Land surface temperature retrieval from Landsat 8 TIRS: comparison between split window algorithm and SEBAL method

Abstract

Land surface temperature (LST) is a key parameter in the physics of the earth surface through the process of energy and water exchange with the atmosphere, which plays an important role in a wide variety of scientific studies, such as ecology, hydrology, and global change studies. Thermal infrared (TIR) remote sensing provides a unique method for obtaining LST information at the regional and global scales since most of the energy detected by the sensor in this spectral region is directly emitted by the land surface. Landsat 8 Thermal Infrared Sensor (TIRS), because of two adjacent thermal bands, band 10 between 10.30-11.30 μm and band 11 between 11.30-12.30 μm, and availability of its images is a good source for retrieving LST. In this study, we compared two different methods for LST inversions from TIRS including the Split Window method and the SEBAL method for Tehran , Iran.. Results show that the LST inverted from the Split Window method has the accuracy with RMSE lower than 1.17 ˚ C, while the SEBAL method has the accuracy with 3.27 ˚ C, so the Split Window algorithm is appropriate method for this study area.