Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing

Bailey, Sean W.; Franz, Bryan A.; Werdell, P. Jeremy

doi:10.1364/oe.18.007521

Cited by 353 publications

(243 citation statements)

References 13 publications

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“…Remote measurements collected at large viewing angles (large atmospheric path lengths) or through elevated aerosol loads, where the water-leaving signal is a much smaller portion of the total observed radiance at the sensor, have higher R rs λ uncertainties than those collected through shorter atmospheric paths or clear atmospheres dominated by simple Rayleigh scattering. Turbid or highly reflective waters require additional corrections to separate the atmospheric signal from the water signal, which adds additional assumptions and inherent uncertainties [53]. Similarly, R rs λ retrievals obtained in the vicinity of land, clouds, or sun glint may be contaminated by stray light or atmospheric adjacency effects.…”

Section: Fig 2 Comparison Of Giop-dc and Ground Truth (In Situ)mentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

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365

319

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Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

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Section: Fig 2 Comparison Of Giop-dc and Ground Truth (In Situ)mentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

Self Cite

365

319

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show abstract

“…Approximately 7300 high-quality MODIS Aqua scenes remained after quality assurance screened out scenes with 80% or more cloud and/or sunglint contaminated pixels. These remaining scenes were processed from Level-1A to Level-2 using L2GEN and its standard atmospheric correction scheme [Ahmad et al, 2010;Bailey et al, 2010], an approach which has been identified as robust for optically complex waters such as those of Chesapeake Bay, USA . We acknowledge that the standard L2GEN atmospheric correction has not formally been tested over optically shallow waters.…”

Section: Modis Aqua Processing and Analysismentioning

confidence: 99%

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

et al. 2015

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A semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water-leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre-existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Group's L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). SWIM-derived values of the total non-water absorption coefficient at 443 nm, a t (443), the particulate backscattering coefficient at 443 nm, b bp (443), and the diffuse attenuation coefficient at 488 nm, K d (488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM-derived values of a t (443), b bp (443), and K d (443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

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“…10 also has a specific implication for atmospheric correction for satellite ocean-color remote sensing. Iterative approaches with ocean reflectance in the red and NIR bands for estimating the NIR ocean contribution have long been used (Stumpf et al 2003;Bailey et al 2010;Wang et al 2013a). However, results in Fig.…”

Section: Some Satellite Ocean-color Remote-sensing Implicationsmentioning

confidence: 99%

“…, 0.05. In addition, the optical feature of insensitive ocean reflectance in red band r wN (645) implies that the current National Aeronautics and Space Administration standard satellite ocean-color atmospheric correction algorithm, which uses red ocean reflectance to estimate the NIR ocean reflectance (Bailey et al 2010), may not be applicable for highly turbid waters. The SWIR atmospheric correction algorithm is required to derive accurate satellite water property products in ocean regions such as the Subei Shoal, Yangtze River Estuary, La Plata River Estuary, and Hangzhou Bay.…”

Section: Some Satellite Ocean-color Remote-sensing Implicationsmentioning

confidence: 99%

“…For turbid waters in coastal regions, however, the NIR black-ocean assumption is often invalid (Ruddick et al 2000;Siegel et al 2000;Wang and Shi 2005), leading to errors in the satellite-derived ocean-color products (Wang 2007;Wang et al 2009b). Some modifications have been implemented to account for the NIR ocean contributions for productive (but not very turbid) nearshore or coastal waters (Stumpf et al 2003;Bailey et al 2010). In effect, various investigators have tried to remove the NIR nL w (l) contributions from the top-ofatmosphere (TOA) radiances, so that a 'black pixel' could be provided for the Gordon and Wang (1994) type atmospheric correction algorithm.…”

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confidence: 99%

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

Shi

Wang

2014

Limnology & Oceanography

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Normalized water-leaving radiance spectra nL w (l) at the red, near-infrared (NIR), and shortwave infrared (SWIR) are quantified and characterized in highly turbid waters of the western Pacific using 3 yr (2009)(2010)(2011) observations from the Moderate Resolution Imaging Spectroradiometer on the satellite Aqua. nL w (645; red), nL w (859; NIR), and nL w (1240; SWIR) were higher in the coastal region and river estuaries, with SWIR nL w (1240) reaching up to , 0.2 mW cm 22 mm 21 sr 21 in Hangzhou Bay during winter. The NIR ocean-reflectance spectral shape represented by the ratio of the normalized water-leaving reflectance r wN (l) at the two NIR bands r wN (748) : r wN (869) is highly dynamic and region-dependent. The NIR spectral feature associated with the sediment source from the Yellow River and Ancient Yellow River is noticeably different from that of the Yangtze River. There are non-negligible SWIR nL w (1240) contributions for waters with the NIR nL w (859) . , 2.5 mW cm 22 mm 21 sr 21 . Estimation of the NIR ocean reflectance with iterative approaches might only be accurate for turbid waters with nL w (859) , , 1.5 mW cm 22 mm 21 sr 21 . Thus, the SWIR atmospherics correction algorithm for satellite ocean-color data processing is indispensable to derive accurate nL w (l) for highly turbid waters. Current existing satellite algorithms for chlorophyll a, diffuse attenuation coefficient at the wavelength of 490 nm (K d (490)), total suspended matter, and inherent optical properties (IOPs) using nL w (l) at the red band for coastal waters are limited and can only be applied to turbid waters with nL w (859) , , 1.5 mW cm 22 mm 21 sr 21 . Thus, the NIR nL w (l) measurements are required to characterize water properties for highly turbid waters. Based on the fact that pure water absorption is significantly larger than other absorption components in the NIR wavelengths, we show that it is feasible to analytically derive accurate IOP data for turbid waters with combined satellite-measured visible-NIR nL w (l) spectra data.

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Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing

Cited by 353 publications

References 13 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

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

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