Retrieval of Microcystis aentginosa Percentage From High Turbid and Eutrophia Inland Water: A Case Study in Taihu Lake

Microcystis aentginosa (MA), which is one kind of cyanobacteria, is the primary algal species in Taihu Lake. The MA bloom has a significantly negative effect on the human health and water environment ecosystem. The monitoring and prediction of MA bloom become more and more critical for the security of drinking water source and environment in the Taihu Lake area. In this paper, the percentage of MA was estimated from remote-sensing reflectance via a novel spectral shape genetic optimization algorithm. This algorithm focuses on the shape of remote-sensing reflectance, and it can remove the influence of the amplitude of remote-sensing reflectance from the retrieval result. The accuracy of this optimization algorithm is acceptable according to both simulated and in situ data. The percentage of mean square root (RMSP) of the percentage of the phytoplankton absorption coefficient to the total absorption coefficient at 440 nm [a_r (440 nm)] between the retrieved and the simulated is 18.39%. The RMSP of the total absorption coefficient at 440 nm [a (440 nm)] between the retrieved and the simulated is 3.65%. The RMSP of the percentage of MA [S_f] between the retrieved and the simulated is 13.60%. The RMSP of the colored dissolved organic matter (CDOM) absorption coefficient slope [S] between the retrieved and the simulated is 5.89%. The RMSP of the particle backscatter coefficient slope [Y] between the retrieved and the simulated is 30.46%. In Taihu Lake, the RMSPs of a_r (440 nm), a (440 nm), S_f , and S between the retrieved and the measured are 36.59%, 35.70%, 19.25%, and 16.80%, respectively. The retrieved percentage of MA (S_f) and Scenedesmus obliquus (1 - S_f) by this model from August 2006, October 2006, to November 2008 indicates the variation trend of algal species in different seasons. This trend accords with the results from pre- - vious studies and observations. This paper extends and advances the previous retrieval methods and confirms that the genetic optimization algorithm can be used to retrieve the information of water constituents in the high turbid and eutrophic inland water mass.