HY-1C Coastal Zone Imager observations of the suspended sediment content distribution details in the sea area near Hong Kong-Zhuhai-Macao Bridge in China

The impacts of the Hong Kong-Zhuhai-Macao Bridge(HKZMB)on suspended sediment content(SSC)were analysed in the Zhujiang River Estuary based on data from HY-1C,which was launched in September 2018 in China,carrying Coastal Zone Imager(CZI)and Chinese Ocean Color and Temperature Scanner on it.A new SSC inversion model was established based on the relationship between in-situ SSC and the remote sensing reflectance in red and near-infrared bands of CZI image.HY-1C satellite data obtained from October to December 2019 were applied to retrieve SSC in the Zhujiang River Estuary.The results show that SSC around the HKZMB is ranging from 20 mg/L to 95 mg/L.SSC change obviously on two sides of the bridge.During flooding and ebbing period,SSC increases obviously downstream of the bridge.SSC difference between upstream and downstream is ranging from 5 mg/L to 20 mg/L.Currents flowing across the HKZMB,the change trend of SSC in most places upstream and downstream is almost the same that SSC downstream of the bridge is higher than SSC upstream.The tidal currents interact with bridge piers,inducing vortexes downstream,leading the sediment to re-suspend downstream of the bridge piers.Other factors,including seafloor topography and wind,can also contribute to the distribution of SSC in the Zhujiang River Estuary.

Atmospheric correction for HY-1C CZI images using neural network in western Pacific region

With a spatial resolution of 50 m,a revisit time of three days,and a swath of 950 km,the coastal zone imager(CZI)offers great potential in monitoring coastal zone dynamics.Accurate atmo-spheric correction(AC)is needed to exploit the potential of quantitative ocean color inversion.However,due to the band setting of CZI,the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily.This research introduces a novel neural network(NN)AC algorithm for CZI data over coastal waters.Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager(OLI)-retrieved high-quality remote sensing reflectance(Rrs)at the CZI bands are built to train the NN model.These reflectance data are obtained from the standard AC algorithm in the SeaDAS.Results indicate that the distributions of the CZI retrieved Rrs were consistent with the quasi-synchronous OLI data,but the spatial information from the CZI is more detailed.Then,the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data.Results further show that the NN-AC can successfully retrieve Rrs for CZI and the coefficients of determination in the blue,green,red,and near-infrared bands were 0.70,0.77,0.76,and 0.67,respectively.The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained.