In this paper we develop and examine several schemes for combining daily images obtained from the Sea-viewing Wide Field Spectrometer (SeaWiFS) with a two-dimensional sediment transport model of Lake Michigan. We consider two data assimilation methods, direct insertion and a kriging-based approach, and perform a forecasting study focused on a 2-month period in spring 1998 when a large storm caused substantial amonunts of sediment resuspension and horizontal sediment transport in the lake. By beginning with the simplest possible forecast method and sequentially adding complexity we are able to assess the improvements offered by combining the satellite data with the numerical model. In our application, we find that data assimilation schemes that include both the data and the lake dynamics improve forecast root mean square error by 40% over purely model-based approaches and by 20% over purely data-based approaches. The manuscript is available in PDF format.