DocumentCode :
1501412
Title :
A Stepwise Refining Algorithm of Temperature and Emissivity Separation for Hyperspectral Thermal Infrared Data
Author :
Cheng, Jie ; Liang, Shunlin ; Wang, Jindi ; Li, Xiaowen
Author_Institution :
State Key Lab. of Remote Sensing Sci., Beijing Normal Univ., Beijing, China
Volume :
48
Issue :
3
fYear :
2010
fDate :
3/1/2010 12:00:00 AM
Firstpage :
1588
Lastpage :
1597
Abstract :
Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in numerous environmental studies. In this paper, a stepwise refining temperature and emissivity separation (SRTES) algorithm is proposed based on the analysis of the relationship between surface self-emission and atmospheric downward spectral radiance in a narrow spectral region. The SRTES algorithm utilizes the residue of atmospheric downward spectral radiance in the calculated surface self-emission as a criterion and adopts a stepwise refining method to determine both the emissivity at the location of an atmospheric emission line in a narrow spectral region and the surface temperature. Three methods have been used to evaluate the SRTES algorithm. First, numerical experiments are conducted to evaluate if the SRTES algorithm can accurately retrieve the "true" LST and LSE from the simulated data. When a noise equivalent spectral error of 2.5 e-9 W/cm2/sr/cm-1 is added into the simulated data, the retrieved temperature bias (Tbias) is 0.04 ? 0.04 K, and the root-mean-square error (rmse) of the retrieved emissivity is below 0.002 except in the extremities of the 714-1250 cm-1 spectral region. Second, in situ measurements are used to validate the SRTES algorithm. The average rmse of the retrieved emissivity often samples is about 0.01 in the 750-1050 cm -1 spectral region and is 0.02 in the 1051-1250 cm-1 spectral region, but the rmse is larger when the sample emissivity is relatively low. Third, our new algorithm is compared with the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm using both a simulated data set and in situ measurements. The comparison demonstrates that the SRTES algorithm performs better than the ISSTES algorithms, and it can overcome some of the common drawbacks in the existing hyperspectral TES algorithms for the accurate retrieval of both temperatur- - e and emissivity.
Keywords :
emissivity; geophysical signal processing; infrared imaging; land surface temperature; remote sensing; ISSTES algorithm; SRTES algorithm; atmospheric downward spectral radiance; atmospheric emission line; emissivity retrieval; hyperspectral TES algorithms; hyperspectral thermal infrared data; iterative spectrally smooth temperature and emissivity separation; land surface emissivity; land surface temperature; noise equivalent spectral error; numerical experiments; stepwise refining algorithm; surface self emission; temperature retrieval; temperature-emissivity separation; wave number 714 cm-1 to 1250 cm-1; Hyperspectral; remote sensing; stepwise; temperature and emissivity separation (TES);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2009.2029852
Filename :
5288591
Link To Document :
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