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We present a closure experiment between new inherent optical properties (IOPs: absorption a, scattering b, back-scattering b(b)) and apparent optical properties (AOPs: remote-sensing reflectance R-rs, irradiance reflectance R, and anisotropic factor at nadir Q(n)) data of Ionian and Adriatic seawaters, from very clear to turbid waters, ranging across one order of magnitude in R-rs. The internal consistency of the IOP-AOP matchups was investigated though radiative transfer closure. Using the in situ IOPs, we predicted the AOPs with the commercial radiative transfer solver Hydrolight. Closure was limited by two unresolved issues, one regarding processing of in situ data and the other related to radiative transfer modeling. First, different correction methods of the absorption data measured by the Wetlabs ac-s produced high variations in simulated reflectances, reaching 40% for the highest reflectances in our dataset. Second, the lack of detailed volume scattering function measurements forces us to adopt analytical functions that are consistent with a given particle backscattering ratio. The analytical phase functions named Fournier-Forand and two-term Kopelevich presented reasonable angular shapes with respect to measurements at a few backward angles. Between these phase functions, induced changes were within 4% for R-rs, within 11% for R, and within 10% for Q(n). Additionally, closure of Q(n) was generally not successful considering radiometric uncertainties. Simulated Q(n) overestimated low values and underestimated high values, especially at 665 nm, where Hydrolight was unable to predict measured Q(n) values greater than 6 sr. The physical nature of Q(n) makes this mismatch almost independent of the measured IOPs, thus precluding Q(n) tuning by varying the former. The non-closure of Q(n) might be caused by an inaccurate phase function and, to a lesser extent, by the modeling of the incoming radiance. For the future, this remains the task of accurate absorption and phase function measurements, especially at red wavelengths. (C) 2016 Optical Society of America

To address the challenges of the parameterization of ocean color inversion algorithms in optically complex waters, we present an adaptive implementation of the linear matrix inversion method (LMI) [J. Geophys. Res.101, 16631 (1996)10.1029/96JC01414], which iterates over a limited number of model parameter sets to account for naturally occurring spatial or temporal variability in inherent optical properties (IOPs) and concentration specific IOPs (SIOPs). LMI was applied to a simulated reflectance dataset for spectral bands representing measured water properties of a macrotidal embayment characterized by a large variability in the shape and amplitude factors controlling the IOP spectra. We compare the inversion results for the single-model parameter implementation to the adaptive parameterization of LMI for the retrieval of bulk IOPs, the IOPs apportioned to the optically active constituents, and the concentrations of the optically active constituents. We found that ocean color inversion with LMI is significantly sensitive to the a priori selection of the empirical parameters g0 and g1 of the equations relating the above-surface remote-sensing reflectance to the IOPs in the water column [J. Geophys. Res.93, 10909 (1988)10.1029/JD093iD09p10909]. When assuming the values proposed for open-ocean applications for g0 and g1 [J. Geophys. Res.93, 10909 (1988)10.1029/JD093iD09p10909], the accuracy of the retrieved IOPs, and concentrations was substantially lower than that retrieved with the parameterization developed for coastal waters [Appl. Opt.38, 3831 (1999)10.1364/AO.38.003831] because the optically complex waters analyzed in this study were dominated by particulate and dissolved matter. The adaptive parameterization of LMI yielded consistently more accurate inversion results than the single fixed SIOP model parameterizations of LMI. The adaptive implementation of LMI led to an improvement in the accuracy of apportioned IOPs and concentrations, particularly for the phytoplankton-related quantities. The adaptive parameterization encompassing wider IOP ranges were more accurate for the retrieval of bulk IOPs, apportioned IOPs, and concentration of optically active constituents.