Retrieving Land Surface Temperature From Landsat 8 Tirs Data Using Rttov And Aster Ged

Land surface temperature (LST) is a key parameter for a wide number of applications, which include hydrology, meteorology and model validation. In this paper a physical single channel algorithm was developed for retrieving LST from the Landsat 8 TIRS data. ASTER Global Emissivity Dataset (GED) and Vegetation Cover Method (VCM) were chosen to improve the accuracy of land surface emissivity and the fast radiative transfer model RTTOV was utilized for atmospheric correction which uses MERRA reanalysis data as inputs. The algorithm is evaluated by the ground measurements collected from in situ sites during the HiWATER experiment. The LST result shows a dynamical variation with the phenological changes and the average Bias and RMSE of the estimated LST for all sites after remove outliers are 0.09K and 2.20K, respectively. This indicates that the algorithm is suitable for producing LST product from Landsat 8 TIRS data and ASTER GED can be used to improve the accuracy of land surface emissivity in arid and semi-arid area.