Based on HYDROLIGHT simulations of more than 2000 reflectance spectra from datasets typical of coastal waters with highly variable optically active constituents as well as on intercomparisons with field measurements, the magnitude of chlorophyll fluorescence was analyzed and parameterized as a function of phytoplankton, CDOM, and suspended inorganic matter concentrations. Using the parameterizations developed, we show that variations in the fluorescence component of water leaving radiance in coastal waters are due more to the variability of attenuation in the water than to the variability of the fluorescence quantum yield, which we estimate to be relatively stable at around 1%. Finally, the ranges of water conditions where fluorescence plays a significant role in the reflectance NIR peak and where it is effectively undetectable are also determined.

Karenia brevis (K. brevis) blooms are of great interest and have been commonly reported throughout the Gulf of Mexico. In this study we propose a detection technique for blooms with low backscatter characteristics, which we name the Red Band Difference (RBD) technique, coupled with a selective K. brevis bloom classification technique, which we name the K. brevis Bloom Index (KBBI). These techniques take advantage of the relatively high solar induced chlorophyll fluorescence and low backscattering of K. brevis blooms. The techniques are applied to the detection and classification of K. brevis blooms from Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color measurements off the Gulf of Mexico. To assess the efficacy of the techniques for detection and classification, simulations, including chlorophyll fluorescence (assuming 0.75% quantum yield) based on K. brevis blooms and non-K. brevis blooms conditions were performed. These show that effective bloom detection from satellite measurements requires a threshold of RBD>0.15W/m²/µm/sr, corresponding to about $5mg/m^3$ of chlorophyll. Blooms can be detected at lower concentration by lowering the RBD threshold but false positives may increase. The classification technique is found most effective for thresholds of RBD>0.15W/m²/µm/sr and KBBI>0.3*RBD. The techniques were applied and shown to be effective for well documented blooms of K. brevis in the Gulf of Mexico and compared to other detection techniques, including FLH approaches. Impacts of different atmospheric corrections on results were also examined.

Retrieving the inherent optical properties of water from remote sensing multispectral reflectance measurements is difficult due to both the complex nature of the forward modeling and the inherent nonlinearity of the inverse problem. In such cases, neural network (NN) techniques have a long history in inverting complex nonlinear systems. The process we adopt utilizes two NNs in parallel. The first NN is used to relate the remote sensing reflectance at available MODIS-visible wavelengths (except the $678\mathrm{nm}$ fluorescence channel) to the absorption and backscatter coefficients at $442\mathrm{nm}$ (peak of chlorophyll absorption). The second NN separates algal and nonalgal absorption components, outputting the ratio of algal-to-nonalgal absorption. The resulting synthetically trained algorithm is tested using both the NASA Bio-Optical Marine Algorithm Data Set (NOMAD), as well as our own field datasets from the Chesapeake Bay and Long Island Sound, New York. Very good agreement is obtained, with $R^2$ values of 93.75%, 90.67%, and 86.43% for the total, algal, and nonalgal absorption, respectively, for the NOMAD. For our field data, which cover absorbing waters up to about $6{\mathrm{m}}^{-1}$, $R^2$ is 91.87% for the total measured absorption.

Retrieval of chlorophyll fluorescence magnitude using Fluorescence Height algorithms in coastal waters is more complicated than in the open ocean because of the strong deviations of elastic reflectance within the fluorescence band from the derived fluorescence baseline. We use results of our recently established relationship between fluorescence magnitude and concentrations of water constituents together with extensive HYDROLIGHT simulations, field and satellite data to analyze the performance and retrieval limitations of MODIS and MERIS FLH algorithms in the variety of coastal waters and to examine improvements for spectral band selection suitable for future sensors.

The Long Island Sound Coastal Observational platform (LISCO) near Northport, New York, has been recently established to support validation of ocean color radiometry (OCR) satellite data. LISCO is equipped with collocated multispectral, SeaPRISM, and hyperspectral, HyperSAS, above-water systems for OCR measurements. This combination offers the potential for improving validation activities of current and future OCR satellite missions, as well as for satellite intercomparisons and spectral characterization of coastal waters. Results of measurements made by both the multi and hyperspectral instruments, in operation since October 2009, are presented, evaluated and their associated uncertainties quantified based on observations for a period of over a year. Multi- and hyperspectral data processing as well as the data quality analysis are described and their uncertainties evaluated. The quantified intrinsic uncertainties of HyperSAS data exhibit satisfactory values, less than 5% over a large spectral range, from 340 to $740\mathrm{nm}$, and over a large range of diurnal daylight conditions, depending on the maximum sun elevation at the solar noon. Intercomparisons between HyperSAS and SeaPRISM data revealed that an overcorrection of the sun glint effect in the current SeaPRISM processing induces errors, which are amplified through the whole data processing, especially at the shorter wavelengths. The spectral-averaged uncertainties can be decomposed as follows: (i) sun glint removal generates 2% uncertainty, (ii) sky glint removal generates strong uncertainties of the order of 15% mainly induced by sun glint overcorrection, (iii) viewing angle dependence corrections improve the data intercomparison by reducing the dispersion by 2%, (iv) normalization of atmospheric effects generates approximately 4% uncertainty. Based on this study, improvements of the sun glint correction are expected to significantly reduce the uncertainty associated with the data processing down to the level of 1%. On the other hand, strong correlations between both datasets ($R^2>0.96$) demonstrate the efficacy of the above-water retrieval concept and confirm that the collocated instrumentation constitutes an important aid to above-water data quality analysis, which makes LISCO a key element of the AERONET-OC network.

We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678) needed for both the normalized fluorescence height (nFLH) and Red Band Difference algorithms (RBD) currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (a_ph443) using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The a_ph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved a_ph443 images are filtered by applying limiting constraints, defined by (i) low backscatter at Rrs 551 nm and (ii) a minimum a_ph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs.