A new approach for land surface emissivity estimation using LDCM data in semi-arid areas: exploitation of the ASTER spectral library data set

Abstract

n this research, a new approach called non-vegetated based emissivity estimation method (NV-method) for estimating land surface emissivity (LSE) on Landsat-8 (known as Landsat Data Continuity Mission, LDCM) data has been proposed for semi-arid areas. At first, a simulation of channel emissivities and reflective bands of basic classes in vegetation and non-vegetated areas is accomplished based on convolving Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral Library with LDCM spectral response functions. Then, four main classes in non-vegetated areas are defined to determine separate emissivity estimate model as a function of reflective bands from basic spectra associated with the main class. The LSEs in mixed and vegetation areas are adopted from the simplified normalized difference vegetation index (NDVI)-based emissivity threshold method (N-method^THM), namely SN-method^THM and improved N-method^THM (IN-method^THM) methods, respectively. The NV-method is empirically tested using LDCM data and the obtained LSEs were compared with two scenes of LSE product of the ASTER. The root mean square error (RMSE) values of computed LSEs by NV-method are 0.46% and 0.81%, for band 10 and 11, respectively, in the first examined scene. While, for the second scene, the RMSE are 0.36% and 0.56% for band 10 and 11, respectively. Moreover, the NV-method were compared with N-method^THM, SN-method^THM, and IN-method^THM in non-vegetated areas. Generally, the obtained results of LSEs by NV-method are better than that of results from the compared methods in non-vegetated areas in terms of statistical measures. Except in rocky class, for which N-method^THM provides better results, the NV-method achieved superior results in soil texture and man-made classes, which are dominating classes in the study area.