Discussion on linear algorithms for simultaneously retrieving three components of case 2 waters in Yellow Sea and East China Sea

Retrieving water components in case 2 waters by remote sensing is a crucial problem in evaluating ocean first productivity and monitoring various disasters. But it is difficult to accurately and universally develop both bio-optical models and remote-sensing reflectance model because independent temporal and spatial variation of dissolved organic matter (CDOM), chlorophyll and total suspended matter (TSM), high concentration of TSM, as well as the local characters of different regions. Currently Linear algorithms such as principal component analysis (PCA), factor analysis (FA), matrix inversion technique and semi-analytical algorithm are widely used in the field of ocean color. Remote sensing reflectance model is derived from the radiative transfer equation, which is significantly featured by non-linearity and negative feedback. In our study, the chlorophyll absorption model and some other parameters of bio-optical models are adjusted. The adjustment is based on the water components concentration measured simultaneously with remote sensing data in the Yellow Sea and the East Sea of China. Then the equation of remote-sensing reflectance model can be changed into linear matrix of water components and coefficients, we find the spectrum curves of total suspended matter coefficient and chlorophyll coefficient turn out significant negative correlation. As a result, when performing matrix retrieval algorithm, chlorophyll concentration and CDOM concentration are out of required accuracy except some special conditions. Experiment results suggested that the TSM had the greatest influence on the linear model.

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