Dynamic Models for Satellite Images
Jonathan R. Stroud, Michael L. Stein, Barry M. Lesht, David J. Schwab, Dmitry Beletsky
George Washington University, University of Chicago, University of Illinois-Chicago, NOAA-GLERL, and University of Michigan
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 ensemble Kalman filter and smoothing algorithms for on-line and
retrospective state estimation. Our approach adddresses the nonlinearities, high-dimensionality
and measurement bias inherent in satellite data. We apply our method to a sequence of
SeaWiFS satellite images in Lake Michigan from March 1998, showing the development of a
large sediment plume. Using this approach, we combine the images with a sediment transport
model to estimate the sediment concentrations and uncertainties over space and time.
The manuscript is available in PDF format.