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dc.contributor.authorFENG Huien_GB
dc.contributor.authorCAMPBELL Janeten_GB
dc.contributor.authorDOWELL MARKen_GB
dc.contributor.authorMOORE Timothy S.en_GB
dc.identifier.citationREMOTE SENSING OF ENVIRONMENT vol. 99 p. 232-243en_GB
dc.description.abstractThis study presents an approach for optimally parameterizing a reflectance model. A parameterization scheme is realized based on a comprehensive bio-optical data set, including subsurface downwelling and upwelling irradiance spectra, absorption spectra of particle and dissolved substances, as well as chlorophyll and total suspended matter concentrations at 45 stations near Tokyo Bay between 1982 and 1984. The irradiance reflectance model is implemented with three-component inherent optical property submodels. In this parameterization scheme, an unsupervised classification was applied in the hyper-spectral space of reflectance, leading to three spectrally distinct optical water types. The reflectance model was parameterized for the entire data set, and then parameterized for each of the water types. The three sets of type-specific model parameters, which define corresponding IOP submodels, are believed to accommodate differences in the optical properties of the in-water constituents. The parameterized reflectance model was evaluated by both reconstructing measured reflectance spectra and solving for the nonlinear inverse problem to retrieve in-water constituent concentrations. The model accuracy was significantly improved in the forward direction for classified waters over that of non-classified waters, but no significant improvement was achieved in the retrieval accuracy (inverse direction). A larger data set with greater resolution of constituent inherent optical properties would likely improve the modeling results.en_GB
dc.description.sponsorshipJRC.H.3-Global environement monitoringen_GB
dc.titleModeling Spectral Reflectance of Optically Complex Waters Using Bio-Optical Measurements from Tokyo Bayen_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Sustainable Resources

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