Total suspended matter concentration (C_TSM) is an important parameter for water quality evaluation.In this study,to improve the estimation accuracy of C_TSM 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 (PC_s),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 PC_s (PC₁ ,PC₂ ,PC₃) could explain the most of TSM variation information and the correlation coefficients between the first 3 PC_s and ln(C_TSM) are 0.728,0.401 and 0.403,respectively;② The optimal model could be developed when the number of PC_s 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.