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Foundation: Major project of Fujian provincial department of 
science & technology. N0.2006F5029 and N0.2006F30104 
Author: Wang Tianxing (1982-), M.Sc, specialized in inversion 
of land surface parameters ;Thermal infrared remote sensing, 
GIS application, Land Use and Land Cover Change. E-mail: 
watixi@163.com.TEL: 13459194134 
mode, Stochastic Geometric model and Fuzzy model (Charles 
Lchoku,1996) to extract the sub-pixel information. Specially, 
the Linear Spectral Mixture Model(LSMM) is currently one of 
the most prevailing sub-pixel model for its simpleness and 
easily operation(Chabrillat, 2000),and has been broadly used in 
land-use and land-cover classification 
(Adams et al., 1995; Gong et al., 1994; Quarmby et al., 
1992),land-cover change detection (Adams et al., 1995; 
Peterson & Stow, 2003), vegetation inversion(Patrick Hostert et 
al,2003), thermal feature extraction(Dengsheng Lu & Qihao 
Weng,2006) and geological surveys (Neville et al., 2003) during 
the past 30 years. Interestingly, some authors also found LSMM 
to be equivalent to the orthogonal subspace projection (OSP) 
method and linear support vector machine (SVM) (Brown et al., 
2000; Settle, 1996). 
Linear Spectral Mixture Model also known as Linear spectral 
unmixing (LSU), sub-pixel sampling, or spectral mixture 
analysis(SMA), is a widely used procedure to determine the 
proportion of constituent materials within a pixel based on the 
materials spectral characteristics (Boardman, 1989),and 
followed by three assumption^ 1) the spectra signals are linearly 
contributed by a finite number of land-cover classes 
(endmembers) within each pixel weighted by their cover 
percentage (Ichoku & Kamieli, 1996); (2) the endmembers in a 
pixel are homogeneous surfaces and spatially segregated 
without multiple scattering (Keshava & Mustard, 2002); and (3) 
the electromagnetic energy of neighboring pixels does not affect 
the spectral signal of the target pixel. Although nonlinear 
mixing effects due to the uncertainty caused by ,such as, 
atmospheric absorption and scattering, adjacent effect of pixel, 
have been considered in previous literature(Borel & Gerstl, 
1994; Ju et al., 2003;Pu et al., 2003) they are complicated and 
case-specific and not seriously deteriorate the unmixing 
results(Xin Miao et al,2006;Chabrillat et al.,2000). 
Researches on LSMM in previous literature mostly focused on 
the evaluation of linear hypothesis relating to itself and 
techniques used to select endmembers and other uncertainties, 
nevertheless, the environment conditions of study area which 
could sway the unmixing-accuracy such as atmosphere 
reflectance or scattering and terrain undulation are not studied. 
The terrain effects are very notable from the satellite imagery