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  • View in gallery

    Schematic of the AERONET data infrastructure. Measurements from CE-318 or CE-318T radiometer systems are ingested through satellite, mobile, or Internet connections. Near-real-time processing, supported by ancillary and calibration data, leads to the generation of data products at incremental quality levels (i.e., from level 1.0 to level 2.0), all accessible on a website.

  • View in gallery

    Location of current AERONET-OC sites contributing to the version-3 database (https://aeronet.gsfc.nasa.gov/ocean_color.html). The blue, green, and gray filled circles indicate the active marine water, inland water, and decommissioned sites, respectively.

  • View in gallery

    (bottom) Temporal expansion of the AERONET-OC sites from 2002 until 2020 in comparison with (top) the major satellite ocean color missions supported by the network. The blue, green, and gray colors indicate the active marine water, inland water, and decommissioned AERONET-OC sites, respectively.

  • View in gallery

    (a) Correction factors CfrChla determined from the f/Q-2002 and f/Q-2013 lookup tables with parameters θ = 40°, φ = 90°, W = 4 m s−1, Chla = 1 mg m−3, τa = 0.2, and θ0 = [20°, 40°, 60°], and (b) the related percent differences ε between CfrChla determined from the f/Q-2013 and f/Q-2002 values.

  • View in gallery

    Snapshot of the GUI supporting the supervised spectrum-by-spectrum quality check of LWN(λ) data. The blue color is used to highlight the examined spectrum.

  • View in gallery

    (a) Number of AERONET-OC LWN spectra at level 1.0, level 1.5, and level 2.0 determined for the period from 2002 until mid-2019 for which LWN(λ) have been available at all quality levels for the major sites, and (b) monthly means of the number of AERONET-OC LWN spectra across the same period with seasonal variations explained by the majority of current sites operated in the Northern Hemisphere.

  • View in gallery

    (a) AAOT, and (b) detail of the deployment jetty hosting the CE-318 and CE-318T collocated radiometer systems operated to verify the equivalence of their performance.

  • View in gallery

    Frequency distribution of (a) W, (b) θ0, (c) τa at 560 nm, and (d) Chla characterizing the AAOT LWN(λ) applied for the comparison of the CE-318 and CE-318T radiometer systems. The symbol N indicates the number of matchups, m is the median, and σ is the standard deviation of the considered quantities.

  • View in gallery

    Scatterplots of (a) LW(θ, φ, λ) and (b) LWN(λ) at matching center wavelengths (nm) from collocated CE-318T and CE-318 deployed at the AAOT. The symbol N indicates the number of matchups, r2 is the determination coefficient, rmsd is the root-mean-square deviation, |ψ|m is the spectrally averaged median of absolute percent differences, and ψm is the spectrally averaged median of percent differences, both of the latter determined with respect to CE-318T data.

  • View in gallery

    Scatterplots of the standard deviations σ(LT) [indicating either σ(LT318) and σ(LT318T)] at the (a) 490- and (b) 667-nm center wavelengths for the CE-318T and CE-318 measurement sequences contributing to the comparison of systems performance. The symbol N indicates the number of matchups, r2 is the determination coefficient, rmsd is the root-mean-square deviation, |ψ|m is the median of absolute percent differences, ψm is the median of percent differences, |Δ|m is the median of absolute differences, and Δm is the median of differences.

  • View in gallery

    AERONET-OC level 2.0 LWN(λ) spectra from the CPL, AAOT, GLR, and GDLT sites (corrected through the Chla approach). The thick black line indicates mean values, and the thick dashed lines indicate ±1σ; N indicates the number of spectra.

  • View in gallery

    Latent maps of AERONET-OC LWN(λ) spectra from (top) CPL, (top middle) AAOT, (bottom middle) GLR, and (bottom) GDLT produced with the first two PCA components applied to (left) LWN(λ)/LWN(560) and (right) LWN(560) vs the first PCA component. The isolines, displayed at 10% increments with increasing value from light yellow to black, illustrate the density distribution of projected AERONET-OC LWN(λ) for the various AERONET-OC sites. The circles indicate the projected BiOMaP LWN(λ), with colors identifying different water types: blue for oligotrophic and mesotrophic chlorophyll-a-dominated waters, red for CDOM-dominated waters, and green for any other optically complex water.

  • View in gallery

    (left) Scatterplots of LWNIOP(λ) vs LWNChla(λ) at matching center wavelengths (nm) restricted to the visible spectral region for sample AERONET-OC sites representative of a variety of water types: CPL, AAOT, GLR, and GDLT. (right) Percent spectral corrections Cfr, indicating either CfrChla (blue) or CfrIOP (red) for the various AERONET-OC sites. Symbols ψm and |ψ|m indicate the spectrally averaged median of percent differences and of absolute percent differences, respectively, between the corrections CfrIOP and CfrChla, N indicates the number of spectra, rmsd is the root-mean-square deviation, and r2 is the determination coefficient.

  • View in gallery

    Percent differences ε between LWNIOP(λ) and LWNChla(λ) at the 490- and 667-nm center wavelengths as a function of θ0 for CPL and AAOT sites. The gray dashed lines indicate the fitted values.

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Advances in the Ocean Color Component of the Aerosol Robotic Network (AERONET-OC)

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  • 1 Joint Research Centre of the European Commission, Ispra, Italy
  • | 2 National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 Barcelona Expert Centre, Institute of Marine Sciences (ICM-CSIC), Barcelona, Spain
  • | 4 AEQUORA, Lisbon, Portugal
  • | 5 Science Systems and Applications Inc., Lanham, Maryland
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Abstract

The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type.

Denotes content that is immediately available upon publication as open access.

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

© 2021 American Meteorological Society.

Corresponding author: G. Zibordi, giuseppe.zibordi@ec.europa.eu

Abstract

The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type.

Denotes content that is immediately available upon publication as open access.

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

© 2021 American Meteorological Society.

Corresponding author: G. Zibordi, giuseppe.zibordi@ec.europa.eu

1. Introduction

The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC; appendix A provides the definitions of all acronyms used in this paper) was conceived to support ocean color activities with spectral normalized water-leaving radiance LWN and aerosol optical depth τa data (appendix B provides a list of the definitions of all variables and symbols used in this paper) retrieved from radiometers deployed on offshore fixed structures (Zibordi et al. 2006). Following the initial field-testing of instruments and measurement protocols (Zibordi et al. 2002) and by fully relying on the AERONET infrastructure (Holben et al. 1998; Holben et al. 2001), the first AERONET-OC site was established during early 2002 at the Acqua Alta Oceanographic Tower (AAOT) in the Adriatic Sea. This deployment, concurrent with the launch of the Medium Resolution Imaging Spectrometer (MERIS) on board the Envisat platform, was then followed by others in different regions of the globe using constructions such as lighthouses, service structures, oil rigs (e.g., Zibordi et al. 2006). Over time, AERONET-OC has become a major source of reference measurements for the assessment of satellite ocean color data (Zibordi et al. 2009a; Moore et al. 2015; Pahlevan et al. 2017; Valente et al. 2019), atmospheric correction processes (Jamet et al. 2011; Fan et al. 2017), bio-optical models (Tan et al. 2019), data merging schemes (Mélin and Zibordi 2007) and vicarious calibration methods (Mélin and Zibordi 2010; Hlaing et al. 2014). AERONET-OC measurement protocol, instruments calibration, data handling, processing, quality assurance, and accessibility, were amply detailed in Zibordi et al. (2009b). During the last decade, however, AERONET-OC marked incremental expansion and profited technological and application-oriented developments leading to further consolidation of the network. This work documents these latest developments and their impact on AERONET-OC data products.

2. Background

AERONET gathers field measurements from CE-318 and CE-318T radiometer systems through a variety of solutions (i.e., satellite, mobile, and Internet connections) and allows for their near-real-time data processing, archival at different quality levels (i.e., level 1.0, level 1.5, and level 2.0) and access through a web interface (see Fig. 1). Since early 2000s this central infrastructure sustained the development, expansion and consolidation of AERONET-OC whose details, spanning from measurement protocol to data access, were provided in Zibordi et al. (2009b). A number of successive focused investigations allowed for a further assessment of AERONET-OC methods and data products. A few studies of explicit relevance addressed the uncertainties of data products (Gergely and Zibordi 2014), the consistency of basic assumptions supporting the determination of LW (Zibordi 2016), the application of alternative approaches for the minimization of bidirectional effects (Talone et al. 2018), the impact of spectral perturbations by deployment structures (Talone and Zibordi 2019), adjacency effects in satellite data as a function of the land/atmosphere optical properties and the distance from the coast (Bulgarelli and Zibordi 2018a). With the objective to introduce those elements supporting recent network developments, a brief summary of AERONET-OC basics is provided in the following sections together with an overview of relevant findings from latest investigations.

Fig. 1.
Fig. 1.

Schematic of the AERONET data infrastructure. Measurements from CE-318 or CE-318T radiometer systems are ingested through satellite, mobile, or Internet connections. Near-real-time processing, supported by ancillary and calibration data, leads to the generation of data products at incremental quality levels (i.e., from level 1.0 to level 2.0), all accessible on a website.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

a. Measurement and data reduction methods

AERONET-OC quantifies the water-leaving radiance LW(θ, φ, λ)—that is, the radiance emerging from below the water surface—from measurements of the total radiance from the sea LT(θ, φ, λ) and of the sky radiance Li(θ′, φ, λ), according to
LW(θ,φ,λ)=LT(θ,φ,λ)ρ(θ,φ,θ0,W)Li(θ',φ,λ),
where θ and θ′ are the viewing angles for sea and sky measurements with θ′ = 180° − θ, φ is the relative azimuth between solar and sensor planes, θ0 is the solar zenith angle, λ is the center wavelength of the specific spectral band, and W is the wind speed. Note that the dependence on θ0 is not explicitly indicated for the radiance terms. The term ρ(θ, φ, θ0, W) is the water surface reflectance factor, a function of the measurement and illumination geometries, and of the sea state expressed through W. In agreement with consolidated measurement schemes [International Ocean Colour Coordinating Group (IOCCG); IOCCG 2019], LT(θ, φ, λ) and Li(θ′, φ, λ) values are determined at θ = 40° and φ = 90°. Larger values of φ (e.g., φ = 135°), which may further minimize glint perturbations with respect to φ = 90° (Mobley 1999), can lead to larger superstructure perturbations with increasing θ0.

The values of ρ(θ, φ, θ0, W) applied in AERONET-OC data processing are those determined by Mobley (1999) at λ = 550 nm through HydroLight simulations (Mobley 1994; Mobley and Sundman 2000) using the Cox and Munk (1954) slope distribution of surface waves. It is recalled that the theoretical sky radiance distribution, neglecting polarization effects but accounting for multiple scattering and aerosol contributions, was constructed benefitting of an irradiance model (Gregg and Carder 1990) and experimental sky radiance patterns (Harrison and Coombes 1988).

The normalized water-leaving radiance LWN(λ), the radiance that would occur with nadir view, no atmosphere, the sun at the zenith and at the mean sun–Earth distance, is computed as
LWN(λ)=LW(θ,φ,λ)Cfr(θ,φ,θ0,λ,τa,IOP,W)[D2td(λ)cosθ0]1,
where D2td(λ) cosθ0 is an estimate of the irradiance ratio of downward Ed(λ) to mean extra-atmospheric E0(λ) (Zibordi et al. 2004), with D accounting for the sun–Earth distance as a function of the day of the year and td(λ) being the atmospheric diffuse transmittance (Deschamps et al. 1983). The correction factor Cfr(θ, φ, θ0, λ, τa, IOP, W) minimizes the impact of bidirectional effects that are due to the nonnadir view of the field radiometer and of the illumination geometry, with τa and IOP indicating the aerosol optical depth and the inherent optical properties of water, respectively (see details in section 3).

AERONET-OC, in agreement with AERONET requirements, relies on CE-318 and the more recent CE-318T radiometers conceived to perform multispectral measurements in the ultraviolet, visible and near-infrared spectral regions with a full-angle field of view of 1.2°. Specifically, CE-318 and CE-318T radiometer systems configured for ocean color applications and called the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Photometer Revision for Incident Surface Measurements (SeaPRISM), autonomously acquire on a channel-by-channel basis (i.e., spectrally asynchronously) the following:

  1. the direct solar irradiance E(θ0, φ0, λ) as a function of θ0, solar azimuth angle φ0, and λ to determine the aerosol spectral optical depth τa(λ) required to compute td(λ);
  2. NT sea-radiance measurements for determining LT(θ, φ, λ);
  3. Ni sky-radiance measurements for determining Li(θ′, φ, λ).

The sky and sea measurements for determining Li(θ′, φ, λ) and LT(θ, φ, λ) are performed with Ni = 3 and NT = 11, respectively. The larger number of NT measurements with respect to Ni, is suggested by the need to statistically address the impact of wave perturbations. Measurements are not performed in the presence of clouds affecting E(θ0, φ0, λ).

For each measurement sequence, Li(θ′, φ, λ) is determined by simply averaging the Ni sky-radiance data. Conversely, LT(θ, φ, λ) is determined from the average of a fixed percent of the NT sea-radiance measurements exhibiting the lowest radiance levels (i.e., 2 out of 11). This approach has been suggested by studies (Zibordi et al. 2002; Hooker et al. 2002) highlighting the need for aggressive filtering of above-water measurements to minimize the effects of wave perturbations in LT(θ, φ, λ).

Equivalent to AERONET atmospheric data products (Holben et al. 2001), ocean color data are also archived at three incremental quality levels. Level 1.0 only includes LWN(λ) data for which (i) Li(θ′, φ, λ) and LT(θ, φ, λ) are determined from measurement sequences not exhibiting missing data; (ii) the dark values are below a given threshold; (iii) the value of φ0 is within site-dependent limits to minimize superstructure perturbations in LT(θ, φ, λ); (iv) τa(λ) has been determined; and (v) W is below the maximum threshold of 15 m s−1.

Level 1.5 includes LWN(λ) derived from level 1.0 data for which (i) cloud screened AERONET τa(λ) exist at level 1.5 in the AERONET database (Smirnov et al. 2000; Giles et al. 2019); (ii) a series of empirical thresholds are satisfied (e.g., LWN(λ) > −0.01 mW cm−2 μm−1 sr−1 indicating absence of exceedingly negative values at any λ); LWN(412) < LWN(443) for coastal sites; LWN(1020) < 0.1 mW cm−2 μm−1 sr−1, except for sites exhibiting very turbid waters, to exclude measurements appreciably affected by the presence of obstacles in the sight of the sea-viewing sensor; and (iii) the NT sea-radiance measurements and Ni sky-radiance measurements exhibit low variance indicating low wave perturbations and negligible cloud contamination, respectively. Specifically, the test on the variance of the NT sea-radiance data removes measurement sequences affected by random high radiance values due to glint or foam reflectance. The test on the variance of the Ni sky-radiance data, in combination with the AERONET cloud screening for τa(λ), aims at removing those measurement sequences likely perturbed by clouds that may hinder the determination of Li(θ′, φ, λ) and consequently decrease the accuracy of LWN(λ). Still, additional perturbations weakening the assumption of clear sky, may result from sparse clouds not affecting the sun disk and the portion of sky in the direction (θ′, φ). Nevertheless, it is expected that uncertainties resulting from those unfavorable measurement conditions are (at least partially) accounted for by the uncertainties assigned to td(λ) and to environmental perturbations (see Zibordi et al. 2009b).

Level 2.0 data include LWN(λ) determined from level 1.5 products for which (i) the level 2.0 AERONET τa(λ) exist; (ii) the NT sea-radiance measurements and Ni sky-radiance measurements satisfy lower variance thresholds with respect to those applied for level 1.5 (details on these thresholds are provided in section 3); (iii) the differences between pre- and postdeployment calibration coefficients for the AERONET-OC radiometer exhibit values smaller than 5% (it is mentioned that generally these differences do not exceed 1% per year); and (iv) LWN(λ) do not show questionable values during the final spectrum-by-spectrum assessment performed by a practiced analyst. This final step has been often supported by an automatic process (D’Alimonte and Zibordi 2006) aiming at rejecting LWN(λ) spectra exhibiting (i) low statistical representativeness within the dataset itself (self-consistency test) and (ii) anomalous features with respect to a reference set of quality-checked data (relative-consistency test). The automatic procedure has been effectively applied to data from a variety of sites (see Zibordi et al. 2009b). However, the analysis of results has shown limits in addressing unique measurement conditions not represented in both the reference dataset or the AERONET-OC data going to be screened. Because of this, the supervised spectrum-by-spectrum quality check is still the fundamental step toward level 2.0 data quality.

Statistically, approximately 44% of the level 1.0 LWN(λ) are raised to level 1.5 and only 28% of the level 1.0 LWN(λ) are qualified for level 2.0, as determined with data from 2002 until mid-2019 for which LWN(λ) values have been available at all quality levels for the major sites.

b. Calibration

Calibrations of each AERONET-OC radiometer system comprise independent actions performed at the Goddard Space Flight Center of the National Aeronautics Space Administration (NASA GSFC) for direct solar irradiance and radiance measurements.

Pre- and postdeployment calibrations for direct solar irradiance are obtained indirectly by relying on a reference instrument in turn calibrated through the Langley method (Holben et al. 1998). Pre- and postdeployment calibrations for radiance measurements, which are relevant to above-water radiometry, are performed using an integrating sphere (IOCCG 2019). These derived pre- and postdeployment calibration coefficients are interpolated over time to account for any sensitivity change of the radiometers during field operation. All calibration measurements and retrieved coefficients are permanently stored in the AERONET database.

Fully independent radiance calibrations performed at the Joint Research Centre (JRC) on a number of AERONET-OC radiometers (tentatively ⅓ of those annually deployed), add a further check to the overall quality process. These independent radiance calibrations rely on the use of National Metrology Institutes (NMI) traceable 1000 W FEL lamps and reference 99% reflectance plaques (IOCCG 2019).

Verification of the accuracy of both NASA GSFC and JRC absolute radiance calibrations were performed by the National Institute of Standards and Technology (NIST) using a CE-318 radiometer. Results indicate uncertainties within approximately 1% in the visible spectral region of major interest for ocean color applications (Johnson et al. 2021).

Also, since 2010 the temperature sensitivity was characterized for each instrument at all spectral bands (Giles et al. 2019). This process has mostly led to an increase of the accuracy for τa determined from the near-infrared spectral bands (i.e., 870 and 1020 nm). Still, it does not have an appreciable impact on the determination of τa and LWN in the visible spectral region.

c. Uncertainties

Uncertainties are a fundamental aspect of any measurement and are essential for a confident application of data products. In the case of AERONET-OC, a quantification of LWN(λ) uncertainties was proposed in Zibordi et al. (2009b). These uncertainties were provided in relative terms (i.e., in percent) making explicit reference to the AAOT site representative of a variety of measurement conditions. The analysis embraced uncertainty contributions from absolute radiance calibrations, sensitivity decay during deployments, corrections for bidirectional effects, water surface reflectance, atmospheric diffuse transmittance, environmental perturbations (sky radiance variability and wave effects). Assuming each uncertainty independent from the others, their combined spectral values resulting from the quadrature sum of the various contributions, lead to values approaching 5% in the blue–green spectral regions and 8% in the red.

The former results provide a term of reference for typical relative uncertainties affecting LWN(λ) AERONET-OC data. Still, these uncertainty values may not equally apply to measurements performed with observation conditions significantly different from those characterizing the AAOT site. The issue has been addressed following the Guide to the Expression of Uncertainty in Measurement (GUM) by quantifying uncertainties affecting data products from a number of AERONET-OC sites. Results from Gergely and Zibordi (2014) indicate the following:

  1. The relative uncertainties determined for AAOT LWN(λ) through the application of GUM accounting for correlations among the various uncertainty contributions, confirm within 0.5% the values earlier provided in Zibordi et al. (2009b). The study also shows a reduction of uncertainties when excluding the highest values of solar zenith angle and of aerosol optical depth.
  2. The relative uncertainties may amply vary from site to site as a function of the water type, which implies different spectral shapes and ranges of LWN(λ). These relative uncertainties may largely exceed the 5% threshold commonly considered for validation activities (Zibordi and Voss 2014).
  3. The absolute uncertainties affecting LWN(λ) also appreciably vary from site to site, but they do not necessarily mirror the values of relative uncertainties. In particular, sites exhibiting relative uncertainties well above those determined for the AAOT site, may exhibit much lower absolute uncertainties.
The above findings are summarized in Table 1 for two AERONET-OC sites: the AAOT characterized by moderately sediment-dominated waters and the Helsinki Lighthouse (HLT) characterized by waters dominated by high concentrations of colored dissolved organic matter (CDOM). Remarkable are the differences between relative and absolute uncertainties, more pronounced in the blue and green spectral bands. Just considering the 412 nm center wavelength, the relative uncertainties for AAOT and HLT determined with GUM exhibit values of 5.1% and 27.4%, respectively (i.e., much lower for the AAOT site). On the contrary, the absolute uncertainties show values of 0.038 and 0.016 mW cm−2 μm−1 sr−1 at 412 nm for AAOT and HLT, respectively (i.e., much lower for the HLT site). Definitively, the median of the LWN(λ) spectral values contributing to the analysis provided in Table 1, well explains the differences: the LWN(λ) median at 412 nm is one order of magnitude higher for the AAOT than for HLT data.
Table 1.

Relative (ur; %) and absolute (ua; mW cm−2 μm−1 sr−1) uncertainties determined for LWN(λ) from the AAOT and HLT sites at nominal center wavelengths from 412 to 667 nm [after Gergely and Zibordi (2014)]. The symbol m indicates the median of the LWN(λ) spectra ( mW cm−2 μm−1 sr−1) included in the uncertainty analysis.

Table 1.

Aside from suggesting the need to compute uncertainties for each specific AERONET-OC site, the above results confirm the importance of determining both relative and absolute uncertainties, whose values are expected to be representative for the range of considered radiances.

d. Products assessment

AERONET-OC LWN(λ) rely on the basic measurement equation defined by Eq. (1) and a relatively small number of data collected with a narrow field of view (i.e., 1.2° full angle). The major drawback, however, arises from the spectrally asynchronous measurement capability of CE-318 and CE-318T radiometers: measurements are performed at different time for each spectral band. While this technical aspect is not relevant for sky or sun measurements benefitting of sources (i.e., sun and sky) relatively stable over a short time during clear sky conditions, it definitively affects sea measurements as a result of wave perturbations implicitly leading to differences in surface perturbations across the various bands.

The measurement capabilities offered by CE-318 and CE-318T systems are thus far away from being ideal for ocean color field applications (IOCCG 2019). Still, strict quality assurance of the measurement sequences can lead to data products meeting requirements for validation activities. As stated in section 1, an early step of the quality assurance process, which includes a number of quality control checks, is the removal of any measurement sequence exhibiting variance above given thresholds in the total radiance from the sea in at least one spectral band (Zibordi et al. 2009b). This means that quality assured measurement sequences raised to level 1.5 and level 2.0 are not affected by large surface perturbations. As a result of the filtering applied, AERONET-OC LWN(λ) are only determined for low-to-mild sea state conditions bound by wind speeds typically not exceeding 5 m s−1. Direct benefit of such quality assurance process is thus the low variability of the water surface reflectance factor ρ as a function of wind speed across a wide range of solar zenith angles (Zibordi 2016).

The measurement sequences passing the former quality assurance step might still be affected by excessive glint resulting from the reflection of sky radiance from brighter portions of the sky. Because of this, as already anticipated, LT(θ, φ, λ) is determined from each quality assured measurement sequence by averaging a percent of the NT data exhibiting the lowest radiance values in each band. The use of the average of the lowest radiance values instead of the mean of all values, still supported by experimental evidence (Zibordi 2012, 2016; Pitarch et al. 2020), might be responsible for an underestimate of LWN(λ) more pronounced at low solar zenith angles θ0 (i.e., below 20°) and increasing with the wind speed. Table 2 summarizes comparison results between LWN(λ) from AERONET-OC and in-water measurements. These results show an excellent agreement between the independent determinations of LWN(λ), nevertheless limited to cases with θ0 > 20° and benefitting of glint filtering (i.e., not including measurement sequences heavily affected by surface perturbations and consequently mostly restricted to relatively low wind speeds). Still, recognizing the lack of a fundamental element justifying the use of the mean of relative minima alternative to the mean of all values, the agreement of the data shown in Table 2 is largely explained by the combined uncertainties of the compared measurements and corroborate the applied AERONET-OC processing scheme.

Table 2.

Comparison results between LWN(λ) from AERONET-OC and independent in-water measurements accounting for 185 matchups at various center wavelengths in the 412–667-nm spectral interval [after Zibordi (2016)]. The symbol ψ indicates the mean of signed percent differences (i.e., an index for the bias affecting the compared data) of AERONET-OC-derived LWN(λ) with respect to in-water data products. The symbol |ψ| indicates the mean of absolute percent differences (i.e., an index for the dispersion of data).

Table 2.

e. Polarization effects

A number of recent studies highlighted the dependence of above-water radiometry on polarization effects (Harmel et al. 2012; Mobley 2015; Hieronymi 2016; D’Alimonte and Kajiyama 2016; Foster and Gilerson 2016; Zhang et al. 2017; Gilerson et al. 2018). Recognizing that an operational network such as AERONET-OC can only rely on consolidated findings, an effort was made to evaluate the impact of polarization through tabulated values of ρ determined accounting (Mobley 2015) and not accounting (Mobley 1999) for polarization effects. The analysis was performed by comparing AERONET-OC LW(λ) data determined using the different ρ values with LW(λ) from independent in-water measurements (Zibordi 2016). Results, restricted to the measurement conditions for which AERONET-OC data can be produced (i.e., wind speeds generally lower than 5 m s−1) indicated a slightly better performance of LW(λ) determined without accounting for polarization effects and justifies the current application of the ρ values from Mobley (1999). Still, alternative ρ values, hopefully accounting for their spectral dependence, can be applied as soon as available and supported by community consensus.

f. Bidirectional effects

The nonisotropy of the in-water light field implies the correction of LW(θ, φ, λ) data for bidirectional effects due to the solar zenith angle and the nonnadir view of the sensor. The correction approach commonly applied for bidirectional effects is that proposed for Case-1 (i.e., chlorophyll-a dominated) waters by Morel et al. (2002) hereinafter called Chla based. Considering that most of the AERONET-OC sites are located in optically complex coastal waters, the corrections relying on the Chla-based approach are likely affected by large uncertainties. Because of this, alternative corrections applicable to both Case-1 and optically complex waters have been evaluated with a view to their applicability to AERONET-OC data.

Among the various approaches proposed in literature (e.g., Park and Ruddick 2005), the one considered for dedicated investigations was that by Lee et al. (2011). This correction, hereinafter called IOP-based, provides the major advantage of relying on the retrieval of water IOPs from LW(θ, φ, λ) itself. An experimental study focused on the comparison of Chla- and IOP-based approaches, still restricted to the bidirectional effects due to nonnadir view, shows average relative differences of corrections varying between 10% and 40% in the blue and green spectral regions with standard deviation of 10%–25% (Talone et al. 2018). By using in situ reference data to quantify the uncertainties affecting the correction factors from the two approaches, the study indicates relative spectrally and water dependent uncertainties varying between 20% and 60% for the Chla-based approach, with the highest values affecting the blue and red regions. Conversely, the IOP-based approach shows relative uncertainties within 20%–35% and lower dependence on wavelength and water type. These results have motivated the implementation of the IOP-based correction approach in the AERONET-OC processing in addition to the Chla-based one.

g. Perturbations by deployment structures

AERONET-OC radiometers are deployed on fixed platforms. The location of the instrument on each platform needs to ensure the minimization of structure perturbations. This implies identifying locations ideally free from obstacles above the radiometer, and allowing the sight of view of the sensor to point at the water surface well away from the main structure during the central hours of the day when satellite overpasses occur (at least for deployments underpinning ocean color validation activities). The former requirement may face challenges due to the size and shape of structures, deployment restrictions due to the presence of obstacles or of highly reflective objects. An early investigation carried out at the AAOT (Hooker and Zibordi 2005) indicated that observations performed pointing at the water surface at a distance at least as large as the height of the deployment structure would minimize perturbations due to the structure itself. This finding, also supported by field measurements performed on a research vessel (Hooker and Morel 2003), was proposed as a practical rule. However, its widespread applicability has not been demonstrated. In particular, the spectral impact of superstructures in LWN(λ) remained an open issue. This latter point has been tackled in a recent investigation performed at the AAOT by collecting above-water radiometry data at various distances from the structure adopting the AERONET-OC viewing geometry (i.e., θ = 40° and φ = 90°) with the deployment platform exhibiting its regular appearance, or alternatively covered by white sheets (Talone and Zibordi 2019).

Results from this investigation indicate that for the low reflectance case (i.e., the AAOT without white cover), the structure perturbations affect LWN(λ) by less than 1% in the visible spectral region when the sea-radiance measurements are performed at distances from the structure larger than the height of the structure itself. However, they may exceed 2% beyond 800 nm. For identical measurement geometries, but for the high reflectance case (i.e., the AAOT with white cover), results show that the impact of the superstructure approaches 1% in the blue–green and may exceed 2% beyond approximately 600 nm. Recognizing that the strict applicability of these results is confined to a specific deployment platform, still, these findings confirm previous deployment recommendations and additionally provide new elements for the quantification of spectral uncertainties affecting data products.

h. Adjacency effects

AERONET-OC sites are almost totally located in coastal regions. This particular condition, generally depending on the larger availability of fixed structures in costal rather than open sea, may impact the exploitation of data products in validation processes. In fact, close regions exhibiting different surface reflectance such as sea and land, naturally lead to adjacency effects in top-of-the-atmosphere data (Bulgarelli and Zibordi 2018a). A number of targeted studies (e.g., Bulgarelli and Zibordi 2018b) showed that adjacency effects may lead to unwanted perturbations in satellite data products up to more than 10 n mi (1 n mi = 1.852 km) away from the coast. These perturbations, which spectrally vary with the reflectance of land surfaces, water type, illumination and viewing geometries, coastline and the specific atmospheric correction method applied, may become responsible for seasonal trends in satellite data (Bulgarelli et al. 2018). Recognizing that adjacency effects only concern satellite data products, still, the application of AERONET-OC data from sites located within a very few nautical miles from the coast may be challenged in validation activities not supported by schemes allowing to remove adjacency perturbations in remote sensing data. This stresses the need to establish AERONET-OC sites at locations well away from the coast when prioritizing support to ocean color validation activities. Nevertheless, time series of AERONET-OC data from regions located within a few nautical miles from the coast are expected to gather increased value when operational atmospheric correction codes will include schemes for the minimization of adjacency effects.

3. An overview on network advances

Aside from a constant increase of the measurement sites across a variety of water types all over the globe, AERONET-OC has incrementally benefitted of investigations such as those on the minimization of bidirectional effects and on technological progresses allowing to expand the number of spectral bands from 9 to 12. Following the release of the version-3 AERONET database (Giles et al. 2019; https://aeronet.gsfc.nasa.gov/new_web/aot_levels_versions.html), the parallel AERONET-OC processing for the version-3 database has incorporated those advances and in the future is expected to also integrate progresses on calibration, data reduction and quality assurance/control. The recent main network advances are detailed in the following subsections.

a. Instrument and band setting

Excluding some early CE-318 analog radiometer systems, most AERONET-OC deployments rely on CE-318 9-channel digital systems and on the recent CE-318T 12-channel ones.

Table 3 shows the reference center wavelengths for the 9-channel and 12-channel systems. The CE-318 9-channel radiometer system largely exhibits spectral bands corresponding to those of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor. Conversely, the CE-318T 12-channel system exhibits bands matching most of the Ocean Land Color Instrument (OLCI) and it is definitively expected to overlap the major ocean color ones of the forthcoming Plankton, Aerosol, Cloud, and Ocean Ecosystem (PACE) mission.

Table 3.

Corresponding nominal center wavelengths λ (nm) of CE-318 9-channel and CE-318T 12-channel radiometer systems (all having 10-nm bandwidth), in comparison with those of recent satellite ocean color sensors (OLCI, VIIRS, and MODIS). The symbols s and i indicate the marine and inland water spectral band configurations, respectively.

Table 3.

Also relevant, considering the growing interest in remote sensing applications focusing on lake waters (e.g., Moore et al. 2019), is that the 12-channel standard system has been conceived with two different spectral band settings: one for marine applications and the other more suitable for inland waters.

Briefly, the CE-318T radiometer system for marine applications, when compared with the CE-318 version, features additional spectral bands centered at 400, 510, 620, and 779 nm. While the supplemental bands in the visible allow for a more comprehensive spectral determination of LWN(λ), the band centered at 779 nm combined with that at 865 nm allow for investigating τa(λ) at center wavelengths equivalent to those commonly supporting the atmospheric correction of satellite ocean color data.

The CE-318T radiometer system for inland water applications, when compared to the marine one, has spectral bands centered at 681 and 709 nm replacing those at 400 and 779 nm. The spectral setting comprising the 667, 681 and 709 nm center wavelengths aims at supporting the detection and quantification of phytoplankton blooms in the near-surface water layer.

While the foreoptics, optics and detector components of CE-318 and CE-318T radiometers are substantially the same for the relevant visible and near-infrared spectral bands, the CE-318T units exhibit an improved analog-to-digital resolution (21 versus 16 effective bits of the CE-318 units). Additionally, CE-318T systems can transmit relatively large volumes of data through General Packet Radio Service (GPRS) [opposite to the limited capability of satellite data collection platforms (DCP)]. Further relevant feature of the new CE-318T systems is the possibility to store large volumes of data (up to 32 gigabytes) on Secure Digital (SD) cards, definitively relevant for remote sites not always within GPRS coverage and constrained by the low transmission rate of DCP (Zibordi et al. 2009b).

CE-318T systems also exhibit some measurement protocol improvement with respect to the former CE-318 version. First, the sign of the relative azimuth φ can be varied during the day or across the year (e.g., from −90° to +90°). This feature is relevant for structures with deployment locations that may not allow respecting the basic geometric requirements during the full day or during part of the year. It is mentioned that the parameters determining the measurement geometry (i.e., θ and φ, in addition to the exact center wavelengths of the instrument) are accessible in each data record at any quality level.

An additional major feature of the CE-318T systems is the capability of performing an increased number of above-water marine or lake measurement sequences. By recalling that for CE-318 systems the number of above-water measurements is limited to a single sequence every 30 min, the marine or lake sequences of relevant measurements performed with CE-318T systems can now be increased by up to a factor of 3, with each sequence completed in 5 min. This may allow producing measurement replicates at a finer temporal scale, relevant to enhance the quality assurance of data and better quantify the environmental perturbations contributing to LWN(λ) uncertainties.

b. Sites

Figure 2 displays the location of the 31 AERONET-OC sites to date. Notable, while the Northern Hemisphere exhibits a substantial midlatitude distribution of sites across continents, the Southern Hemisphere shows a very few active ones. The marine sites represent a number of water types embracing chlorophyll-a-, sediment-, and CDOM-dominated waters. When considering the lake sites, they embrace a variety of inland waters characterized by different seasonal conditions including the occurrence of harmful algal blooms. Finally, note the number of decommissioned marine sites. This is mostly an indication of the difficulty to maintain access to offshore locations often implying complex logistics and extraordinary safety measures governed by agreements with nonresearch bodies owning the structures.

Fig. 2.
Fig. 2.

Location of current AERONET-OC sites contributing to the version-3 database (https://aeronet.gsfc.nasa.gov/ocean_color.html). The blue, green, and gray filled circles indicate the active marine water, inland water, and decommissioned sites, respectively.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

The temporal expansion of the AERONET-OC sites is illustrated in Fig. 3 together with the satellite ocean color missions that mostly benefit from the network data. Table 4 provides the comprehensive list of AERONET-OC sites (to date) together with details on location, type of structure and responsible institution.

Fig. 3.
Fig. 3.

(bottom) Temporal expansion of the AERONET-OC sites from 2002 until 2020 in comparison with (top) the major satellite ocean color missions supported by the network. The blue, green, and gray colors indicate the active marine water, inland water, and decommissioned AERONET-OC sites, respectively.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

Table 4.

AERONET-OC sites contributing to the version-3 database (to date). Here, EU indicates the European Union, US indicates the United States, and UK indicates the United Kingdom.

Table 4.

c. Processing

A number of changes have been implemented in the AERONET-OC processing supporting the version-3 database. A primary feature of the new processing release, when compared to the previous one supporting the version-2 database, is the capability to handle data from the CE-318T 12-channel instruments in addition to the CE-318 9-channel ones. This does not simply imply the need for handling a higher number of spectral bands, but also the necessity to address the impact of a larger number of spectral values on the quality assurance process or, to deal with an increased number of spectrally resolved parameters such as the correction factors for bidirectional effects.

The LWN(λ) data in the version-2 database relied on the application of spectral thresholds to the standard deviations of the NT sea-radiance measurements and Ni sky-radiance measurements, identical for both level 1.5 and level 2.0. These spectral thresholds constrained in the range of 0.1–0.2 mW cm−2 μm−1 sr−1 for the NT sea-radiance measurements and in the range of 0.02–0.03 mW cm−2 μm−1 sr−1 for the Ni sky-radiance measurements, were determined from the analysis of data collected at the AAOT over a variety of water types during ideal observation conditions: clear sky and low-to-mild sea state. It is mentioned that the standard deviation of CE-318 and CE-318T radiance measurements determined with a stable laboratory source is generally lower than 0.002 and 0.001 mW cm−2 μm−1 sr−1, respectively, in the spectral range of interest.

As already stated, the application of thresholds to the standard deviation of successive acquisitions aims at removing any data record likely (i) affected by significant wave perturbations or foam making difficult the application of an accurate surface reflectance factor ρ for the determination of the sky glint contribution or (ii) perturbed by clouds in the direction (θ′, φ). Obviously, an increase in the number of spectral bands naturally enhances the probability of excluding measurement sequences just because data at a single spectral band do not meet requirements. Aiming at preserving the same rigor characterizing level 2.0 LWN(λ) in the version-2 database (i.e., level 2.0 is the quality level suggested for the validation of satellite data products), level 2.0 LWN(λ) data in the version-3 database have been obtained applying the same thresholds declared above. Conversely, level 1.5 LWN(λ) data in the version-3 database have been obtained by relaxing (i.e., doubling) the thresholds. This solution provides the advantage of increasing by some percent (depending on the site) the number of LWN(λ) data at level 1.5 in the version-3 database at the expense of an increased uncertainty due to environmental perturbations. This revision of the processing scheme is solely supported by the assumption that an increase of the number of data at level 1.5, despite a potentially lower accuracy, may provide benefit to a variety of real-time applications spanning from early assessment of satellite data products to water quality monitoring.

The AERONET-OC version-2 database relied on the Chla-based approach specifically proposed to minimize bidirectional effects in Case-1 waters (Morel et al. 2002). The related lookup tables to compute the correction factors applied to CE-318 data, however, exhibit spectral limitations. In fact the f/Q table by Morel et al. (2002) hereinafter identified as f/Q-2002, are restricted to key ocean color center wavelengths (i.e., 412, 443, 490, 510, 560, 620, and 660 nm), and consequently do not confidently support corrections for bidirectional effects at significantly different center wavelengths. Because of this, new lookup tables (hereinafter identified as f/Q-2013) produced by the Observatoire Océanologique de Villefranche-sur-Mer applying the same processing solutions as in Morel et al. (2002), but with a spectral resolution of 5 nm and extending over the 350–700 nm spectral range (B. Gentili, unpublished data), have now been incorporated in the current AERONET-OC processing. The f/Q-2013 lookup tables with higher spectral resolution and spectral range relative to f/Q-2002, provide the immediate advantage to allow determining the correction factors Cfr for the Chla-based approach minimizing uncertainties due to spectral extrapolations. Further advantage offered by the f/Q-2013 with respect to the f/Q-2002 tables is the possibility of indexing the clear sky illumination conditions as a function of τa with values of 0.05, 0.10, 0.2, 0.4, and 1.0.

As already anticipated, an additional implementation incorporated in the current AERONET-OC processing supporting the version-3 database is the IOP-based correction approach to generate LWN(λ) likely free from the water type restrictions intrinsic of the Chla-based one.

A brief introduction to both the Chla- and IOP-based correction approaches is provided in the following subsections, while an evaluation of their impact on AERONET-OC data representative of different water types is addressed in the discussion section.

1) Chla-based correction approach

The Chla-based approach minimizes the dependence of LW(θ, φ, λ) on the viewing geometry and solar zenith following Eq. (2) with Cfr(θ, φ, θ0, λ, τa, IOP, W) given by
CfrChla(θ,φ,θ0,λ,τa,IOP,W)=(0,W)(θ,W)Q(θ,φ,θ0,λ,τa,Chla)f(θ0,λ,τa,Chla)×f(0,λ,τa,Chla)Q(0,0,0,λ,τa,Chla),
where ℜ accounts for combined reflection/refraction effects on the downward irradiance and upwelling radiance propagating through the water surface, Q is the Q factor indicating the ratio of upward irradiance to upwelling radiance just below the surface, and f relates the irradiance reflectance to the water IOPs. The aerosol optical depth τa expresses dependence on the atmospheric optical properties and the chlorophyll-a concentration Chla indicates dependence on IOPs. It is recalled that the values of Chla applied for the determination of CfrChla and accessible in both the version-2 and version-3 databases, are computed through band-ratio algorithms (Zibordi et al. 2009b). When available, as for the Adriatic, Baltic and Black Sea sites (see Zibordi et al. 2009a, 2015), regional algorithms are applied. Conversely, the OC2v4 algorithm (O’Reilly et al. 2000) developed relying on a global dataset representative of both oligotrophic and eutrophic waters, is used. Because of this, AERONET-OC Chla values need to be considered best estimates with uncertainties that may largely vary across sites.

With reference to Morel et al. (2002), by assuming Case-1 waters, simulated values of ℜ and f/Q included in the f/Q-2002 and f/Q-2013 tables were determined as a function of the relevant variables. Taking into account the different f/Q values, Fig. 4 shows a basic comparison of the correction factors CfrChla extracted from the f/Q-2002 and f/Q-2013 lookup tables. The analysis is restricted to the AERONET-OC viewing geometry (i.e., θ = 40°, φ = 90°) with W = 4 m s−1, Chla = 1 mg m−3, τa = 0.2 and various solar zenith angles (i.e., θ0 = [20°, 40°, 60°]). Results indicate a close agreement between CfrChla from the f/Q-2002 and f/Q-2013 table values. Still, differences exhibit an impact that may slightly exceed ±1% in LWN(λ) for the specific case considered.

Fig. 4.
Fig. 4.

(a) Correction factors CfrChla determined from the f/Q-2002 and f/Q-2013 lookup tables with parameters θ = 40°, φ = 90°, W = 4 m s−1, Chla = 1 mg m−3, τa = 0.2, and θ0 = [20°, 40°, 60°], and (b) the related percent differences ε between CfrChla determined from the f/Q-2013 and f/Q-2002 values.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

2) IOP-based correction approach

The IOP-based approach (Lee et al. 2011) relies on the application of the following equation relating LW(θ, φ, λ) to the inherent optical properties:
LW(θ,φ,λ)=Ed(λ)[G0w(θ,φ,θ0)+G1w(θ,φ,θ0)bbw(λ)a(λ)+bb(λ)]×bbw(λ)a(λ)+bb(λ)+[G0p(θ,φ,θ0)+G1p(θ,φ,θ0)bbp(λ)a(λ)+bb(λ)]bbp(λ)a(λ)+bb(λ),
where Ed(λ) is the downward irradiance. The symbol a(λ) indicates the water absorption coefficient defined by the contributions of pure seawater aw(λ), detritus plus CDOM adg(λ) and phytoplankton aph(λ). The symbol bb(λ) indicates the water backscattering coefficient given by the sum of the contributions of water molecules bbw(λ) and particles bbp(λ).
The approach implies the determination of a(λ) and bb(λ) from Eq. (4) using the model coefficients G0w(θ,φ,θ0), G1w(θ,φ,θ0), G0p(θ,φ,θ0) and G1p(θ,φ,θ0) derived from simulated LW(θ, φ, λ). From the retrieved values of a(λ) and bb(λ), LWN(λ) is then determined using the model coefficients G0w(0,0,0), G1w(0,0,0), G0p(0,0,0) and G1p(0,0,0) in agreement with
LWN(λ)=E0(λ)[G0w(0,0,0)+G1w(0,0,0)bbw(λ)a(λ)+bb(λ)]bbw(λ)a(λ)+bb(λ)+[G0p(0,0,0)+G1p(0,0,0)bbp(λ)a(λ)+bb(λ)]bbp(λ)a(λ)+bb(λ),
where E0(λ) is the mean extra-atmospheric solar irradiance.

d. Supervised spectrum-by-spectrum quality check

The supervised spectrum-by-spectrum quality check introduced in section 2 aims at creating a list of flagged LWN(λ) spectra. Drawback of such a solution is the relative time-consuming process and the potential for subjective decisions on dubious cases. Still, the process is fundamental to quality check LWN(λ) data from unique water types or related to occasional biological events, that could be rejected by an automatic scheme based on statistical methods.

A graphical user interface (GUI) supports the supervised spectrum-by-spectrum quality check of LWN(λ) prior being qualified for level 2.0 through a number of synoptic verifications. A snapshot of the GUI is presented in Fig. 5 to illustrate its various components identified as (i) reduced dimension spectral mapping (latent maps), (ii) spectral self-consistency, (iii) spectral time consistency, (iv) temporal evolution, and (v) additional details.

Fig. 5.
Fig. 5.

Snapshot of the GUI supporting the supervised spectrum-by-spectrum quality check of LWN(λ) data. The blue color is used to highlight the examined spectrum.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

The comprehensive specifics provided by these verifications should allow any experienced analyst to decide on the quality of each spectrum candidate to level 2.0. Decisions are saved in an exclusion list containing details on the LWN(λ) spectra to be excluded from level 2.0. It is, however, anticipated that an effort is ongoing to automatize most of the current quality assurance/control scheme.

With reference to Fig. 5 the various elements of this final quality check based on decisions from an experienced analyst are briefly summarized:

  1. Reduced dimension spectral mapping (latent maps, column 1 in Fig. 5) aims at verifying the pertinence of the LWN(λ) spectrum to a specific water type (i.e., chlorophyll-a-, CDOM-, or non-CDOM-dominated optically complex waters) and its statistical representativity in the AERONET-OC dataset going to be screened. This is achieved by projecting the LWN(λ) spectrum to be examined on a latent map obtained with reference LWN(λ) data whose accuracy and water type membership are known (D’Alimonte et al. 2012; Zibordi et al. 2011). The axes of the reference latent maps are the first two components from principal component analysis (PCA) applied to LWN(λ)/LWN(560) normalized reference spectra (upper map) and LWN(560) versus the first PCA component (lower map). This step is completed by projecting N additional AERONET-OC LWN(λ) spectra exhibiting PCA and LWN(560) features equivalent to that of the examined spectrum (here N = 25). The radius of the circle enclosing the previous spectra (i.e., the examined one and the additional N) in the map displaying the first two PCA components, provides qualitative indication on the actual statistical representativity of the shape of the examined spectrum into the dataset: a larger circle indicates a spectrum exhibiting a lower statistical representativity in the dataset to be screened. Overall the latent maps are most beneficial for newly established sites for which the water type needs to be identified or alternatively for sites exhibiting a variety of water types.
  2. Spectral self-consistency (column 2 in Fig. 5) aims at providing a physical visualization of the examined spectrum (displayed in blue) and of the additional N exhibiting similar features in the latent space. The LWN(λ) spectra are displayed both in absolute units and normalized values. The graphs aim at further assist with the assessment of the spectral consistency of the examined spectrum with respect to those having similar features in the latent maps.
  3. Spectral time consistency (column 3 in Fig. 5) aims at showing the examined spectrum (displayed in blue) in conjunction with all those pertaining to a specific time interval ΔT (here ΔT = 12 h). In this case, the graphs displaying the absolute and normalized spectra, provide means to compare the spectrum to be examined with those occurring close in time and expected to likely show similar spectral features. The example provided in the snapshot shows the examined spectrum exhibiting anomalously high absolute values at 490 nm and at 560 nm likely due to surface perturbations (e.g., glint, foam, floating material). It is mentioned that the red horizontal lines aim at highlighting any negative value of the examined spectrum.
  4. Temporal evolution (column 4 in Fig. 5) aiming at providing the actual temporal representation of each spectrum displayed in the spectral time-consistency graphs. These plots, which also display the spectral data in absolute units and normalized values (which implies a constant value of 1 at 560 nm for all the normalized spectra), are designed to help determining if any temporal change can be explained by the dynamic of the site. Actually, the spectrum showing anomalously high absolute values in the spectral time-consistency graph, is responsible for an abrupt change with time in the temporal evolution plots: this supports the hypothesis of occasional surface perturbations affecting LT(θ, φ, λ) data.
  5. Additional details (columns 5 and 6 in Fig. 5) provided by the GUI include a graphical representation of the month of relevance for the examined spectrum (displayed in blue) with respect to the overall temporal distribution of the data to be screened; an individual representation in absolute units and normalized values of the examined spectrum; and finally the lists of the spectra contributing to spectral self-consistency (upper list) and spectral time consistency (lower list) with information on date, time, solar zenith and azimuth angles.

4. Discussion

From 2002 up to now, AERONET has shown an increase of one order of magnitude of LWN(λ) data at the various quality levels (see Fig. 6) accompanied by an equivalent growth of data users. The discussion section is thus devoted to those elements relevant for AERONET-OC data applications. These embrace a verification of the equivalent performance of CE-318 and CE-318T systems, the representativity of sample AERONET-OC sites across various marine regions, and finally the impact of the Chla- and IOP-based correction approaches on LWN(λ) across AERONET-OC sites.

Fig. 6.
Fig. 6.

(a) Number of AERONET-OC LWN spectra at level 1.0, level 1.5, and level 2.0 determined for the period from 2002 until mid-2019 for which LWN(λ) have been available at all quality levels for the major sites, and (b) monthly means of the number of AERONET-OC LWN spectra across the same period with seasonal variations explained by the majority of current sites operated in the Northern Hemisphere.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

a. Equivalence of performance between radiometer series

An extended comparison of CE-318 and CE-318T derived LW (θ, φ, λ) and LWN(λ) data products was performed by operating collocated systems deployed at the AAOT on a jetty extending beyond the upper floor of the main structure (see Fig. 7).

Fig. 7.
Fig. 7.

(a) AAOT, and (b) detail of the deployment jetty hosting the CE-318 and CE-318T collocated radiometer systems operated to verify the equivalence of their performance.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

The comparison has been restricted to data from instruments deployed during November 2017–July 2018 and August 2019–September 2020. Data from an intermediate deployment have been excluded due to a significant bias affecting the radiance measurements from the CE-318, not explained by the pre- and postdeployment calibrations. The proposed comparison benefits from 753 matchups constructed from CE-318 and CE-318T quality-checked measurement sequences performed within 5 min from each other. The relevant quantities defining the measurement conditions [i.e., W, θ0, τa(560), and Chla] are provided in Fig. 8 in the form of distributions. To be noted, the values of wind speed exhibit median m = 2.54 m s−1 and standard deviation σ = 1.63 m s−1, which indicate that the comparison is restricted to relatively low-to-mild sea states (common to AERONET-OC data due to the glint filtering embedded in the quality assurance process). The values of θ0 vary from approximately 20° to 80° with median m = 43.1°, τa(560) exhibits median m = 0.13 and standard deviation σ = 0.10, and Chla estimated with a regional bio-optical algorithm (Berthon and Zibordi 2004) shows median m = 0.95 mg m−3 and standard deviation σ = 0.94 mg m−3.

Fig. 8.
Fig. 8.

Frequency distribution of (a) W, (b) θ0, (c) τa at 560 nm, and (d) Chla characterizing the AAOT LWN(λ) applied for the comparison of the CE-318 and CE-318T radiometer systems. The symbol N indicates the number of matchups, m is the median, and σ is the standard deviation of the considered quantities.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

Comparison results are provided in Fig. 9 for both LW(θ, φ, λ) and LWN(λ), restricted to equivalent center wavelengths. It is mentioned that the impact of differences in center wavelengths (e.g., 551 nm for CE-318 data and 560 nm for CE-318T) has been minimized through the application of band-shift corrections to the CE-318 data (Zibordi et al. 2009a). Results show LW(θ, φ, λ) from CE-318T with respect to those from CE-318T, exhibiting a spectrally averaged median of percent differences ψm = −0.45% and a spectrally averaged median of absolute differences |ψ|m = 2.95%. The corresponding statistical values for LWN(λ) show very close values (i.e., ψm = −0.04% and |ψ|m = 2.60%) indicating a minor impact by the normalization process.

Fig. 9.
Fig. 9.

Scatterplots of (a) LW(θ, φ, λ) and (b) LWN(λ) at matching center wavelengths (nm) from collocated CE-318T and CE-318 deployed at the AAOT. The symbol N indicates the number of matchups, r2 is the determination coefficient, rmsd is the root-mean-square deviation, |ψ|m is the spectrally averaged median of absolute percent differences, and ψm is the spectrally averaged median of percent differences, both of the latter determined with respect to CE-318T data.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

Spectral results from the LWN(λ) comparison are summarized in Table 5 for the equivalent center wavelengths. The value of ψm spectrally varies between approximately 0% and −2% while |ψ|m is generally within 3% in the blue–green spectral region but approaches 5% at 667 nm. The latter higher dispersion value in the red with respect to those in the blue–green spectral regions is explained by environmental perturbations enhanced by the very low radiance values characterizing LWN(λ) at 667 nm. Overall, the above differences are explained by the combined uncertainties affecting LWN(λ) AERONET-OC data assumed identical for both CE-318 and CE-318T products.

Table 5.

Statistical results from the spectral comparison of LWN (λ) from CE-318T and CE-318 radiometers at corresponding center wavelengths in the 412–667-nm spectral interval for the 753 matchups analyzed. The symbol ψm indicates the median of percent spectral differences, and |ψ|m is the median of absolute percent spectral differences [both with respect to the CE-318 LWN (λ) data]. The symbol Δm indicates the median of spectral differences, |Δ|m is the median of absolute spectral differences, rmsd is the root-mean-square deviation (all in units of mW cm−2 μm−1 sr−1), and r2 is the determination coefficient.

Table 5.

Figure 10 displays the scatterplots of the standard deviations σ(LT) at the sample center wavelengths 490 and 667 nm for the CE-318 and CE-318T quality assured sequences of the NT sea-radiance measurements leading to the determination of LT (θ, φ, λ). The similarity of σ(LT) for the data from the two systems indicates equivalent wave effects in the independent determinations of LT(θ, φ, λ). In fact, the lack of large systematic differences in σ(LT) suggests equivalent wave perturbations in sea-viewing measurements from the two radiometer systems. Remember that the relatively small σ(LT) is the result of the application of the glint filtering to measurement sequences during the quality assurance process (see section 2). Note that the large dispersion of the plotted σ(LT) values is explained by the random nature of wave perturbations characterizing the independent CE-318 and CE-318T measurements.

Fig. 10.
Fig. 10.

Scatterplots of the standard deviations σ(LT) [indicating either σ(LT318) and σ(LT318T)] at the (a) 490- and (b) 667-nm center wavelengths for the CE-318T and CE-318 measurement sequences contributing to the comparison of systems performance. The symbol N indicates the number of matchups, r2 is the determination coefficient, rmsd is the root-mean-square deviation, |ψ|m is the median of absolute percent differences, ψm is the median of percent differences, |Δ|m is the median of absolute differences, and Δm is the median of differences.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

These findings support the temporal consistency of the accuracy of AERONET-OC data at and across sites regardless of the instrument operated (i.e., CE-318 or CE-318T). Still, an analysis performed on the standard deviation σ(Li) of the Ni sky-radiance measurements leading to the determination of Li(θ′, φ, λ) for collocated CE-318 and CE-318T systems, has shown occasional higher values of σ(Li) for CE-318T than for CE-318 measurements. These higher values of σ(Li) are likely explained by instrumental noise and are currently under investigation. This noise may occasionally prevent raising to level 2.0 the LWN(λ) retrieved from CE-318T measurements when σ(Li) exceeds the thresholds permitted (see section 3).

b. Representativity of sites across water types

AERONET-OC LWN(λ) spectra from different sites exhibit features representative for various water types. Data from sample sites are displayed in Fig. 11. The different panels show spectra from Casablanca Platform (CPL) in the western Mediterranean Sea frequently characterized by chlorophyll-a-dominated waters, the AAOT in the northern Adriatic Sea characterized by moderately sediment-dominated waters, Gloria (GLR) in the northwestern Black Sea characterized by waters dominated by sediments and CDOM, and finally Gustaf Dalen Lighthouse (GDLT) exhibiting waters heavily dominated by CDOM.

Fig. 11.
Fig. 11.

AERONET-OC level 2.0 LWN(λ) spectra from the CPL, AAOT, GLR, and GDLT sites (corrected through the Chla approach). The thick black line indicates mean values, and the thick dashed lines indicate ±1σ; N indicates the number of spectra.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

The actual representativity of the LWN(λ) spectra from the previous sites with respect to those from a wide range of water types included in the Bio-Optical Mapping of Marine Properties (BiOMaP) dataset (Zibordi et al. 2011), is illustrated through the latent maps displayed in Fig. 12 featuring the shape and amplitude of spectra (D’Alimonte et al. 2012). Specifically, as already detailed in section 3, the LWN(λ) spectral shape is presented through the first two PCA components determined for the spectra normalized to their value at 560 nm (i.e., LWN(λ)/LWN(560)) while the amplitude is simply determined by the LWN(560) value.

Fig. 12.
Fig. 12.

Latent maps of AERONET-OC LWN(λ) spectra from (top) CPL, (top middle) AAOT, (bottom middle) GLR, and (bottom) GDLT produced with the first two PCA components applied to (left) LWN(λ)/LWN(560) and (right) LWN(560) vs the first PCA component. The isolines, displayed at 10% increments with increasing value from light yellow to black, illustrate the density distribution of projected AERONET-OC LWN(λ) for the various AERONET-OC sites. The circles indicate the projected BiOMaP LWN(λ), with colors identifying different water types: blue for oligotrophic and mesotrophic chlorophyll-a-dominated waters, red for CDOM-dominated waters, and green for any other optically complex water.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

With reference to Fig. 12, the circles indicate the projection of the BiOMaP LWN(λ) spectra classified applying a priori knowledge on their nature: oligotrophic and mesotrophic chlorophyll-a-dominated waters in blue, CDOM-dominated waters in red, and any other optically complex water in green. The contour plots designate the density distribution of the AERONET-OC projected LWN(λ) spectra. Specifically, the AERONET-OC projected spectra are mapped to a uniform grid where the number of occurrences at each grid element determines the contour lines.

Interestingly, as anticipated by the shape of the mean spectra displayed in Fig. 11, the AERONET-OC LWN(λ) spectra projected in Fig. 12 confirm ample representativity for the various water types with respect to the BiOMaP dataset. In summary, the above analysis supports the exploitation of AERONET-OC LWN(λ) in validation activities across a variety of water types such as chlorophyll-a-dominated waters at CPL, highly CDOM-dominated waters at GDLT, and finally waters dominated by different concentrations of sediments and CDOM at AAOT and GLR.

c. Impact of Chla- and IOP-based corrections for bidirectional effects

The impact of Chla- and IOP-based corrections for bidirectional effects are discussed by comparing LWN(λ) obtained with the two correction approaches. Before addressing results from the comparison, it is reminded that the Cha approach was conceived for Case-1 waters: consequently, its application to optically complex waters may become the source of large uncertainties. Still, the Chla approach is applied to AERONET-OC data as well as to satellite ocean color radiometric products from various missions. Because of this, the comparison of the two different correction approaches using AERONET-OC data from a variety of water types is of relevance for the user community.

An additional element requiring clarification is the application of both CfrChla and CfrIOP up to the 667-nm center wavelength for marine and up to 709 nm for lake data. Thus, corrections CfrChla and CfrIOPare not applied to LW(θ, φ, λ) data at center wavelengths beyond 709 nm for which the uncertainties are expected to exceed several tens of percent.

Results from comparisons performed for AERONET-OC representative sites (i.e., CPL, AAOT, GLR and GDLT) are summarized in Fig. 13 through scatterplots of LWNIOP(λ) versus LWNChla(λ) to illustrate the relative spectrally averaged impact of the two corrections, and additionally with graphs showing the mean and related standard deviation of the spectral corrections (i.e., CfrChla and CfrIOP for the Chla- and IOP-based approaches, respectively).

Fig. 13.
Fig. 13.

(left) Scatterplots of LWNIOP(λ) vs LWNChla(λ) at matching center wavelengths (nm) restricted to the visible spectral region for sample AERONET-OC sites representative of a variety of water types: CPL, AAOT, GLR, and GDLT. (right) Percent spectral corrections Cfr, indicating either CfrChla (blue) or CfrIOP (red) for the various AERONET-OC sites. Symbols ψm and |ψ|m indicate the spectrally averaged median of percent differences and of absolute percent differences, respectively, between the corrections CfrIOP and CfrChla, N indicates the number of spectra, rmsd is the root-mean-square deviation, and r2 is the determination coefficient.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

As expected, both the scatterplots and the graphs displaying corrections indicate relatively low absolute differences between methods for CPL characterized by Case-1 waters. In this specific case, notable is the small spectral dependence of both corrections.

A higher disagreement between CfrChla and CfrIOP values is shown for AAOT, GLR and GDLT, all characterized by optically complex waters. Regardless of the relatively small spectrally averaged values of ψm, which are all within approximately ±2% and consequently suggest minor biases between the LWNIOP(λ) and LWNChla(λ) data products, CfrIOP and CfrChla exhibit marked spectral differences approaching mean values of ±5%. Notable is the higher spectral dependence of CfrChla with respect to CfrIOP with corrections that may exceed −15% at 560 nm for CfrChla versus approximately −10% for CfrIOP (see the panels referring to GLR and GDLT in Fig. 13). This difference varies with the solar zenith angle and thus exhibits a seasonal dependence. Evidence is provided in Fig. 14 at the 490 and 667 nm center wavelengths for the CPL and AAOT sites, respectively, representative for chlorophyll-a-dominated and optically complex waters.

Fig. 14.
Fig. 14.

Percent differences ε between LWNIOP(λ) and LWNChla(λ) at the 490- and 667-nm center wavelengths as a function of θ0 for CPL and AAOT sites. The gray dashed lines indicate the fitted values.

Citation: Journal of Atmospheric and Oceanic Technology 38, 4; 10.1175/JTECH-D-20-0085.1

We also mention that both the Chla- and IOP-based correction approaches are sensitive to the spectral values of extra-atmospheric solar irradiance E0(λ). For the Chla-based approach the impact on LWN(λ) comes from the determination of Chla through band-ratio algorithms relying on the remote sensing reflectance RRS(λ) [where RRS(λ) = LWN(λ)/E0(λ)]. In the specific case of the version-2 and version-3 databases, applying the ASTM E490 (Tobiska 2002) and NRL SSI2 (Coddington et al. 2016) E0(λ), respectively, the spectral differences affecting LWN(λ) are generally lower than 0.5% across the visible spectrum. For the IOP-based approach, as a result of the application of Eq. (5), the impact is proportional to the percent difference between E0(λ) values, which can exceed several percent (Shanmugam and Ahn 2007).

The capability of providing LWN(λ) data independently determined applying CfrChla and CfrIOP regardless of the water type, maximize the potentials for the exploitation of AERONET-OC data in validation exercises requiring consistent corrections for bidirectional effects in both in situ and satellite data, often varying across missions and applications.

5. Summary and conclusions

AERONET-OC was conceived to support satellite ocean color activities through globally distributed standardized instruments operated on offshore fixed structures. After an early development phase (Zibordi et al. 2004, 2006) and successive consolidation (Zibordi et al. 2009b), AERONET-OC increased from the initial five sites constituting the network in 2006 to 31 in 2020. Concurrently, it benefitted from a number of investigations fundamental for a quantitative application of its data products. These studies further refined the quality assurance methods, addressed uncertainties across sites characterized by different water types, assessed assumptions supporting the determination of LWN(λ), verified the applicability of alternative approaches for the minimization of bidirectional effects, consolidated knowledge on the impact of spectral perturbations by deployment structures and of adjacency effects in satellite data. Additional efforts were placed in verifying and consolidating new technological advances allowing to extend the number of spectral bands from the 9 of CE-318 radiometer systems to the 12 of CE-318T ones.

This work documents recent AERONET-OC advances on hardware and data processing. Specifically, considering the transition from CE-318 to CE-318T radiometer systems, the work provides evidence of the equivalence of instruments performance and data products. Additionally, after showing the variety of water types characterizing the AERONET-OC marine sites, the work illustrates differences between the Chla-based correction approach proposed for chlorophyll-a-dominated waters and the IOP-based one expected to be applicable to both chlorophyll-a-dominated and optically complex waters. Comparable corrections for bidirectional effects are shown by the two approaches at a site characterized by oligotrophic-mesotrophic chlorophyll-a-dominated waters. As expected, corrections exhibit (absolute) differences exceeding several percent with a pronounced spectral dependence for optically complex waters.

Note also that the AERONET-OC version-3 database includes LWN(λ) from either CE-318 9-channel and CE-318T 12-channel radiometer systems corrected for bidirectional effects applying both the Chla- and IOP-based approaches regardless of the water type. This solution aims at ensuring the widest support to the application of AERONET-OC data in matchup analysis for satellite data products from various missions.

Acknowledgments

Acknowledgments are given to the entire AERONET team for the effort to include and sustain the ocean color component in the AERONET observational network. Extensive acknowledgments are also given to Bernard Gentili from the Observatoire Océanologique de Villefranche-sur-Mer (France) for providing access to the updated version of the f/Q tables. Acknowledgments are given to Tamito Kajiyama for assisting with the programming of the routines required for the IOP-based corrections.

Data availability statement

AERONET-OC data are available from NASA (https://aeronet.gsfc.nasa.gov/new_web/ocean_color.html).

APPENDIX A

Glossary

AAOT

Acqua Alta Oceanographic Tower

BiOMaP

Bio-Optical Mapping of Marine Properties

AERONET

Aerosol Robotic Network

AERONET-OC

Ocean Color Component of AERONET

CDOM

Colored dissolved organic matter

CPL

Casablanca Platform

DCP

Data collection platform

GDLT

Gustaf Dalen Lighthouse Tower

GLR

Gloria Platform

GUM

Guide to the Expression of Uncertainty in Measurements

HLT

Helsinki Lighthouse Tower

GPRS

General Packet Radio Service

GUI

Graphical user interface

IOP

Inherent optical properties

MODIS

Moderate Resolution Imaging Spectroradiometer

OLCI

Ocean Land Color Instrument

PACE

Plankton, Aerosol, Cloud and Ocean Ecosystem

PCA

Principal component analysis

SeaPRISM

SeaWiFS Photometer Revision for Incident Surface Measurements

SeaWiFS

Sea-Viewing Wide Field-of-View Sensor

SD

Secure Digital

VIIRS

Visible Infrared Imaging Radiometer Suite

APPENDIX B

Primary Symbols

a

Absorption coefficient of natural water

adg

Absorption coefficient of detritus plus CDOM

aph

Absorption coefficient of phytoplankton

aw

Absorption coefficient of pure seawater

bb

Backscattering coefficient of natural water

bbp

Backscattering coefficient of particles

bbw

Backscattering coefficient of water molecules

Chla

Chlorophyll-a concentration

Cfr

Correction factor for bidirectional effects

CfrChla

Correction factor for bidirectional effects relying on Chla

CfrIOP

Correction factor for bidirectional effects relying on IOP

D

Day of the year

E

Direct sun irradiance

E0

Extra-atmospheric sun irradiance

Ed

Total downward irradiance

f

Parameter relating irradiance and IOPs

G0w,G1w,G0p, and G1p

Model parameters

Li

Sky radiance

LT

Total radiance from the sea

LW

Water-leaving radiance

LWN

Normalized water-leaving radiance

Ni

Number of successive sky-radiance observations for the determination of Li

NT

Number of successive sea-radiance observations for the determination of LT

Q

Q factor

RRS

Remote sensing reflectance

Parameter defining the water surface reflection/refraction

td

Diffuse atmospheric transmittance

W

Wind speed

Δm

Median of differences

|Δ|m

Median of absolute differences

θ

Viewing angle for sea-radiance observations

θ0

Sun zenith angle

θ

Viewing angle for sky-radiance observations

λ

Wavelength

ρ

Water surface reflectance factor

τa

Aerosol optical depth

σ(Li)

Standard deviation of the Ni sky-radiance observations

σ(LT)

Standard deviation of the NT sea-radiance observations

φ

Relative azimuth between sun and sensor

φ0

Sun azimuth

ψ

Mean of percent differences

|ψ|

Mean of absolute percent differences

ψm

Median of percent differences

|ψ|m

Median of absolute percent differences

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