Modeling spectral reflectance of optically complex waters using bio-optical measurements from Tokyo Bay

Abstract This 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.

[1]  E. Ann Gallie,et al.  Specific absorption and backscattering spectra for suspended minerals and chlorophyll-a in chilko lake, British Columbia , 1992 .

[2]  K. Carder,et al.  Marine humic and fulvic acids: Their effects on remote sensing of ocean chlorophyll , 1989 .

[3]  R. Bukata,et al.  Optical Properties and Remote Sensing of Inland and Coastal Waters , 1995 .

[4]  Howard R. Gordon,et al.  Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery , 1983, Lecture Notes on Coastal and Estuarine Studies.

[5]  L. Prieur,et al.  Analysis of variations in ocean color1 , 1977 .

[6]  M. DeGrandpre,et al.  Seasonal variation of CDOM and DOC in the Middle Atlantic Bight: Terrestrial inputs and photooxidation , 1997 .

[7]  David A. Siegel,et al.  Variability in near-surface particulate absorption spectra: What can a satellite ocean color imager see? , 1994 .

[8]  Janet W. Campbell,et al.  On the seasonal correlation of surface particle fields with wind stress and Mississippi discharge in , 2004 .

[9]  H. Gordon,et al.  Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review , 1983 .

[10]  H. Claustre,et al.  Variability in the chlorophyll‐specific absorption coefficients of natural phytoplankton: Analysis and parameterization , 1995 .

[11]  L. Prieur,et al.  Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains1 , 1981 .

[12]  Motoaki Kishino,et al.  Estimation of the spectral absorption coefficients of phytoplankton in the sea , 1985 .

[13]  D. Siegel,et al.  Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea , 1997 .

[14]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[15]  Stéphane Maritorena,et al.  Optimization of a semianalytical ocean color model for global-scale applications. , 2002, Applied optics.

[16]  Hui Feng,et al.  A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms , 2001, IEEE Trans. Geosci. Remote. Sens..

[17]  E. Fry,et al.  Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements. , 1997, Applied optics.

[18]  B. Mitchell,et al.  A chlorophyll-dependent semianalytical reflectance model derived from field measurements of absorption and backscattering coefficients within the Southern Ocean , 2001 .

[19]  R. Maffione,et al.  Instruments and methods for measuring the backward-scattering coefficient of ocean waters. , 1997, Applied optics.

[20]  P. Kowalczuk.  Seasonal variability of yellow substance absorption in the surface layer of the Baltic Sea , 1999 .

[21]  C. Davis,et al.  Model for the interpretation of hyperspectral remote-sensing reflectance. , 1994, Applied optics.

[22]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[23]  M. Chami,et al.  Inversion of oceanic constituents in case I and II waters with genetic programming algorithms. , 2002, Applied optics.

[24]  M. Perry,et al.  In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance , 1995 .

[25]  H. Gordon,et al.  Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. , 1975, Applied optics.

[26]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[27]  H. Gordon,et al.  Simultaneous retrieval of oceanic and atmospheric parameters for ocean color imagery by spectral optimization: a validation , 2003 .

[28]  C. Whitlock,et al.  Comparison of reflectance with backscatter and absorption parameters for turbid waters. , 1981, Applied optics.

[29]  R. Bukata,et al.  Estimation of Organic and Inorganic Matter in Inland Waters: Optical Cross Sections of Lakes Ontario and Ladoga , 1991 .

[30]  John T. O. Kirk,et al.  Dependence of relationship between inherent and apparent optical properties of water on solar altitude , 1984 .

[31]  Marcel Babin,et al.  Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio-optical models , 1998 .

[32]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .