Algorithms to retrieve the spectral diffuse attenuation coefficient of light in the ocean from remote sensing.

We recently found a significant bias between spectral diffuse attenuation coefficient (Kd(λ)) retrievals by common ocean color algorithms and measurements from profiling floats [Remote. Sens.14, 4500 (2022)10.3390/rs14184500]. Here we show, using a multi-satellite match-up dataset, that the bias is markedly reduced by simple "tuning" of the algorithm's empirical coefficients. However, while the float dataset encompasses a larger proportion of the ocean's variability than previously used datasets, it does not cover the whole range of variability of observed remote sensing reflectance (Rrs). Thus, using algorithms tuned to this more comprehensive dataset may still result in a temporal and/or geographical bias in global application. To address this generalization issue, we evaluated a variety of analytical algorithms based on radiative transfer theory and settled on a specific one. This algorithm computes Kd(λ) from inherent optical properties (IOPs) obtained from an Rrs inversion and information about the angular distribution of the radiance transmitted through the air/ocean interface. The resulting Kd(λ) estimates at 412 and 490 nm were not appreciably biased against the float measurements. Evaluation using other in-situ datasets and radiative transfer simulations was also satisfactory. Statistical performance was good in both clear and turbid waters. Further work should be conducted to examine whether the tuned algorithms and/or the new analytical algorithm demonstrate adequate hyperspectral performance.

Evaluation of the NASA OBPG MERIS ocean surface PAR product in clear sky conditions.

The operational MEdium Resolution Imaging Spectrometer (MERIS) daily mean photosynthetically available radiation (PAR) product generated by the NASA Ocean Biology Processing Group (OBPG) was evaluated in clear sky conditions against in-situ measurements at various sites in the northwestern Mediterranean Sea (BOUSSOLE buoy), the northwestern Pacific (CCE-1 and -2 moorings), and the northeastern Atlantic (COVE platform). The measurements were first checked and corrected for calibration errors and uncertainties in data processing by comparing daily means for clear days (i.e., no clouds from sunrise to sunset and low aerosol abundance) with theoretical values from an accurate Monte Carlo radiative transfer code. The OBPG algorithm performed well when sky was completely cloudless during daytime, with a bias of 0.26 E/m2/d (0.6%) and a RMS difference of 1.7 E/m2/d (4.0%). Using satellite-derived aerosol optical thickness (AOT) and Angström coefficient instead of climatology slightly degraded the results, which was likely due to uncertainties in the aerosol retrievals. A sensitivity study to aerosol properties indicated that climatology may not work in some situations (e.g., episodic dust, pollution, or biomass burning events), suggesting that it is best to use actual aerosol estimates in clear sky conditions. The analysis also revealed that specifying aerosol properties, therefore atmospheric transmittance, from AOT and Angström coefficient, even retrieved from the satellite imagery, may not be sufficient in the presence of absorbing aerosols, especially when loadings are important. Performance was degraded when including situations of clear sky at the time of the MERIS observation but cloudy sky before and/or after overpass, resulting in a bias (overestimation) of 2.8 E/m2/d (7.3%) and a RMS difference of 6.0 E/m2/d (15.8%). The relatively large overestimation was due to the inability of the OBPG PAR algorithm to detect cloudiness at times other than the time of satellite overpass. The key to improving the daily mean PAR estimates in such situations does not reside so much in improving the radiative transfer treatment or specifying more accurately aerosol properties, but rather in accounting properly for the diurnal variability of cloudiness. To this end, a methodology that utilized Modern Era Retrospective Reanalysis for Research and Applications, Version 2 (MERRA-2) hourly cloud data (fractional coverage, optical thickness) was proposed and tested, reducing the bias to 1.6 E/m2/d (4.2%). Improvement was not sufficient in some situations, due to the coarse resolution and uncertainties of the MERRA-2 products, which could not describe properly the cloud properties at the local scale (MERIS pixel). The treatment is applicable to any cloud situation and should be considered in a future version of the of OBPG PAR algorithm. This would require, however, refreshing the standard OBPG PAR products generated as part of the ocean-color processing line according to MERRA-2 data availability.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era.

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

Water-leaving contribution to polarized radiation field over ocean.

The top-of-atmosphere (TOA) radiation field from a coupled atmosphere-ocean system (CAOS) includes contributions from the atmosphere, surface, and water body. Atmospheric correction of ocean color imagery is to retrieve water-leaving radiance from the TOA measurement, from which ocean bio-optical properties can be obtained. Knowledge of the absolute and relative magnitudes of water-leaving signal in the TOA radiation field is important for designing new atmospheric correction algorithms and developing retrieval algorithms for new ocean biogeochemical parameters. In this paper we present a systematic sensitivity study of water-leaving contribution to the TOA radiation field, from 340 nm to 865 nm, with polarization included. Ocean water inherent optical properties are derived from bio-optical models for two kinds of waters, one dominated by phytoplankton (PDW) and the other by non-algae particles (NDW). In addition to elastic scattering, Raman scattering and fluorescence from dissolved organic matter in ocean waters are included. Our sensitivity study shows that the polarized reflectance is minimized for both CAOS and ocean signals in the backscattering half plane, which leads to numerical instability when calculating water leaving relative contribution, the ratio between polarized water leaving and CAOS signals. If the backscattering plane is excluded, the water-leaving polarized signal contributes less than 9% to the TOA polarized reflectance for PDW in the whole spectra. For NDW, the polarized water leaving contribution can be as much as 20% in the wavelength range from 470 to 670 nm. For wavelengths shorter than 452 nm or longer than 865 nm, the water leaving contribution to the TOA polarized reflectance is in general smaller than 5% for NDW. For the TOA total reflectance, the water-leaving contribution has maximum values ranging from 7% to 16% at variable wavelengths from 400 nm to 550 nm from PDW. The water leaving contribution to the TOA total reflectance can be as large as 35% for NDW, which is in general peaked at 550 nm. Both the total and polarized reflectances from water-leaving contributions approach zero in the ultraviolet and near infrared bands. These facts can be used as constraints or guidelines when estimating the water leaving contribution to the TOA reflectance for new atmospheric correction algorithms for ocean color imagery.

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models.

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Water column correction for coral reef studies by remote sensing.

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

Inherent optical properties and satellite retrieval of chlorophyll concentration in the lagoon and open ocean waters of New Caledonia.

The retrieval of chlorophyll-a concentration from remote sensing reflectance (Rrs) data was tested with the NASA OC4v4 algorithm on the inner New Caledonian lagoon (Case 2) and adjacent open ocean (Case 1) waters. The input to OC4v4 was Rrs measured in situ or modeled from water's inherent optical properties (2001-2007). At open ocean stations, backscattering and absorption coefficients were correlated with chlorophyll (R(2)=0.31-0.51, respectively), in agreement with models for Case 1 waters. Taking spectrofluorometric measurement as reference, the OC4v4 model leads to an average underestimation of 33% of the chlorophyll concentration. For the lagoon waters, OC4v4 performed inadequately because the backscattering coefficient, highly correlated with turbidity and suspended matter (R(2)=0.98), was poorly correlated to chlorophyll (R(2)=0.42). The OC4v4 performance was better in deep lagoon waters for stations with a TDT index (Tchla x depth/turbidity) higher than 19 mg m(-2) NTU(-1) (R(2)=0.974, bias=10.2%). Global Imager Rrs provided a good estimate of Tchla (R(2)=0.79, N=28) in the deeper part of the lagoon.

Importance and estimation of aerosol vertical structure in satellite ocean-color remote sensing.

The vertical distribution of absorbing aerosols affects the reflectance of the ocean-atmosphere system. The effect, due to the coupling between molecular scattering and aerosol absorption, is important in the visible, especially in the blue, where molecular scattering is effective, and becomes negligible in the near infrared. It increases with increasing Sun and view zenith angles and aerosol optical thickness and with decreasing scattering albedo but is practically independent of wind speed. Relative differences between the top of the atmosphere reflectance simulated with distinct vertical distributions may reach approximately 10% or even 20%, depending on aerosol absorption. In atmospheric correction algorithms, the differences are directly translated into errors on the retrieved water reflectance. These errors may reach values well above the 5x10(-4) requirement in the blue, even for small aerosol optical thickness, preventing accurate retrieval of chlorophyll-a [Chl-a] concentration. Estimating aerosol scale height or altitude from measurements in the oxygen A band, possible with the polarization and directionality of the Earth's reflectance instrument and medium resolution imaging spectrometer, is expected to improve significantly the accuracy of the water reflectance retrievals and yield acceptable [Chl-a] concentration estimates in the presence of absorbing aerosols.

Remote sensing of phytoplankton chlorophyll-a concentration by use of ridge function fields.

A methodology is presented for retrieving phytoplankton chlorophyll-a concentration from space. The data to be inverted, namely, vectors of top-of-atmosphere reflectance in the solar spectrum, are treated as explanatory variables conditioned by angular geometry. This approach leads to a continuum of inverse problems, i.e., a collection of similar inverse problems continuously indexed by the angular variables. The resolution of the continuum of inverse problems is studied from the least-squares viewpoint and yields a solution expressed as a function field over the set of permitted values for the angular variables, i.e., a map defined on that set and valued in a subspace of a function space. The function fields of interest, for reasons of approximation theory, are those valued in nested sequences of subspaces, such as ridge function approximation spaces, the union of which is dense. Ridge function fields constructed on synthetic yet realistic data for case I waters handle well situations of both weakly and strongly absorbing aerosols, and they are robust to noise, showing improvement in accuracy compared with classic inversion techniques. The methodology is applied to actual imagery from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS); noise in the data are taken into account. The chlorophyll-a concentration obtained with the function field methodology differs from that obtained by use of the standard SeaWiFS algorithm by 15.7% on average. The results empirically validate the underlying hypothesis that the inversion is solved in a least-squares sense. They also show that large levels of noise can be managed if the noise distribution is known or estimated.

Radiometric calibration of SeaWiFS in the near infrared.

The radiometric calibration of the Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS) in the near infrared (band 8, centered on 865 nm) is evaluated by use of ground-based radiometer measurements of solar extinction and sky radiance in the Sun's principal plane at two sites, one located 13 km off Venice, Italy, and the other on the west coast of Lanai Island, Hawaii. The aerosol optical thickness determined from solar extinction is used in an iterative scheme to retrieve the pseudo aerosol phase function, i.e., the product of single-scattering albedo and phase function, in which sky radiance is corrected for multiple scattering effects. No assumption about the aerosol model is required. The aerosol parameters are the inputs into a radiation-transfer code used to compute the SeaWiFS radiance. The calibration method has a theoretical inaccuracy of plus or minus 2.0-3.6%, depending on the solar zenith angle and the SeaWiFS geometry. The major source of error is in the calibration of the ground-based radiometer operated in radiance mode, assumed to be accurate to +/- 2%. The establishment of strict criteria for atmospheric stability, angular geometry, and surface conditions resulted in selection of only 26 days for the analysis during 1999-2000 (Venice site) and 1998-2001 (Lanai site). For these days the measured level-1B radiance from the SeaWiFS Project Office was generally lower than the corresponding simulated radiance in band 8 by 7.0% on average, +/- 2.8%.

Analysis of shipboard aerosol optical thickness measurements from multiple sunphotometers aboard the R/V Ronald H. Brown during the Aerosol Characterization Experiment--Asia.

Marine sunphotometer measurements collected aboard the R/V Ronald H. Brown during the Aerosol Characterization Experiment-Asia (ACE-Asia) are used to evaluate the ability of complementary instrumentation to obtain the best possible estimates of aerosol optical thickness and Angstrom exponent from ships at sea. A wide range of aerosol conditions, including clean maritime conditions and highly polluted coastal environments, were encountered during the ACE-Asia cruise. The results of this study suggest that shipboard hand-held sunphotometers and fast-rotating shadow-band radiometers (FRSRs) yield similar measurements and uncertainties if proper measurement protocols are used and if the instruments are properly calibrated. The automated FRSR has significantly better temporal resolution (2 min) than the hand-held sunphotometers when standard measurement protocols are used, so it more faithfully represents the variability of the local aerosol structure in polluted regions. Conversely, results suggest that the hand-held sunphotometers may perform better in clean, maritime air masses for unknown reasons. Results also show that the statistical distribution of the Angstrom exponent measurements is different when the distributions from hand-held sunphotometers are compared with those from the FRSR and that the differences may arise from a combination of factors.

Reducing variability that is due to secondary pigments in the retrieval of chlorophyll a concentration from marine reflectance: a case study in the western equatorial Pacific Ocean.

A neural network is developed to retrieve chlorophyll a concentration from marine reflectance by use of the five visible spectral bands of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The network, dedicated to the western equatorial Pacific Ocean, is calibrated with synthetic data that vary in terms of atmospheric content, solar zenith angle, and secondary pigments. Pigment variability is based on in situ data collected in the study region and is introduced through nonlinear modeling of phytoplankton absorption as a function of chlorophyll a, b, and c and photosynthetic and photoprotectant carotenoids. Tests performed on simulated yet realistic data show that chlorophyll a retrievals are substantially improved by use of the neural network instead of classical algorithms, which are sensitive to spectrally uncorrelated effects. The methodology is general, i.e., is applicable to regions other than the western equatorial Pacific Ocean.

SIMBAD: a field radiometer for satellite ocean-color validation.

A hand-held radiometer, called SIMBAD, has been designed and built specifically for evaluating satellite-derived ocean color. It provides information on the basic ocean-color variables, namely aerosol optical thickness and marine reflectance, in five spectral bands centered at 443, 490, 560, 670, and 870 nm. Aerosol optical thickness is obtained by viewing the Sun disk and measuring the direct atmospheric transmittance. Marine reflectance is obtained by viewing the ocean surface and measuring the upwelling radiance through a vertical polarizer in a geometry that minimizes glitter and reflected sky radiation, i.e., at 45 degrees from nadir (near the Brewster angle) and at 135 degrees in azimuth from the Sun's principal plane. Relative inaccuracy on marine reflectance, established theoretically, is approximately 6% at 443 and 490 nm, 8% at 560 nm, and 23% at 670 nm for case 1 waters containing 0.1 mg m(-3) of chlorophyll a. Measurements by SIMBAD and other instruments during the Second Aerosol Characterization Experiment, the Aerosols-99 Experiment, and the California Cooperative Oceanic Fisheries Investigations cruises agree within uncertainties. The radiometer is compact, light, and easy to operate at sea. The measurement protocol is simple, allowing en route measurements from ships of opportunity (research vessels and merchant ships) traveling the world's oceans.