Backscattering in coastal waters is stronger than that in open sea because of high suspended sediment

and its information is reflected well in remote sensing images. The key to estimate suspended sediment concentration （SSC）from remote sensing images is to establish the relationship between SSC and water reflectance R.Many theoretic or empirical models have been developed for calculating SSC. But most of them need real-time survey data of SSC

which limit the use of these models. In this paper

spectral mixing analysis method

which needs little real-time data

was introduced in detail. The pixels with minimun and maximal SSC were used as endmembers

and other pixels were regarded as the mixtures of the two endmembers. A linear spectral mixing analysis model was as followed:R b=（1-f high）R Lb+f highR Hb+E b b=1

2

...

Bwhere R bwas the bth reflectance

R Lband R Hbwere the bth reflectance for minimum or maximal SSC

f high was the fraction of maximal SSC

E bwas residual error and Bwas the number of bands. In order to get the minimum sum of E b（b=1

2

...

B）

the following model has been developed:f high=∑Bb=1（R b-R Lb）（R Hb-R Lb）∑Bb=1（R Hb-R Lb）2 Based on TM remote sensing images

the quantitative relationship between SSC and spectral reflectance was studied. The distribution of SSCs in coastal waters of Minjiang River was calculated by using spectral mixing analysis and empirical formula respectively. The results showed that:（1） the high SSC regions lay in the mouth of Minjiang River

and the further from the coastal

the lower was the SSC; （2） the distribution of SSC can be estimated quickly and objectly from remote sensing images; （3） spectral mixing analysis is a good method

especially in areas lack of much measured data

for it can make full use of multi-band of images and needs less samples.