Water-leaving Radiance Research Articles

Destriping algorithm for improved satellite-derived ocean color product imagery.

While modern multi-detector sensors offer a much improved image resolution and signal-to-noise ratio among other performance benefits, the multi-detector arrangement gives rise to striping in satellite imagery due to various sources, which cannot be perfectly corrected by sensor calibration. Recently, Bouali and Ignatov (2014) [J. Atmos. Oceanic Technol., 31, 150-163 (2014)] introduced a new approach to remove relatively small detector performance-related striping from thermal infrared bands for improved sea surface temperature data. We show that this methodology, with appropriately chosen parameters and adjustments, can also be applied to remove striping of a much larger variance from the solar reflective band data. Specifically, we modify and apply this new approach to remove striping from satellite-derived normalized water-leaving radiance spectra nLw(λ) obtained from solar reflective bands. It is important that the destriping approach not be applied to the top-of-atmosphere radiances. The results show a significant improvement in image quality for both nLw(λ) spectra and nLw(λ)-derived ocean biological and biogeochemical products such as chlorophyll-a concentration, and the water diffuse attenuation coefficient at the wavelength of 490 nm Kd(490).

On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing

Landsat-8 carries two separate sensors, namely the Operational Land Imager (OLI) and the Thermal Infrared Radiometer Suite (TIRS), that image the earth surface throughout the visible and thermal portions of the spectrum. Compared to Landsat heritage sensors, the OLI has enhanced features, which include its 12-bit radiometric resolution and the addition of a band centered at 443nm. The dramatically improved data quality/quantity expands existing applications of Landsat imagery in aquatic sciences from the retrieval of bio-geochemical properties, such as near-surface concentrations of chlorophyll-a (CHL) and total suspended solids (TSS), to benthic mapping. This study offers analysis of OLI's absolute radiometric performance over bodies of water using benchmark observations, namely the top-of-atmosphere (TOA) ocean color observations and marine in situ radiometric measurements. Sensor-to-sensor comparisons are performed to derive gain factors (g1) from near-concurrent observations in TOA radiance and reflectance domains. The gains in the radiance domain were further validated/adjusted by determining a second set of gains (g2) via analysis of OLI-derived water-leaving radiance, i.e., Lw(λ), against in situ measurements made either at the Ocean Color AErosol RObotic NETwork (AERONET-OC) sites or during field campaigns. The analyses yield the OLI calibration uncertainties that need to be accounted for when studying aquatic environments. It was found that, for the visible and near-infrared channels, the OLI radiometric responses, on average, are well in agreement (<2% discrepancies) with the TOA radiances estimated by ocean color satellites or those predicted by models based on measurements of aquatic and atmospheric properties. However, the TOA radiance at the new 443-nm band is found to be, on average, 3.4% larger than the reference observations. The inter-sensor comparisons in the reflectance domain, however, indicated slightly different results with the OLI responses being low in the blue bands. To enhance the retrieval accuracy of aquatic-science products from OLI datasets, sets of temporally averaged gains (radiance and reflectance) are derived and recommended for use prior to the retrieval of in-water products.

Radiometric calibration of ocean color satellite sensors using AERONET-OC data.

Radiometric vicarious calibration of ocean color (OC) satellite sensors is carried out through the full sunlight path radiative transfer (RT) simulations of the coupled ocean-atmosphere system based on the aerosol and water-leaving radiance data from AERONET-OC sites for the visible and near-infrared (NIR) bands. Quantitative evaluation of the potential of such approach for achieving the radiometric accuracies of OC satellite sensors is made by means of direct comparisons between simulated and satellite measured top of atmosphere (TOA) radiances. Very high correlations (R ≥ 0.96 for all visible channels) are achieved for the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor when this approach is applied with the data from the LISCO and WaveCIS AERONET-OC sites. Vicarious calibration gain factors derived with this approach are highly consistent, with comparisons between the two sites exhibiting around 0.5% discrepancy in the blue and green parts of the spectrum, while their average temporal variability is also within 0.28% - 1.23% permitting the approach to be used, at this stage, for verification of sensor calibration performance.

Observation of a turbid plume using MODIS imagery: The case of Douro estuary (Portugal)

The Douro is one of the longest rivers in the Iberian Peninsula and represents the most important freshwater input into the Atlantic Ocean in the northwestern Portuguese coast. In order to fill the gaps on the comprehension of the spatial variability of the Douro estuarine plume, an innovative methodology was applied to observe and characterize this plume and its spatial and temporal variability. Using images of normalized water-leaving radiance at 555nm from MODIS (Moderate Resolution Imaging Spectroradiometer) in combination with long-term concurrent wind, tidal and river discharge data (2003–2011), this work evaluates the effect of the main drivers (river discharge, wind and tide) on the plume features. Expected strong relationships between river discharge and turbid levels were detected. A circular pattern is well distinct off Douro estuary when the flow exceeds 500m3s−1. Under upwelling favorable winds and high river discharge regime, an offshore expansion is observed. During southerly winds, there is a plume confinement to the coast. Easterly winds produce a bulge formation with an offshore detachment from the coast while westerly winds tend to confine freshwater close to the shoreline and decrease the cross-shore advection. Under high river discharges, a remarkable tidal influence in the near-field regions is observed close to the coast and near the river mouth.

Improved near-infrared ocean reflectance correction algorithm for satellite ocean color data processing.

A new approach for the near-infrared (NIR) ocean reflectance correction in atmospheric correction for satellite ocean color data processing in coastal and inland waters is proposed, which combines the advantages of the three existing NIR ocean reflectance correction algorithms, i.e., Bailey et al. (2010) [Opt. Express18, 7521 (2010)Appl. Opt.39, 897 (2000)Opt. Express20, 741 (2012)], and is named BMW. The normalized water-leaving radiance spectra nLw(λ) obtained from this new NIR-based atmospheric correction approach are evaluated against those obtained from the shortwave infrared (SWIR)-based atmospheric correction algorithm, as well as those from some existing NIR atmospheric correction algorithms based on several case studies. The scenes selected for case studies are obtained from two different satellite ocean color sensors, i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP), with an emphasis on several turbid water regions in the world. The new approach has shown to produce nLw(λ) spectra most consistent with the SWIR results among all NIR algorithms. Furthermore, validations against the in situ measurements also show that in less turbid water regions the new approach produces reasonable and similar results comparable to the current operational algorithm. In addition, by combining the new NIR atmospheric correction with the SWIR-based approach, the new NIR-SWIR atmospheric correction can produce further improved ocean color products. The new NIR atmospheric correction can be implemented in a global operational satellite ocean color data processing system.

Atmospheric correction based on inherent optical properties of sea water at NIR wavelengths combined with an automated aerosol spectra determination (ASD) technique

Atmospheric correction is the process whereby atmospheric effects on sensor-recorded radiance are removed and the surface radiance is estimated. Atmospheric effects due to gaseous absorption, molecular scattering – and their seasonal as well as latitudinal variations – can be adequately accounted for by using pre-computed look-up tables. However, scattering by aerosol particles is difficult to correct. At-sensor radiance at near-infrared (NIR) wavelengths, after being corrected for gaseous absorption and molecular scattering, was assumed (in standard atmospheric correction) to have been entirely due to aerosol scattering and was used to calculate the aerosol parameters. This assumption, although valid for open ocean clear waters, is not valid for turbid waters due to scattering by suspended particles in the water, which results in an appreciable amount of water-leaving radiance in the NIR region. A new turbid water atmospheric correction scheme is described here for Oceansat-2 Ocean Colour Monitor (OCM-2) data based on inherent optical properties (IOPs) of sea water at NIR, and obtaining an accurate spectral profile of aerosol radiance.

Remote Estimation of Chlorophyll-a in Inland Waters by a NIR-Red-Based Algorithm: Validation in Asian Lakes

Satellite remote sensing is a highly useful tool for monitoring chlorophyll-a concentration (Chl-a) in water bodies. Remote sensing algorithms based on near-infrared-red (NIR-red) wavelengths have demonstrated great potential for retrieving Chl-a in inland waters. This study tested the performance of a recently developed NIR-red based algorithm, SAMO-LUT (Semi-Analytical Model Optimizing and Look-Up Tables), using an extensive dataset collected from five Asian lakes. Results demonstrated that Chl-a retrieved by the SAMO-LUT algorithm was strongly correlated with measured Chl-a (R2 = 0.94), and the root-mean-square error (RMSE) and normalized root-mean-square error (NRMS) were 8.9 mg∙m−3 and 72.6%, respectively. However, the SAMO-LUT algorithm yielded large errors for sites where Chl-a was less than 10 mg∙m−3 (RMSE = 1.8 mg∙m−3 and NRMS = 217.9%). This was because differences in water-leaving radiances at the NIR-red wavelengths (i.e., 665 nm, 705 nm and 754 nm) used in the SAMO-LUT were too small due to low concentrations of water constituents. Using a blue-green algorithm (OC4E) instead of the SAMO-LUT for the waters with low constituent concentrations would have reduced the RMSE and NRMS to 1.0 mg∙m−3 and 16.0%, respectively. This indicates (1) the NIR-red algorithm does not work well when water constituent concentrations are relatively low; (2) different algorithms should be used in light of water constituent concentration; and thus (3) it is necessary to develop a classification method for selecting the appropriate algorithm.

Retrieval of vertical particle concentration profiles by optical remote sensing: a model study.

Water-leaving radiance is subject to depth variability of the water constituents. The optical penetration depth is strongly dependent on the wavelength λ, which allows to retrieve a non-uniform vertical profile of an optically-active constituent CTSM(z) from remote-sensing reflectance Rrs(λ,Cz). We define the apparent particle concentration CTSM,app(λ) of a vertically homogeneous water column whose Rrs(λ,Cconst) matches Rrs(λ,Cz). Subsequently, we define a vertically-weighted averaged particle concentration CTSM,ave(λ), only dependent on CTSM(z), and retrieve CTSM(z) by minimizing the error between CTSM,app(λ) and CTSM,ave(λ) with genetic algorithms. We conclude that the retrieval is excellent if the sub-surface maximum lays close to the surface or the background concentration of CTSM(z) is low. Conversely, results worsen for opposite conditions, due to insufficient signal strength from superimposed sub-surface maxima.

A new theory and its application to remove the effect of surface-reflected light in above-surface radiance data from clear and turbid waters

Water-leaving radiances (Lw) measured from the deck of a ship or boat in oceanic and lake waters are widely and operationally used for satellite sensor vicarious calibration and validation and development of remote-sensing algorithms to understand interdisciplinary coastal ocean properties and processes. However, accurate determination of Lw remains to be a challenging issue because of the limitations of the existing methods to accurately remove the undesired signal (surface-reflected light of the sky and sun) from above-surface measurements of the total upwelling radiance leaving the water surface. In this study, a new theory is developed and applied to the above-surface radiometric data measured from clear, turbid and eutrophic waters. The new method effectively removes surface-reflected contributions from the total upwelling radiance signal under different sky (clear sky to overcast sky) and sun glint conditions. The Lw spectra obtained from the above-surface radiance data using the new method are found to match well with those extrapolated from the upwelling radiances (Lu) measured with another set of underwater radiometers (used just below the sea surface). The new method proves to be a viable alternative, especially in circumstances when the above-surface measurements of radiances are severally contaminated by the surface-reflected light fields. Since spectral radiance measurements are also sensitive to the observation angles, and to the magnitude of the radiometer׳s solid angle field of view, above-surface radiances are also measured for different viewing angles in highly eutrophic waters. Such measurements show large deviations in Lw spectra except at lower viewing angles (30°). When applied to these data, the new method eliminates the undesired signal encountered at higher viewing angles and delivers accurate water-leaving radiance data. These results suggest that the new method is capable of removing the surface-reflected light fields from both time series and discrete radiometric data in a wide range of oceanic, coastal and inland water bodies under different sky and sun glint conditions.

A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: a case study for the Pearl River Estuary

The algorithms for atmospheric correction (AC) of Moderate Resolution Imaging Spectroradiometer (MODIS) data may yield negative water-leaving radiances in highly turbid coastal waters along the Pearl River Estuary of China. This failure of AC can be attributed to the high reflectance from the suspended sediments in the near-infrared (NIR) bands, which results in an overestimate of the aerosol scattering and hence overcorrection in the visible bands. In this letter, a new shortwave infrared extrapolation (SWIRE) method is presented for Terra-MODIS data. The Rayleigh-corrected reflectances in the SWIR bands (1.24, 1.64, and 2.13 μm) are used to determine an exponential function with respect to wavelength, which is used to correct the NIR bands (0.748 and 0.869 μm) for sediment scattering and hence estimate the aerosol scattering reflectances in these bands. Then the Gordon and Wang AC approach is applied. The performance of this new SWIRE algorithm is demonstrated using two examples centred on the Pearl River Estuary, with the results showing that the new approach can improve the AC for these turbid waters.

Ocean reflectance spectra at the red, near‐infrared, and shortwave infrared from highly turbid waters: A study in the Bohai Sea, Yellow Sea, and East China Sea

Normalized water‐leaving radiance spectra nLw(λ) at the red, near‐infrared (NIR), and shortwave infrared (SWIR) are quantified and characterized in highly turbid waters of the western Pacific using 3 yr (2009–2011) observations from the Moderate Resolution Imaging Spectroradiometer on the satellite Aqua. nLw(645; red), nLw(859; NIR), and nLw(1240; SWIR) were higher in the coastal region and river estuaries, with SWIR nLw(1240) reaching up to ∼ 0.2 mW cm−2 µm−1 sr−1 in Hangzhou Bay during winter. The NIR ocean‐reflectance spectral shape represented by the ratio of the normalized water‐leaving reflectance ρwN(λ) at the two NIR bands ρwN(748) : ρwN(869) is highly dynamic and region‐dependent. The NIR spectral feature associated with the sediment source from the Yellow River and Ancient Yellow River is noticeably different from that of the Yangtze River. There are non‐negligible SWIR nLw(1240) contributions for waters with the NIR nLw(859) &gt; ∼ 2.5 mW cm−2 µm−1 sr−1. Estimation of the NIR ocean reflectance with iterative approaches might only be accurate for turbid waters with nLw(859) &lt; ∼ 1.5 mW cm−2 µm−1 sr−1. Thus, the SWIR atmospherics correction algorithm for satellite ocean‐color data processing is indispensable to derive accurate nLw(λ) for highly turbid waters. Current existing satellite algorithms for chlorophyll a, diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), total suspended matter, and inherent optical properties (IOPs) using nLw(λ) at the red band for coastal waters are limited and can only be applied to turbid waters with nLw(859) &lt; ∼ 1.5 mW cm−2 µm−1 sr−1. Thus, the NIR nLw(λ) measurements are required to characterize water properties for highly turbid waters. Based on the fact that pure water absorption is significantly larger than other absorption components in the NIR wavelengths, we show that it is feasible to analytically derive accurate IOP data for turbid waters with combined satellite‐measured visible‐NIR nLw(λ) spectra data.

A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters

Atmospheric correction of satellite ocean color imagery in sediment-laden and algal bloom waters remains to be a challenging task because of the inefficiency of present methods to accurately assess aerosol radiances in the near-infrared (NIR) bands and extrapolate these into the visible spectrum. This study presents a novel method for estimation and extrapolation of aerosol radiances in MODIS–Aqua bands over optically complex oceanic waters dominated by suspended sediments and algal blooms. The Rayleigh-corrected radiance at 748nm, after correcting the influence of the elevated radiance contributed by suspended sediments and algal blooms, is employed in the present method to estimate aerosol radiance in a MODIS–Aqua band centered at 531nm. Then, the normalized Gaussian distribution function is used to extrapolate aerosol radiance over the entire wavebands (from 412 to 748nm). This method retrieves water-leaving radiances Lw(λ) nearly comparable to those of the NIR scheme for extremely clear waters (chlorophyll <0.15mgm−3) with an error of <5% which is an acceptable limit for such waters. Evaluation of the performance of the new method with NIR and NIR–SWIR switching schemes using in-situ Lw(λ) measurements from highly turbid waters off Point Calimere (on the southeast coast of India) and other regional waters (NOMAD in-situ Lw(λ) data) shows an apparent improvement in Lw(λ) retrievals with the new method over NIR and NIR–SWIR switching schemes (i.e., with a decrease in mean relative error by 22.59% and 23.06% respectively). The performance of the new method is demonstrated for several MODIS-Aqua imageries of optically contrasted and complex waters (containing different in-water and floating algal blooms), and yields physically realistic water-leaving (or surface-leaving) radiances in all visible bands. By contrast, the NIR and NIR–SWIR switching schemes show large distortions in Lw(λ) structures with large negative values across the visible bands. These results suggest that the new method will have important implications for satellite remote sensing of optically complex waters, since the accuracy of bio-geophysical products derived from such data mainly depends on the water-leaving radiance products delivered by the atmospheric correction algorithm.

FLUORESCENCE PROPERTIES OF MECHANICALLY DISPERSED CRUDE OIL

Despite of numerous actions undertook by the global and local authorities towards protection of marine environment, oil pollution loads into the Baltic Sea still tend to increase and need to be continuously estimated in order to apply the legal regulations. There is a demand for multifarious studies on the environmental effects of oil products. Dispersed oil droplets occur in seawater as the result of contaminated river inflows, bilge water discharges and as the consequence of mechanical and chemical dispersion of oil spills. Their optical properties depend on oil type, concentration and size distribution. Oil content influences many environmental factors, like water quality and bio-optical parameters (e.g. water-leaving radiance, inherent optical properties, seawater fluorescence). We present a unique study of the collected database of crude oil fluorescence spectra for better understanding the correlations between oil optical properties and its concentration, as well as for evaluation of the oil droplets size by the application of vacuum filtering using three filters of different permeability. Fluorescence spectra have been registered for oil-in-water emulsion samples prepared in the laboratory by mechanical dispersion. We discuss the optical properties of crude oil and the relationships between them in the context of potential remote detection of dispersed oil in seawater.

Assessment of AERONET-OC LWN uncertainties

This study presents a detailed analysis of the uncertainties affecting the normalized water-leaving radiance (LWN) from above-water measurements performed within the context of the Ocean Colour component of the Aerosol Robotic Network (AERONET-OC). The analysis, conducted in agreement with the ‘Guide to the Expression of Uncertainty in Measurement’ (GUM), indicates uncertainties of LWN markedly dependent on the optical properties of seawater for a number of AERONET-OC sites located in different marine regions. Results obtained for the Adriatic Sea site, characterized by a large variety of measurement conditions, confirm previous uncertainties from an independent study indicating median values of relative combined uncertainties of 5% in the blue–green part of the spectrum and of approximately 7% in the red. Additional investigations show that the former uncertainties can be reduced by ∼1% when restricting the determination of AERONET-OC LWN to measurements performed at low sun zenith angle and low aerosol optical thickness.

Remote Sensing of Water Optical Property for China's Inland Lake Taihu Using the SWIR Atmospheric Correction With 1640 and 2130 nm Bands

Using the shortwave infrared (SWIR) atmospheric correction algorithm with 1640 and 2130 nm bands, water optical property data for China's inland freshwater Lake Taihu have been derived from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. Using MODIS-Aqua measurements from 2002 to 2010, seasonal and spatial distributions of the normalized water-leaving radiance nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (λ) spectra from visible to the near-infrared (NIR) and SWIR (1240 nm) are derived and used for study and characterization of Lake Taihu water optical properties. In particular, for the first time, spatial and seasonal variations of nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (λ) at the SWIR 1240 nm (nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (1240)) are derived for the lake and show some different features from those of the red and NIR nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (λ) (nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (645) and nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (859)). Time series of monthly MODIS-derived nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (λ) spectra for Lake Taihu are obtained and analyzed, showing important seasonal and interannual variations. The results indicate that the SWIR nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (1240) contributions in the lake are mainly due to the presence of significant amounts of algae, while the red and NIR nL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> (λ) variations result from changes of total suspended sediment (TSS) amounts in the water column. Furthermore, this study shows that all three SWIR bands at 1240, 1640, and 2130 nm are useful and required for satellite water quality remote sensing for extremely turbid near-shore and inland waters.

Satellite-derived suspended particulates in the Saint Lawrence Estuary: uncertainties due to bottom effects

Historical regional budgets of suspended particulate matter (SPM) in surface waters (i.e., 0–20 m depth) of the St. Lawrence Estuary (SLE) are not very accurate because of the lack of in situ measurements. The aim of this study was to improve the accuracy of SPM budgets in the SLE based on spaceborne ocean color measurements by investigating one kind of uncertainty on satellite-derived SPM concentration and related with the influence of the bottom depth and (or) bottom reflectivity (hereafter bottom effects) on water-leaving radiance. Theoretical results suggest that bias on optically derived SPM concentration due to bottom effects is greater in the lower estuary and can be up to 36.6% when the water depth is ≤10 m, sediments are dominated by sand, and water turbidity is relatively low (beam attenuation coefficient <1 m−1). Sea-Viewing Wide Field of view Sensor measurements and radiative transfer simulations suggest that bottom effects may introduce an uncertainty of 3.4% or 1.1 × 103 tonnes on 1 m vertically integrated SPM mass over the whole estuary. This error increases (up to 14.5% or 2.9 × 103 tonnes) when optically shallow areas were excluded from the regional SPM budget analysis.

Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: Application to the detection of phytoplankton groups in open ocean waters

Remote sensing of ocean color is a powerful tool for monitoring phytoplankton in the ocean with a high spatial and temporal resolution. Several methods were developed in the past years for detecting phytoplankton functional types from satellite observations. In this paper, we present an automatic classification method based on a neural network clustering algorithm in order to classify the anomalies of water leaving radiance spectra (Ra), introduced in the PHYSAT method by Alvain et al. (2005), and analyze their variability at the global scale. The use of an unsupervised classification aims at improving the characterization of the spectral variability of Ra in terms of shape and amplitude as well as the expansion of its potential use to larger in situ datasets for global phytoplankton remote sensing. The Self-Organizing Map algorithm (SOM, Kohonen, 1984) aggregates similar spectra into a reduced set of pertinent groups, allowing the characterization of the Ra variability, which is known to be linked with phytoplankton community composition (Alvain et al., 2012).Based on the same sample of Ra spectra, a comparison between the previous version of PHYSAT (Alvain et al., 2005, 2008) and the new one using SOM shows that it is now possible to take into consideration all the types of spectra. This was not possible with the previous approach, based on thresholds, defined in order to avoid overlaps between the spectral signatures of each phytoplankton group. The SOM-based method is relevant for characterizing a wide variety of Ra spectra through its ability to handle large amounts of data, in addition to its statistical reliability and processing speed compared to the previous PHYSAT. The former approach might have introduced potential biases and thus, its extension to larger databases was very restricted. This is not the case with the new statistical design presented in this work.In the second step, some new Ra spectra have been related to phytoplankton groups using collocated field pigment inventories from a large in situ database. Phytoplankton groups were identified based on biomarker pigment ratio thresholds taken from the literature. SOM was then applied to the global daily SeaWiFS (Sea-viewing Wide Field-of-view Sensor) imagery archive between 1997 and 2010. Global distributions of major phytoplankton groups were analyzed and validated against in situ data. Thanks to its ability to capture a wide range of spectra and to manage a larger in situ pigment dataset, the neural network tool allows to classify a much higher number of pixels (2 times more) than the previous PHYSAT method for the five phytoplankton groups taken into account in this study (Synechococcus-like-cyanobacteria, diatoms, Prochlorococcus, Nanoeucaryotes and Phaeocystis-like). In addition, different Ra spectral signatures have been associated to diatoms. These signatures are located in various environments where the inherent optical properties affecting the Ra spectra are likely to be significantly different. Local phenomena such as diatom blooms in the upwelling regions or during climatic events (i.e. La Niña) are more clearly visible with the new method.

Evaluation of the VIIRS ocean color monitoring performance in coastal regions

Ocean color (OC) remote sensing has entered a new phase with the successful deployment of the Visible Infrared Imager Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The representativeness and accuracy of the VIIRS geophysical products need to be assessed before a wide use of these data by the scientific community. As an integral part of the VIIRS sensor calibration and validation efforts, our group has been continuously monitoring the validity of the VIIRS's OC and atmospheric data stream through time-series in-situ data acquired at the observatory sites which are part of the AERONET-OC network. This paper addresses the preliminary evaluations of the VIIRS sensor's performance for retrieving OC data of typical coastal water environments, by carrying out time-series, as well as qualitative and quantitative match-up comparisons analysis between in-situ and satellite retrieved OC data. Initial time-series match-up comparisons carried out for a year period (January to December, 2012) show that VIIRS data exhibits strong temporal and statistical agreements with AERONET-OC data demonstrating a potential in enhanced coastal water monitoring from space. VIIRS data of two NASA-OBPG processing schemes which apply different vicarious calibration gains and NOAA-IDPS system are analyzed based on in-situ data of LISCO and WaveCIS AERONET-OC sites which are located in Long Island Sound and Gulf of Mexico respectively as well as OC retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Aqua satellite. The underlying cause of the discrepancies observed in VIIRS retrieved normalized water-leaving radiances is also investigated. Finally, as the NASA-OBPG and NOAA-IDPS processing schemes for ocean color data of the VIIRS sensor continue to evolve, the results underline the necessity for monitoring and assessing the validity and consistency of VIIRS' ocean color products, especially for coastal waters.

Tidal effects on ecosystem variability in the Chesapeake Bay from MODIS-Aqua

Eight-year ocean color observations between 2002 and 2009 from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the satellite Aqua are used to quantitatively assess the tidal effects on variability of ocean's optical, biological, and biogeochemical properties in the Chesapeake Bay. We demonstrate that tidal variation in the lower Chesapeake Bay is one of the important ocean processes that drive significant changes of the ocean's optical and biogeochemical properties. Normalized water-leaving radiance spectra (nLw(λ)), water diffuse attenuation coefficient at the wavelength of 490nm (Kd(490)), and total suspended sediment (TSS) concentration show significant tidal variations in the lower Chesapeake Bay. Indeed, in the lower Bay region, Kd(490) increases from 0.9m−1 under low tidal current conditions at 0.10–0.15m/s to 1.35m−1 under high tidal current conditions at 0.45–0.50m/s, while chlorophyll-a concentration in the lower Bay region decreases slightly with an increase of the tidal current. Tide-driven variability in Kd(490) and TSS in the lower Bay is similar to and slightly larger than the seasonal variability in the region. On the other hand, different from the variability in the lower Bay, the tidal effects on the ecosystem are small and negligible in the middle and a large part of upper Chesapeake Bay regions.

OSABT: An Innovative Algorithm to Detect and Characterize Ocean Surface Algal Blooms

Ocean surface algal blooms (OSABs) are an emerging and progressively more important phenomenon in many regional waters. Current techniques perhaps delineate the spatial distributions of OSABs from the above-atmosphere spectral radiances or Raleigh corrected radiances, so far optical spectral characterization of the OSABs has been impossible due to the inability of the atmospheric correction algorithm to account for their enhanced radiances at the near-infrared (NIR) bands. An innovative algorithm, referred as the “OSABT”, is developed to provide essential data for detection and optical spectral characterization of OSABs in coastal and open ocean waters. The algorithm is applied to MODIS-Aqua imagery from the Arabian Sea and its results are systematically studied with regard to the spectral characteristics of OSABs, its evolution stages, sub-pixel variability, and atmospheric correction issues. The effectiveness of the OSABT is tested for several other regional waters on MODIS-Aqua data, where spatial bloom characteristics are previously known, and their optical spectra are featured with enhanced radiance at the NIR bands and reduced radiance at the red bands. The spectral signatures differ for each OSABs, and correlate to their evolution stages as well. These results support future applicability of this algorithm to derive the water-leaving radiance and new indices from the OSAB pixels for qualitative and quantitative assessment of the optical and biological OSAB properties as well as their associated ecological activities (e.g., growth, aggregation, density and impacts). The advantage of the OSABT is that it can be applied rather generally to other satellite-based sensors with no short-wave infrared bands, provided that suitable radiometric calibration coefficients are determined for using the OSABT.