This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of
environmental processes.We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and
retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite
data. We apply the method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, when a large sediment plume
was observed in the images following a major storm event. Using our approach, we combine the images with a sediment transport model to
produce maps of sediment concentrations and uncertainties over space and time.We show that our approach improves out-of-sample RMSE
by 20%–30% relative to standard approaches. This article has supplementary material online.
