Noise tolerance of algorithms for estimating chlorophyll a concentration in turbid waters.

The accuracy and noise tolerance of 13 global models and 5 Case II chlorophyll a (chl a) retrieval models were evaluated using three dataset. It was found that if 5% input noise related to atmospheric correction is considered, then the uncertainty associated with noise tolerance varied from 5.5% to 55.6%, and these uncertainties generally accounts for 15.63% to 24.75% of the total uncertainty. This observation suggests that an optimal algorithm not only should have a strong chl a concentration prediction ability but also should possess high insensitivity to the noise of remote-sensing imagery. The accuracy evaluations of chl a models were based on comparisons of chl a predicted models with chl a concentration measured analytically for field measurements. The results indicate that none of the selected chl a estimation algorithms provide accurate retrievals of chl a in turbid waters. This may be attributed to the strong optical influence of organic and inorganic matter at the blue green range, and the non-negligible of non-organic matter absorption at the red and near-infrared ranges. In order to solve this problem, the chl a concentration retrieval models must be further optimized. After being optimized using the empirical optimized method constructed in this paper, a single parameterized NDCI (normalized difference chl a index) model produces accurate retrievals in the Yellow River Estuary, Taihu Lake and Chesapeake Bay. If 5% input noise associated with residual uncertainty 0of atmospheric correction is taken into account, the model produces only 29.96% uncertainty for the remote sensing of chl a concentration in these three turbid waters.