中文摘要：

总悬浮物浓度（CTSM）是水质评价的重要参数.为了提高内陆Ⅱ类水体总悬浮物浓度估算的精度,利用主成分分析方法对2009年4月太湖水体实测高光谱数据进行降维处理,进而以不同数量的主成分作为变量,分别构建总悬浮颗粒物浓度的多元线性回归估算模型并比较这些模型的效果,从而确定最优的主分量个数;结合近年运行的高光谱传感器,对模型的适用性进行评价.结果表明：①前三个主成分（PC1、PC2、PC3）从不同侧面涵盖了悬浮物浓度信息,它们与ln（CTSM）的相关系数分别为0.728、0.401和0.403;②当主成分个数为6时,模型达到最优;模型的精度高于4个传统经验模型;③在400～ 850 nm之间,波段数大于45的高光谱传感器数据都能利用主成分分析的方法构建精度较高的总悬浮物浓度估算模型;此外,MERIS、J1-HSI、Hyperion和CHRIS这些常用的高光谱传感器的波段设置,都适合于主成分建模.

英文摘要：

Total suspended matter concentration （CTSM） is all important parameter for water quality evaluation.In this study,to improve the estimation accuracy of CTs in inland type Ⅱ water,principal component analysis （PCA） was used to reduce the dimensions of hyperspectral data measured in Lake Taihu in April,2009.Different multiple linear regression models of TSM were subsequently constructed using several principle components （PCs）,and the optimal model was determined by comparing the performance of these models with each other.Finally,the applicability of the model to image data of the several current hyperspectral sensors was evaluated.The results show：① The first 3 PCs （PC1,PC2,PC3） could explain the most of TSM variation information and the correlation coefficients between the first 3 PCs and In（CTSM） are 0.728,0.401 and 0.403,respectively ; ② The optimal model could be developed when the number of PCs selected to be six.The performance of the model proposed in this study is better than that of the four traditional empirical models; ③ Image data of the hyperspectral sensor that has more than 45 bands between 400 and 850 nm could be used to build a stable and accurate model for estimating TSM using PCA.In addition,data from frequently used sensors such as MERIS,HJ1-HSI,Hyperion and CHRIS could be also used to build this type model.