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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
Both theoretical study and experimental analysis indicated that 
the ANN technology was feasible to water color remote sensing 
research, and the model had strong ability to simulate 
complicated inversing relation of second type water body. 
On the basis of satellite synchronous monitoring experiment, a 
BP neural network model was constructed, by which 
concentrations of SS, CODMn, DO, T-N, T-P and chl-a were 
inversed from Landsat TM data and the accuracy was 
acceptable, the relative error could be controlled below 25%. 
And reasons of simulating error, ways of improving model and 
applications of the model were analyzed in details. The result of 
this research showed that based on a small-scale of satellite 
synchronous experiment, the model could be applied 
successfully in investigation, analysis and estimation of lake 
water quality. 
REFERENCES: 
Bricaud, A., and Morel, A., 1986. Light attenuation and 
scattering by phytoplankton cells: a theoretical modeling. Appl. 
Opt., 25, pp. 571-580. 
Campbell, J. W., O'Reilly, J. E., 1988. Role of satellites in 
estimating primary productivity on the northwest Atlantic 
continental shelf. Cont. Shelf Res., 8, pp. 179-204. 
Claudia, G., Monica, P., 2001. Detecting chlorophyll, Secchi 
disk depth and surface temperature in a sub-alpine lake using 
Landsat imagery. The Science of the Total Environment, 268, 
pp. 19-29. 
Chen, C. Q., Shi, P., Mao, Q. W., 1996. Study on modeling 
chlorophyll concentration of surface coastal water using TM 
data. Environmental Science and Technology, 11(3), pp. 168- 
176(in Chinese). 
Cheng, S. T., Kuang, C., Wang, J. P, 2002. Probe into the 
Water Color Remote Sensing Theoretical Model. Journal of 
Tsinghua University, 42(8), pp. 1027-1031 (in Chinese). 
Cong, S., 1998. Neural network theory and application on 
MATLAB toolbox. The university of science and technology of 
china press, Anhui, 11, pp. 45-60(in Chinese). 
Ekstrand, S., 1992. Landsat TM based quantification of 
chlorophyll-a algae bloom in coastal waters. /nt. J. Remote 
Sens., 10, pp. 1913-1926. 
Kuang, C., Wang, J. P., Cheng, S. T., etc., 2002. The Inversing 
Study of Water Color Remote Sensing in Sea Area of Macau. 
In: The fourth symposium on environmental and city 
development. Beijing, China, pp. 81-86 (in Chinese). 
Morel, A., Prieur, L., 1977. Analysis of variations in ocean 
color. Limnol. Oceanogr., 22, pp. 709-722. 
Richter, R. A., 1990. Fast atmospheric correction algorithm 
applied to Landsat TM Images. Int. J. Remote Sens., 11, pp. 
159-166. 
Richter, R. A., 1996. Spatially adaptive fast atmospheric 
correction algorithm. Int. J. Remote Sens., 17, pp. 1201-1214. 
681 
Shuleikin, V. V., 1933. Data on the optics of a strongly 
scattering medium, applied to sea water, fog and cloud. 
Geofizika, 3, pp. 3-5. 
Zhao, B. Y., He, B., Zhu, Y. Y., etc., 2000. Remote sensing 
water quality model of suspended sediments in the dianchi lake 
water bodies. Environmental Science and Technology, 3(94), 
pp. 16-18(in Chinese). 
Zhan, H. G., Shi, P., Chen, C. Q., 2000. Inversing chlorophyll 
concentration of sea water using artificial neural network. 
Chinese science bulletin, 45(17), pp. 1779-1884(in Chinese).