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Proceedings Paper

Some insights of spectral optimization in ocean color inversion
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Paper Abstract

Over the past few decades, various algorithms have been developed for the retrieval of water constituents from the measurement of ocean color radiometry, and one of those approaches is spectral optimization. This approach defines an error function (or cost function) between the observed spectral remote sensing reflectance and an estimated spectral remote sensing reflectance over the range of observed wavelengths, with the latter modeled using a few variables that represent the optically active properties (such as the absorption coefficient of phytoplankton and the backscattering coefficient of particles). The values of the variables when the error function reaches a minimum are the optimized properties. The applications of this approach implicitly assume that there is only one global minimum condition, and that any local minimum (if exist) can be avoided through the numerical optimization scheme. Here, with data from numerical simulations, we show the shape of the error surface as a mechanism to visualize the solution space for the model variables. Further, using two established models as examples, we demonstrate how the solution space changes under different model assumptions as well as the impacts on the quality of the retrieved water properties.

Paper Details

Date Published: 8 October 2011
PDF: 11 pages
Proc. SPIE 8175, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011, 817508 (8 October 2011); doi: 10.1117/12.897875
Show Author Affiliations
Zhongping Lee, Univ. of Massachusetts at Boston (United States)
Bryan Franz, NASA Goddard Space Flight Ctr. (United States)
Shaoling Shang, Xiamen Univ. (China)
Qiang Dong, Mississippi State Univ. (United States)
Robert Arnone, U.S. Naval Research Lab. (United States)

Published in SPIE Proceedings Vol. 8175:
Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011
Charles R. Bostater; Stelios P. Mertikas; Xavier Neyt; Miguel Velez-Reyes, Editor(s)

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