PyLST: A Python-based application for retrieving Land Surface Temperature from Landsat 5, 7, 8, & 9

Abstract Land Surface Temperature (LST) can be used to understand the impacts of changes in Land Use and Land Cover (LULC) through remote sensing. This research introduces an open-access Python-based user interface for retrieving LST from Landsat images (Landsat 5, 7, 8 & 9) using multiple algorithms including Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA). This software enables users to efficiently choose the most suitable algorithms by comparing different methods within their study area. A total of 24 Landsat images, comprising six images for each Landsat mission and encompassing various seasons, were employed to assess and compare the accuracy of the algorithms. All methods presented acceptable results, however, RTE provided slightly better results for Landsat 5 and Landsat 7 with a lower RMSE value. In Landsat 8 and 9, SWA had better results than the other algorithms (RMSE 2.1°C).