Environmental Monitoring and Assessment

, Volume 145, Issue 1–3, pp 339–347

A bio-optical model based method of estimating total suspended matter of Lake Taihu from near-infrared remote sensing reflectance



Total suspended matter is an important water quality parameter, and plays a key role in water quality evaluation, especially of inland waters. Many different methods have been developed to estimate TSM from remote sensing data, in which empirical methods and model-based methods are two types of commonly used methods. Compared with empirical methods, model-based methods have the advantages of definite physical meanings, high robustness and retrieval accuracy. In model-based methods, matrix inversion method is commonly used in monitoring water qualities of inland waters. However, matrix inversion method has to predetermine some optical parameters by empirical values or simplified optical model, which may introduce some errors in retrieved water quality parameters. In order to overcome the shortcomings of matrix inversion method and increase the estimating accuracy of total suspended mater, in this paper, a bio-optical model based method is developed, which estimates total suspended mater by using remote sensing reflectance of two near-infrared bands. This method is validated by in-situ experiment data measured in Lake Taihu, a big turbid lake in eastern China. The results show that this method has better performance than matrix inversion method. The average relative error of the estimated total suspended matter by this method is only 13.0%, which is much smaller than the errors by matrix inversion method (32.7%). This method has the advantages of definite physical meaning, easiness to carry out, and high estimating accuracy. However, the applicable scope of this method has limitations: it can only be applied to optically deep waters with high concentrations of total suspended matter.


Bio-optical model Total suspended matter TSM Lake Taihu Remote sensing reflectance Empirical method Model-based method Matrix inversion method 


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© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.State Key Laboratory of Remote Sensing ScienceInstitute of Remote Sensing Applications of Chinese Academy of SciencesBeijingChina
  2. 2.Department of GeographyQueen’s UniversityKingstonCanada

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