Hassan Emami - Department of Geomatics, School of Marand Engineering, University of Tabriz, Tabriz-Iran
Seyyed Qasem Rostami - Department of Surveying Engineering, Faculty of Engineering, University of Bojnord, Bojnord-Iran

In this work, a knowledge based approach is proposed to overcome the errors and uncertainties in land surfaceemissivity (LSE) estimation and consequently land surface temperature (LST) retrieval. The Knowledge Based Methods(KBMs) which including two LSE estimation methods. The effectiveness of KBMs proposed is empirically tested over twoscenes of Landsat-۸ (known as Landsat Data Continuity Mission, LDCM) data sets and the obtained LSEs by conventionaland proposed methods were compared to the LSE product of the ASTER by image-based cross-comparison. In bothscenes, the NDVI-based emissivity method (NBEM) provide appropriate results among five conventional methods. Incontrast, Validity Average (VAvg) achieves superior results among proposed methods for both scenes. Moreover, the errorranges and RMSE of cross-comparison for the obtained LSE in proposed methods were remarkably decreased. Also, inthis research, for LST cross-comparison, an alternative scaling method based on LST products of MODIS wasproposed .The LST validation results demonstrated that proposed methods provide better estimates in terms of threeaccuracy measures in both examined datasets. Furthermore, the obtained LST of Knowledge Based LSE estimationmethod, show that the proposed methods provide better estimates in both examined datasets in terms of the threestatistical R۲ (improved ۸.۱۶%), the adjusted R۲(improved ۵%), MD (Bias) (improved ۱.۰۳K), and RMSE (improved ۰.۶K)measures rather than LST retrieval using conventional LSEs method.

Remote sensing, Knowledge based method, Land surface emissivity, Land surface temperature, LDCM