Explore the vast range of titles available.

This paper presents a Python QGIS (PyQGIS) plugin, which has been developed for the purpose of producing Land Surface Temperature (LST) maps from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS, Thermal Infrared (TIR) imagery. The plugin has been developed purposely to ease the process of LST extraction from Landsat Visible, Near Infrared (VNIR) and TIR imagery. It has the ability to estimate Land Surface Emissivity (LSE), calculating at-sensor radiance, calculating brightness temperature and performing correction of brightness temperature against atmospheric interference though the Plank function, Mono Window Algorithm (MWA), Single Channel Algorithm (SCA) and the Radiative Transfer Equation (RTE). Using the plugin, LST maps of Moncton, New Brunswick, Canada have been produced for Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS. The study put much more emphasis on the examination of LST derived from the different algorithms of LST extraction from VNIR and TIR satellite imagery. In this study, the best LST values derived from Landsat 5 TM were obtained from the RTE and the Planck function with RMSE of 2.64 °C and 1.58 °C, respectively. While the RTE and the Planck function produced the best results for Landsat 7 ETM+ with RMSE of 3.75 °C and 3.58 °C respectively and for Landsat 8 TIRS LST retrieval, the best results were obtained from the Planck function and the SCA with RMSE of 2.07 °C and 3.06 °C, respectively.

Understanding land surface temperature (LST) dynamics is crucial for assessing the impacts of changes in land use and land cover (LULC) through remote sensing. However, the complexity and time-intensive nature of existing LST extraction algorithms pose challenges for many users. In response, this study introduces an open-access Python-based user interface tailored for extracting LST from Landsat images (Landsat 5, 7, 8 and 9) using multiple algorithms, including the Mono-Window Algorithm (MWA), radiative transfer equation (RTE) method, Single Channel Algorithm (SCA), and Split Window Algorithm (SWA). The primary problem addressed by this research is the accessibility and usability of LST extraction methods for researchers and practitioners. By developing a user-friendly interface that facilitates algorithm comparison and selection, the software aims to streamline the process of LST retrieval and analysis. To evaluate the efficacy of the implemented algorithms, 24 Landsat images, spanning different seasons (six images per Landsat mission), were utilized. Results indicate that while all methods yielded acceptable outcomes, the RTE method demonstrated slightly superior accuracy for Landsat 5 and 7, with lower root mean square error (RMSE) values. Conversely, for Landsat 8 and 9, the SWA exhibited the best performance, achieving an RMSE of 2.1 °C.

Land Surface Temperature (LST) is an important measurement in studies related to the Earth surface’s processes. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the Terra spacecraft is the currently available Thermal Infrared (TIR) imaging sensor with the highest spatial resolution. This study involves the comparison of LSTs inverted from the sensor using the Split Window Algorithm (SWA), the Single Channel Algorithm (SCA) and the Planck function. This study has used the National Oceanic and Atmospheric Administration’s (NOAA) data to model and compare the results from the three algorithms. The data from the sensor have been processed by the Python programming language in a free and open source software package (QGIS) to enable users to make use of the algorithms. The study revealed that the three algorithms are suitable for LST inversion, whereby the Planck function showed the highest level of accuracy, the SWA had moderate level of accuracy and the SCA had the least accuracy. The algorithms produced results with Root Mean Square Errors (RMSE) of 2.29 K, 3.77 K and 2.88 K for the Planck function, the SCA and SWA respectively.