It is very hard to access cloud-free remote sensing data, especially for the ocean color images. A cloud removal approach from ocean color satellite images based on numerical modeling is introduced. The approach removes cloud-contaminated portions and then reconstructs the missing data utilizing model simulated values. The basic idea is to create the relationship between cloud-free patches and cloud-contaminated patches under the assumption that both of them are influenced by the same marine hydrodynamic conditions. Firstly, we find cloud-free GOCI (the Geostationary Ocean Color Imager) retrieved suspended sediment concentrations (SSC) in the East China Sea before and after the time of cloudy images, which are set as initial field and validation data for numerical model, respectively. Secondly, a sediment transport model based on COHERENS, a coupled hydrodynamic-ecological ocean model for regional and shelf seas, is configured. The comparison between simulated results and validation images show that the sediment transport model can be used to simulate actual sediment distribution and transport in the East China Sea. Then, the simulated SSCs corresponding to the cloudy portions are used to remove the cloud and replace the missing values. Finally, the accuracy assessments of the results are carried out by visual and statistical analysis. The experimental results demonstrate that the proposed method can effectively remove cloud from GOCI images and reconstruct the missing data, which is a new way to enhance the effectiveness and availability of ocean color data, and is of great practical significance.