Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 TIRS Data

On the basis of the thermal infrared radiative transfer theory, this paper addressed the retrieval of Land Surface Temperature (LST) from Landsat 8-the latest satellite in the Landsat Data Continuity Mission (LDCM) project in two thermal infrared channels, using the Generalized Split-Window (GSW) algorithm. Meanwhile, a linear bidirectional reflectance distribution function (BRDF) models were used to estimate the emissivity according to different surface classification. A series of ranging of typical surface emissivity and the atmospheric water vapor content (WV) were used into an accurate atmospheric radiative transfer model MODTRAN 4.3 to derive the coefficients in the algorithm. The simulation result showed the LST estimated by the algorithm with the Root Mean Square Error (RMSE) is 1.26K for the all ranges of the atmospheric WV and the results could be better in lower atmospheric WV condition.