Abstract
Knowledge of the distribution and variation of water turbidity directly represent important information related to the marine ecology and multiple biogeochemical processes, including sediment transport and resuspension and heat transfer in the upper water layer. In this study, a neural network (NN) approach was applied to derive the water turbidity using the geostationary ocean color imager (GOCI) data in turbid estuaries of the Yellow River and the Yangtze River. The results showed a good agreement between the GOCI-derived turbidity and in situ measured data with a determination coefficient (R2) of 0.84, root mean squared error (RMSE) of 58.8 nephelometric turbidity unit (NTU), mean absolute error of 25.1 NTU, and mean relative error of 34.4%, showing a better performance than existing empirical algorithms. The hourly spatial distributions of water turbidity in April 2018 suggested that high turbidity regions were distributed in the Yellow River estuary, Yangtze River estuary, Hangzhou Bay, and coastal waters of Zhejiang Province. Furthermore, the relationship between water turbidity and tide were estimated. A defined turbid zone was defined to evaluate the diurnal variations of turbidity, which has subtle changes at different times. Our results showed an inverse relationship between turbidity and tide over six selected stations, i.e., when the value of turbidity is high, then the corresponding tidal height is usually low, and vice versa. The combined effects of tidal height and tidal currents could explain the phenomena, and other factors such as winds also contribute to the turbidity distributions.
Highlights
Water turbidity is an important proxy for monitoring water quality and can reflect the integrated optical properties of water conditions [1,2]
This study demonstrated that the spatial distributions and variations of water turbidity in China seas can be estimated accurately using the neural network (NN) approach with hourly intervals based on geostationary ocean color imager (GOCI) satellite data
An NN approach was designed to derive the water turbidity in the turbid estuary of the Yellow River and Yangtze River based on GOCI data
Summary
Water turbidity is an important proxy for monitoring water quality and can reflect the integrated optical properties of water conditions [1,2]. Turbidity is widely used to indicate the total suspended matter (TSM) and optical parameters [3,4]. Turbidity is a water-quality parameter that can be derived from remote sensing data. The underlying physical process for measuring suspended particle via turbidity is that particles causes scatter of light which is proportional to the TSM in the waters. Turbidity is a measure of the loss of light transparency in the near-infrared (860 nm) caused by the suspended particles. Measuring TSM gravimetrically is a rather time-consuming process and turbidity measured in situ is an efficient and cost-effective way to measure the influence of suspended particles in the estuaries. Satellite remote sensing technology in this context is very advantageous and potentially allows overcoming the spatiotemporal limitations of traditional measurements, providing an ideal opportunity for investigating water turbidity
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