Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25881
Title: Improving sediment transport prediction by assimilating satellite images in a tidal bay model of Hong Kong
Authors: Zhang, P
Wai, OWH 
Chen, X
Lu, J
Tian, L
Keywords: Data assimilation
Deep bay
MODIS
Optimal interpolation
Satellite image
Sediment transport model
Issue Date: 2014
Publisher: Mdpi Ag
Source: Water (Switzerland), 2014, v. 6, no. 3, p. 642-660 How to cite?
Journal: Water (Switzerland) 
Abstract: Numerical models being one of the major tools for sediment dynamic studies in complex coastal waters are now benefitting from remote sensing images that are easily available for model inputs. The present study explored various methods of integrating remote sensing ocean color data into a numerical model to improve sediment transport prediction in a tide-dominated bay in Hong Kong, Deep Bay. Two sea surface sediment datasets delineated from satellite images from the Moderate Resolution Imaging Spectra-radiometer (MODIS) were assimilated into a coastal ocean model of the bay for one tidal cycle. It was found that remote sensing sediment information enhanced the sediment transport model ability by validating the model results with in situ measurements. Model results showed that root mean square errors of forecast sediment both at the surface layer and the vertical layers from the model with satellite sediment assimilation are reduced by at least 36% over the model without assimilation.
URI: http://hdl.handle.net/10397/25881
ISSN: 2073-4441
DOI: 10.3390/w6030642
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