Estimations of Secchi depth from the turbid Eastern China Coastal Seas using MODIS data

ABSTRACT

In this study, a neural network-based Secchi depth retrieval model (NNSD) was developed using observations obtained during 2003–2012 in the Bohai, Yellow, and China East Seas (Eastern China Coastal Seas, ECCS). Based on the results of the analyses, the NNSD model produced less than 25% uncertainty in quantifying the Secchi depth in the optically complex ECCS. The slope of the linear relationship between the field-measured and NNSD model-derived Secchi depth varied from 0.98 to 1.08 among the datasets. However, the corresponding determination coefficients were not any lower than 0.91. To determine the effectiveness of the NNSD model in deriving the Secchi depth in the ECCS, the performances of the three existing models were also evaluated, and the results are presented in this study. By comparison, using the NNSD model decreased the uncertainty by 26% when compared with the three existing models in deriving the Secchi depth in the ECCS. Finally, the NNSD model was further applied to the MODIS data over the ECCS to briefly illustrate its applicability to general oceanographic studies. The NNSD model could produce 24.62% MRE and 0.14 RMSE values in deriving the Secchi depth from the ECCS, which was > 26% MRE and > 0.07 RMSE values better than all the BGSD, KDSD and SASD models. In regards to the spatial distribution pattern of the Secchi depth, the eastern and northern sections of the ECCS were much higher than the western and southern areas. Additionally, the temporal distribution mode indicated that the winter season was higher than the summer season. These spatiotemporal characteristics were caused primarily by the regional climate, river discharges, and strong tidal currents and winds, among other factors.

KEYWORDS:

• heat stratification
• upper oceanic layer
• turbidity water
• solar heating
• water quality

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Acknowledgements

This work was financially supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) (grant no. 2022QNLM050302-4) and the National Program of Geological Survey (No. DD20191008 and DD20221711).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) [2022QNLM050302-4]; National Program of Geological Survey [DD20191008, DD20221711].