Application of open source coding technologies in the production of land surface temperature (LST) maps using landsat and aster imagery

This study presents a Python QGIS plugin which has been developed to extract the Land Surface Temperature (LST) from ASTER and Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS Thermal Infrared (TIR), Visible and Near Infrared (VNIR) imagery. It has been written using a free and open source Python programming language to work in the QGIS software. Due the difficulties arising from the implementation of the algorithms involved in LST extraction, most users have not managed to benefit enough from the data collected by the ASTER and Landsat sensors. This study has implemented the Mono Window Algorithm (MWA), the Single Channel Algorithm (SCA) for Landsat, the Radiative Transfer Equation (RTE), the Planck function, the Single Channel Algorithm (SCA) for ASTER and the Split Window Algorithm (SWA) for ASTER. Through the use of the plugin developed in the study, the LST maps of New Brunswick-Canada have been produced from the data obtained from the sensors. The accuracy assessment was done against near surface temperatures measured by the meteorological stations of the area. The best results obtained from Landsat 5 TM had Root Mean Square Errors (RMSE) of 1.58 °C, while the ones of Landsat 7 ETM+ had RMSE of 2.96 °C and for Landsat 8 TIRS, the RMSE were 2.07 °C. The plugin developed in this study is expected available for download through the official QGIS repository i.e. https://plugins.qgis.org/plugins without any cost. Through the plugin it is expected that users from other disciplines such geomatics, hydrology, energy, geothermal studies, evapotranspiration and other environmental related fields can manage to benefit from the plugin in the production of land surface temperature maps.