STUDY ON REMOTE SENSING ESTIMATION OF SUSPENDED MATTER CONCENTRATIONS BASED ON IN SITU HYPERSPECTRAL DATA IN LAKE TAI WATERS

Suspended matter concentration is an important parameter of water quality and water environment evaluation. The field experiments including water quality analysis and spectrum measurements were carried out in 74 stations of Lake Tai during 14 days from 8(th) Nov. 2007 to 21(th) Nov. 2007. After analyzing the correlations between remote sensing reflectance and suspended matter concentrations, the results show that remote sensing reflectance in the range of 400 similar to 900nm wave bands is highly and moderately related to total suspended matter (TSM) and inorganic suspended matter (ISM) concentrations, and the biggest Pearson coefficients for TSM and ISM all appear at 725nm, and they are 0.883 and 0.869 respectively. And remote sensing reflectance is't related to organic suspended matter concentration. Neural network models of retrieving suspended matter concentrations were established by. using remote sensing reflectance at sensitive wave bands. As to TSM concentration retrieval, a neural network model with 6 nerve cells in connotative layer shows best, whose R-2 is 0.948 and RMSE is 4.947; hut as to ISM, another model with 4 nerve cells in connotative layer is the best one, whose R-2 is 0.956, and RMSE is 5.104. Additionally, error analysis of neural network model and empirical model were conducted by using test samples. Based on the above analyses, the conclusion is that neural network models with hyper-spectrum remote sensing reflectance are more suitable for retrieving suspended matter concentrations of TSM and ISM than empirical models.