中文摘要：

悬浮物浓度是水质和水环境评价的重要参数之一．利用2007-11-08～2007-11-21 14天时间对太湖74个样点进行水质取样分析和波谱实测．在提取水体遥感反射率后，分析其与悬浮物浓度的相关关系，发现在400～900nm波段范围的各波长遥感反射率与总悬浮物、无机悬浮物浓度都存在中高度相关，最大相关系数均出现在725nm处，分别为0．883和0．869．而与有机悬浮物浓度则无较好的相关性．同时利用敏感波段的遥感反射率建立了悬浮物浓度估算的神经网络模型，结果表明：对于总悬浮物浓度，隐含层节点数为6的神经网络模型的R^2=0．948，RMSE=4．947，在备节点中的训练效果最佳；而对于无机悬浮物浓度，隐含层节点数为4的神经网络模型的R^2=0．956，RMSE=5．104，模型整体训练结果最好．此外，通过测试样本对神经网络模型和经验模型的预测误差进行分析表明，无论是估算总悬浮物浓度，还是无机悬浮物浓度，无论从建模样本的建模精度，还是测试样本的误差分析，神经网络模型都优干经验模型．

英文摘要：

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 Tat during 14 days from 8th 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 - 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 ; but 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.