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ABSTRACT
In 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-methodTHM), namely SN-methodTHM and improved N-methodTHM (IN-methodTHM) 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-methodTHM, SN-methodTHM, and IN-methodTHM 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-methodTHM provides better results, the NV-method achieved superior results in soil texture and man-made classes, which are dominating classes in the study area.
Acknowledgements
The authors would like to thank the providers of the LDCM imagery held in the USGS archives and reprocessing data sets (landsat.usgs.gov), the JPL for the ASL (v2.0) and the USGS Spectral Libraries, and Dr E. Ghambari from the University of Melbourne, Parkville, Vic, Australia, for the topographic and atmospheric correction of the LDCM data.
Disclosure statement
No potential conflict of interest was reported by the authors.