primary objective of this study is to calculate LST using satellite data using different LST retrieval models. LST retrieval methods: Radiative Transfer Equation (RTE), Mono Window Algorithm (MWA), and Single Channel Algorithm (SCA) were used and compared. To ensure validation of the estimated LST values, a comparison was conducted with weather station-based temperature measurements obtained from the Indian Meteorological Department (IMD). The results suggest that the land surface emissivity method to calculate LST, as proposed by Sobrino et al. (2008), provides the best performance among all LST methods. Although the approach proposed by Sobrino et al.’s (2008) LSE model achieved satisfying performance, MWA exhibited a marginally superior level of accuracy (0.052°C RMSE).
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