A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (R_rs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured R_rs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

Generation of random sea surfaces using wave variance spectra and Fourier transforms is formulated in a way that guarantees conservation of wave energy and fully resolves wave height and slope variances. Monte Carlo polarized ray tracing, which accounts for multiple scattering between light rays and wave facets, is used to compute effective Mueller matrices for reflection and transmission of air- or water-incident polarized radiance. Irradiance reflectances computed using a Rayleigh sky radiance distribution, sea surfaces generated with Cox–Munk statistics, and unpolarized ray tracing differ by 10%–18% compared with values computed using elevation- and slope-resolving surfaces and polarized ray tracing. Radiance reflectance factors, as used to estimate water-leaving radiance from measured upwelling and sky radiances, are shown to depend on sky polarization, and improved values are given.

Accurate calculation of underwater light is fundamental to predictions of upper-ocean heating, primary production, and photo-oxidation. However, most ocean models simulating these processes do not yet incorporate radiative transfer modules for their light calculations. Such models are often driven by above-surface, broadband, daily averaged irradiance or photosynthetically available radiation (PAR) values obtained from climatology or satellite observations, sometimes without correction for sea-surface reflectance, even though surface reflectance can reduce in-water values by more than 20%. We present factors computed by a radiative transfer code that can be used to convert above-surface values in either energy or quantum units to in-water net irradiance, as needed for calculations of water heating, and to in-water PAR, as needed for calculations of photosynthesis and photo-oxidation.

Ocean ecosystem models require accurate calculations of both hydrodynamics and biology; those calculations in turn require accurate calculation of in‐water irradiance. Ecosystem models now achieve great accuracy in their hydrodynamical predictions, and the biological modules are becoming correspondingly sophisticated. The optical calculations are however often oversimplified, to the possible detriment of the physical and biological predictions. We used a recently developed, extremely fast radiative transfer code, EcoLight‐S, to study differences in ecosystem and thermal development in an idealized upwelling‐downwelling system when simple versus accurate irradiance calculations are used. The use of accurate irradiances gave up to 57% differences in chlorophyll concentrations after two weeks of simulated time, compared to predictions based on irradiances obtained using a simple exponential attenuation formula. Accurate irradiance calculations increased sea surface temperatures and decreased temperatures at depth, leading to increased stratification. Use of EcoLight‐S couples the physical and biological calculations so that biology feeds back to physics, and vice versa. EcoLight‐S outputs ancillary quantities such as remote sensing reflectance and in‐water spectral irradiance, which can be used to validate ecosystem predictions using remotely sensed ocean color imagery or optical measurements from buoys or gliders, without the need to convert such measurements to chlorophyll values. After optimization, the ecosystem model total run times with EcoLight‐S were less than 20% more than for the analytical irradiance models. We also found that the use of 24 h average irradiances gave factor‐of‐two differences in chlorophyll concentrations compared to the use of a diel irradiance pattern with the same 24 h average value.

Ocean physical-biological-optical ecosystem models can require light calculations at thousands of grid points and time steps. Implicit inverse models that recover ocean absorption and scattering properties from measured light variables can require thousands of solutions of the radiative transfer equation. An extremely fast radiative transfer code, EcoLight-S(ubroutine), has been developed to address these needs. EcoLight-S requires less than one second on a moderately fast computer to compute spectral irradiances over near-ultraviolet to near-infrared wavelengths with errors in the photosyntheically available radiation (PAR) of no more than ten percent throughout the euphotic zone. It is thus possible to replace simple and often inaccurate analytical PAR or spectral irradiance models with more accurate radiative transfer calculations, with very little computational penalty. EcoLight-S is applicable to Case 2 and optically shallow waters for which no analytical light models exist. EcoLight-S also computes upwelling and downwelling plane irradiances, nadir and zenith radiances, and the remote-sensing reflectance. These quantities allow ecosystem predictions to be validated with optical measurements obtained from in-water instruments or remotely sensed imagery.