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

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Abstract

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.

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Acknowledgements

This study is supported by the China State Major Basic Research Project (2013CB429701), Projects of International Cooperation and Exchanges of National Natural Science Foundation of China (41210005), Science Foundation for 100 Excellent Youth Geological Scholars of China Geological Survey, Serial Maps of Geology and Geophysics on China Seas and Land on the Scale of 1:1000000 (200311000001), and the Public Science and Technology Research Funds Projects of Ocean (201005030). We would like to just express our gratitude to two anonymous reviewers for their useful comments and suggestions.

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  1. School of Ocean Sciences, China University of Geosciences, Beijing, 100083, China

    Jun Chen

  2. The Key Laboratory of Marine Hydrocarbon Resources and Environmental Geology, Qingdao Institute of Marine Geology, Qingdao, 266071, China

    Jun Chen

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Correspondence to Jun Chen.

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Chen, J. Noise tolerance of algorithms for estimating chlorophyll a concentration in turbid waters. Environ Monit Assess 186, 2297–2311 (2014). https://doi.org/10.1007/s10661-013-3538-z

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