A Python-Based Application for Retrieving Land Surface Temperature (LST) from Landsat Imagery
zahra parvar
1
(
PhD student ,Environmental science and engineering, Faculty of Fisheries and Environmental Science, Department of Environmental Science, Gorgan University of Agricultural Sciences and Natural Resources.
)
عبدالرسول سلمان ماهینی
2
(
دانشگاه علوم کشاورزی و منابع طبیعی گرگان- دانشکده شیلات و محیط زیست- گروه محیط زیست
)
Keywords: Radiative Transfer Equation, Split window algorithm, Single Channel Algorithm, remote sensing, Mono Window Algorithms,
Abstract :
Abstract LST (land surface temperature) derived from thermal infrared remote sensing images is directly related to land use and land cover changes. Remote sensing, as an irreplaceable method to obtain LST at global and regional scales, enables effective monitoring of LST with Spatio-temporal continuity. LST helps in separating urban areas from bare areas and improves land use/cover generation through classification of remotely sensed imagery. In this study, a Python-based user interface was developed to make land surface temperature retrieval easier and faster. LST can be retrieved by inputting required parameters in different methods such as Single Channel Algorithm (SCA), Radiative Transfer Equation (RTE) method, Split Window Algorithm (SWA), and two Mono Window Algorithms (MWA), from Landsat missions (Landsat 5, 7, and 8). Comparing the results in this study showed that RTE and SCA with root mean square error (RMSE) equal to 3.76 and 8.97 degrees Celsius had the highest and lowest accuracy. LST is affected by atmospheric particulate matter, land cover and urban morphology. Various methods of LST retrieval consider surface temperature, water vapor and other atmospheric factors. The developed user interface helps researchers and managers in monitoring land surface temperature change through time as affected by land use/cover, especially urban land use
