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In this research, a synthesis approach to estimating land surface emissivity (LSE) using remote sensing data and a spectral library that can be used to any optical sensor is proposed. The suggested method not only estimates the LSE more accurately as a function of the reflection of various surface effects, but it also takes into account the spectral response functions (SRF) of the thermal and reflective bands when calculating the LSE. Moreover, by using reflection of all reflectance bands, the proposed method strengthens the prior methods' poor link between LSE and reflectance of only the red band. The suggested approach was applied to a Landsat 8 imagery, and the resulting LSE was compared to and verified using two LSE products from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The findings indicated that the LSE from the proposed methodology in Landsat 8 thermal band 10 has a root- mean-square error of 0.76 % and 0.75 %, respectively, when compared to the equivalent LSE product of the first and second ASTER Images. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. Additional source of uncertainty in thermal band 11 might be the relatively substantial discrepancy in the SRF, spectral range, and effective wavelength between Landsat 8 band 11 and the 14-ASTER band. The results of this study compute the surface emissivity as a function of the reflectance of the reflective bands, and each pixel associated with that reflectance has a unique emissivity value that differs from that of nearby pixels. The prior technique, on the other hand, assigned a constant emissivity coefficient to the value of the group of pixels, and the surface emissivity was computed as a constant discrete value in each part of the image. [ABSTRACT FROM AUTHOR]