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To solve the radiative transfer equation and relate inherent optical properties (IOPs) to apparent optical properties (AOPs), knowledge of the volume scattering phase function is required. Due to the difficulty of measuring the phase function, it is frequently approximated. We explore the sensitivity of derived AOPs to the phase function parameterization, and compare measured and modeled values of both the AOPs and estimated phase functions using data from Monterey Bay, California during an extreme "red tide" bloom event. Using in situ measurements of absorption and attenuation coefficients, as well as two sets of measurements of the volume scattering function (VSF), we compared output from the Hydrolight radiative transfer model to direct measurements. We found that several common assumptions used in parameterizing the radiative transfer model consistently introduced overestimates of modeled versus measured remote-sensing reflectance values. Phase functions from VSF data derived from measurements at multiple wavelengths and a single scattering single angle significantly overestimated reflectances when using the manufacturer-supplied corrections, but were substantially improved using newly published corrections; phase functions calculated from VSF measurements using three angles and three wavelengths and processed using manufacture-supplied corrections were comparable, demonstrating that reasonable predictions can be made using two commercially available instruments. While other studies have reached similar conclusions, our work extends the analysis to coastal waters dominated by an extreme algal bloom with surface chlorophyll concentrations in excess of 100 mg m -3 .

Atmospheric correction of visible/infrared spectra traditionally involves either (1) physics-based methods using Radiative Transfer Models (RTMs), or (2) empirical methods using in situ measurements. Here a more general probabilistic formulation unifies the approaches and enables combined solutions. The technique is simple to implement and provides stable results from one or more reference spectra. This makes empirical corrections practical for large or remote environments where it is difficult to acquire coincident field data. First, we use a physics-based solution to define a prior distribution over reflectances and their correction coefficients. We then incorporate reference measurements via Bayesian inference, leading to a Maximum A Posteriori estimate which is generally more accurate than pure physics-based methods yet more stable than pure empirical methods. Gaussian assumptions enable a closed form solution based on Tikhonov regularization. We demonstrate performance in atmospheric simulations and historical data from the "Classic" Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) acquired during the HyspIRI mission preparatory campaign.