Land surface window emissivity is an important parameter for estimating the longwave radiative budget. This study focuses on estimating the window (8--12 ¿m) emissivity from the waveband emissivities of the five thermal infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). ASTER data along with the Temperature-Emissivity Separation (TES) algorithm allows us to estimate surface channel emissivities with 90 m spatial resolution globally. Multiple regression was used to relate window emissivity to the five ASTER emissivities. This regression was developed using spectral libraries. Its residual error was less than 0.005 (RMSE) for values ranging between 0.81 and 1.00. We applied this regression to ASTER emissivities extracted from data acquired in 2001 and 2002 over a 240 ¿ 1200 km area in a desert of North Africa. A comparison against a classification based emissivity map showed significant differences ranging between -0.08 and +0.06.