A filtering approach for estimating lake water quality from remote sensing data |
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| Affiliation: | 1. CSIRO: Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia;2. University of Technology Sydney: Plant Functional Biology and Climate Change Cluster, PO Box 123, Broadway, NSW 2007, Australia;1. Purdue University, Department of Forestry and Natural Resources, 195 Marsteller Street, West Lafayette, IN 47907, USA;2. Michigan Department of Natural Resources, Alpena Fisheries Research Station, 160 E. Fletcher, Alpena, MI 49707, USA;3. Michigan Department of Natural Resources, Lake St. Clair Fisheries Research Station, 33135 South River Road, Harrison Township, MI 48045, USA;4. NOAA/GLERL Lake Michigan Field Station, 1431 Beach Street, Muskegon, MI 49441, USA;1. Lake Michigan Biological Station, Illinois Natural History Survey, University of Illinois, Zion, IL, USA;2. Dept. of Environmental Science and Biology, The College at Brockport — State University of New York, Brockport, NY, USA;3. Dept. of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA;4. School of Freshwater Sciences, University of Wisconsin–Milwaukee, Milwaukee, WI, USA;5. School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, USA |
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| Abstract: | In this paper we consider the estimation of lake water quality constituent distributions from hyperspectral remote sensing data. We present a computational approach that can be used to assimilate information from mathematical evolution models into data processing. The method is based on a reduced order iterated extended Kalman filter, and a convection–diffusion model is used to describe the movement of the water quality constituents. The performance of the technique is evaluated in a simulation study. The results show that the filter approach with an appropriate evolution model yields estimates that have better spatial and temporal resolutions than those obtained with conventional methods. Furthermore, the use of a feasible evolution model may make it possible to obtain information also on the concentrations in the lower parts of the lake. |
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| Keywords: | Water quality Bio-optical modeling Convection–diffusion model Filtering |
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