By using the remote sensing data we can carry on the stream water quality detection.Most successful studies on water quality monitoring by remote sensing were mainly relied on choice of feasible method of atmospheric correction.This research incorporates digital count

radiance

reflectance

and reflectance with transmittance four different correction procedures to evaluate the effect on simulation.Dark object subtraction was selected for all procedures

and followed by separating the samples into two groups for the reason of the seasonal variation.In order to consider the sampling difficulty on SPOT images with its limited pixel resolution

two step unsupervised pre-classification and supervised post-verification were used for extracting the reliable water pixels from SPOT images which are corresponding to the same water quality monitoring locations in ten different days.The study adopted multivariate regression（MR）

artificial neural network（ANN）

and discriminant analysis（DA） to examine and compare the results of different relationships between optical spectrum and water quality.The overall results showed that the analysis from multivariate regression and discriminant analysis were not as good as the results obtained from artificial neural network in the study area.For atmospheric correction

the simple digital count with dark object subtraction method is necessary and sufficiently enough to count on atmospheric interference by comparing the results from four different correction procedures.However

this result of limited optical data correction and learning technique needs to be further confirmed by using higher resolution satellite images and more case studies.Basically

it is evident that artificial neural network has the potential and feasibility of monitoring water qualities and its derived index.