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Proceedings Paper

Evaluation of the improved linear emissivity constraint temperature and emissivity separation method by using the simulated hyperspectral thermal infrared data
Author(s): Hua Wu; Zhao-Liang Li; Bo-Hui Tang; Rong-Lin Tang
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Paper Abstract

In this study, an improved linear emissivity constraint temperature and emissivity separation (I-LECTES) method was first proposed to overcome the discontinuities problem of the retrieved land surface emissivities (LSEs) in the former linear emissivity constraint temperature and emissivity separation (LECTES) method. Consequently, the hyperspectral thermal infrared data were carefully simulated according to the configuration of Designs & Prototypes microFTIR Model 102, and were used to evaluate the performance of the I-LECTES method. Meanwhile, the I-LECTES method was also compared with the LECTES method. Different the atmosphere and surface circumstances were considered, as well as the different levels of noise equivalent temperature difference (NEΔT). The results showed that the proposed I-LECTES method is of a better accuracy compared with the LECTES method and has the characteristic of keeping the retrieved LSEs continuous, which sounds more reasonable. Because the noises in the ground measured radiance may have more effects on the accuracies of land surface temperature (LST) and LSEs than those in the atmospheric downwelling radiance, the noise in the ground measured radiance should be removed as much as possible to improve the accuracies of retrieved LST and LSEs. Furthermore, taken into account the lower retrieval accuracies for the cold and dry atmosphere, both the I-LECTES method and the LECTES method should be taken a full consideration. The proposed method is regarded to be promising because of its holding continuity and noise-immune.

Paper Details

Date Published: 9 December 2015
PDF: 8 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082L (9 December 2015); doi: 10.1117/12.2210940
Show Author Affiliations
Hua Wu, Institute of Geographic Sciences and Natural Resources Research (China)
Zhao-Liang Li, Guilin Univ. of Technology (China)
Institute of Geographic Sciences and Natural Resources Research (China)
Bo-Hui Tang, Institute of Geographic Sciences and Natural Resources Research (China)
Rong-Lin Tang, Institute of Geographic Sciences and Natural Resources Research (China)


Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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