AERONET-OC: A Network for the Validation of Ocean Color Primary Products

Zibordi, Giuseppe; Mélin, Frédéric; Berthon, Jean-François; Holben, B. N.; Slutsker, I.; Giles, D. M.; D’Alimonte, Davide; Vandemark, D.; Feng, Hui; Schuster, Gregory L.; Fabbri, B. E.; Kaitala, Seppo; Seppälä, Jukka

doi:10.1175/2009jtecho654.1

Cited by 344 publications

(264 citation statements)

References 41 publications

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“…SeaPRISM is regularly operated at the AAOT from a deployment platform located in the western corner of the superstructure at approximately 15 m above the sea level (Zibordi et al, 2009c). Measurements performed with a FAFOV of 1.2 • every 30 min for the determination of L w (λ) at a number of center-wavelengths including 412, 443, 488, 531, 551, 670 nm (Zibordi et al, 2009c) are (i) the direct sun irradiance E s (θ 0 , φ 0 , λ) acquired to determine the atmospheric optical thickness τ a (λ) used for the theoretical computation of E d (0 + , λ), and (ii) a sequence of 11 sea-radiance measurements for determining L T (θ, φ, λ) and of 3 skyradiance measurements for determining L i (θ , φ, λ). These sequences are serially repeated for each λ with φ = 90 • , θ = 40 • and θ = 140 • .…”

Section: Seaprismmentioning

confidence: 99%

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In situ determination of the remote sensing reflectance: an inter-comparison

et al. 2012

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Abstract. Inter-comparison of data products from simultaneous measurements performed with independent systems and methods is a viable approach to assess the consistency of data and additionally to investigate uncertainties. Within such a context the inter-comparison called Assessment of In Situ Radiometric Capabilities for Coastal Water Remote Sensing Applications (ARC) was carried out at the Acqua Alta Oceanographic Tower in the northern Adriatic Sea to explore the accuracy of in situ data products from various in-and above-water optical systems and methods. Measurements were performed under almost ideal conditions, including a stable deployment platform, clear sky, relatively low sun zenith angles and moderately low sea state. Additionally, all optical sensors involved in the experiment were inter-calibrated through absolute radiometric calibration performed with the same standards and methods. Inter-compared data products include spectral waterleaving radiance L w (λ), above-water downward irradiance E d (0 + ,λ) and remote sensing reflectance R rs (λ). Data products from the various measurement systems/methods were directly compared to those from a single reference system/method. Results for R rs (λ) indicate spectrally averaged values of relative differences comprised between −1 and +6 %, while spectrally averaged values of absolute differences vary from approximately 6 % for the above-water systems/methods to 9 % for buoy-based systems/methods. The agreement between R rs (λ) spectral relative differences and estimates of combined uncertainties of the inter-compared systems/methods is noteworthy.

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Section: Seaprismmentioning

confidence: 99%

“…An additional element is the need to minimize the effects of glint perturbations in L T (θ, φ, λ) and possibly the effects of cloud perturbations in L i (θ , φ, λ). This is achieved by deriving these values from the average of independent measurements satisfying strict filtering criteria (Zibordi et al, 2009c;Zibordi, 2012).…”

Section: Seaprismmentioning

confidence: 99%

In situ determination of the remote sensing reflectance: an inter-comparison

et al. 2012

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“…The adopted aerosol single scattering albedo is approximately 0.99 at each wavelength. Water characteristics have been extracted from in situ measurements collected since 1995 at the AAOT [1]. Land optical properties were extracted from the MODIS multiyear climatological snow-free aggregate black-sky (BSA) and white-sky (WSA) albedo data at λ=470, 555, 659 and 858 nm [9].…”

Section: Simulations Ofmentioning

confidence: 99%

“…The AERONET-OC network [1] of in-situ autonomous radiometer sensors for the measurement of aerosol optical thickness and water-leaving radiance has been established in the last years to allow for an accurate validation of retrieved satellite ocean color products. Consistent effort has been done over the years to investigate, quantify (and eventually correct) the sources of uncertainties affecting the validation process.…”

Section: Introductionmentioning

confidence: 99%

“…This signal contribution is usually neglected by conventional atmospheric correction schemes applied to ocean color imagery. Investigations on uncertainties induced by adjacency effects at AERONET-OC sites have been already performed using both a simple parametric relationship [1] and analyzing the spatial variability of satellitederived products along transects extending from the coast and intercepting selected sites [3]. The somehow contradictive results underlined the need to further investigate the topic, modeling the effects with significantly increased accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the adjacency effects at AERONET-OC sites

Bulgarelli¹,

Zibordi²,

Kiselev³

2013

AIP Conference Proceedings

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Abstract.A methodology is presented to quantify the adjacency effects induced in satellite ocean color radiometric data by the presence of land in coastal regions. Specifically, the adjacency radiance, defined as the difference in top-ofatmosphere (TOA) signal when accounting for and when neglecting nearby mainland, is parameterized to decouple the dependence on the optical properties of land and water from those of atmosphere and sea surface, and from measurement geometry. The methodology is applied for a set of realistic satellite observation conditions, along a transect in the Northern Adriatic Sea crossing the Acqua Alta Oceanographic Tower (AAOT, 45.31N, 12.51E) ocean color validation site. The newly developed Novel Adjacency pertUrbation Simulator for CoastAl Areas (NAUSICAA) 3D backward MonteCarlo code and the well established highly accurate plane-parallel FEM radiative transfer code, are used to simulate signal contributions at TOA. Results at relevant ocean color center-wavelengths for the AAOT site indicate average adjacency radiance contributions at TOA lower than ±0.5% in the visible spectral region, while reaching about 2% at 765 and 865 nm. Summer cases exhibit values above the average, while the opposite is observed for winter cases. As expected, the largest adjacency contributions occur for slanted satellite observations from over the land.

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A system to measure the data quality of spectral remote sensing reflectance of aquatic environments

Wei

Lee

Shang

2016

J. Geophys. Res. Oceans

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Spectral remote‐sensing reflectance (Rrs, sr−1) is the key for ocean color retrieval of water bio‐optical properties. Since Rrs from in situ and satellite systems are subject to errors or artifacts, assessment of the quality of Rrs data is critical. From a large collection of high quality in situ hyperspectral Rrs data sets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate the quality of an individual Rrs spectrum. This QA scheme consists of a unique Rrs spectral reference and a score metric. The reference system includes Rrs spectra of 23 optical water types ranging from purple blue to yellow waters, with an upper and a lower bound defined for each water type. The scoring system is to compare any target Rrs spectrum with the reference and a score between 0 and 1 will be assigned to the target spectrum, with 1 for perfect Rrs spectrum and 0 for unusable Rrs spectrum. The effectiveness of this QA system is evaluated with both synthetic and in situ Rrs spectra and it is found to be robust. Further testing is performed with the NOMAD data set as well as with satellite Rrs over coastal and oceanic waters, where questionable or likely erroneous Rrs spectra are shown to be well identifiable with this QA system. Our results suggest that applications of this QA system to in situ data sets can improve the development and validation of bio‐optical algorithms and its application to ocean color satellite data can improve the short‐term and long‐term products by objectively excluding questionable Rrs data.

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AERONET-OC: A Network for the Validation of Ocean Color Primary Products

Cited by 344 publications

References 41 publications

In situ determination of the remote sensing reflectance: an inter-comparison

In situ determination of the remote sensing reflectance: an inter-comparison

Modeling the adjacency effects at AERONET-OC sites

A system to measure the data quality of spectral remote sensing reflectance of aquatic environments

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