An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation

Werdell, P. Jeremy; Bailey, Sean W.

doi:10.1016/j.rse.2005.07.001

Cited by 601 publications

(451 citation statements)

References 69 publications

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“…We acquired coincident observations of in situ and synthesized R rs λ , b bp λ , a dg λ , a ϕ λ , and C a from the NASA bio-Optical Marine Algorithm Data set (NOMAD) [32] and the IOCCG Ocean Colour Algorithms Working Group synthetic data set [1]. We also acquired coincident Level-2 satellite-to-in situ match-ups for the NASA SeaWiFS and MODISA instruments from the OBPG (http://seabass.gsfc.nasa .gov/seabasscgi/validation_search.cgi).…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The elevated (>1) and reduced (<1) ratios of b bp λ and a λ suggest incomplete R rs λ -IOP closure in NOMAD ( Table 2). The R rs λ , b bp λ , and a λ were collected using different instruments and methods, and uncertainties associated with varied measurements and methodologies are neither negligible nor spectrally independent [32]. The divergent ratios of a dg λ and a ϕ λ with increasing wavelength likely result from low R rs λ and absorption signals at long wavelengths and imperfect parameterization of S dg and S bp , both of which control the rate of decline of their eigenvectors.…”

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

confidence: 99%

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Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

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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: Data Acquisitionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…When the model algorithm coefficients are calculated from only a subset of data, similar to that which is available in the real world (e.g. NOMAD, Werdell and Bailey, 2005), the resulting function is similar to those used for SeaWiFS and MODIS Chl a products (Table 1; Fig. 4).…”

Section: Discussion and Summarymentioning

confidence: 99%

“…OC4 for SeaWiFS and OC-CCI, OC3M-547 for MODIS). The NASA bio-Optical Marine Algorithm Dataset (NOMAD; Werdell and Bailey, 2005) was constructed from coincident radiometric observations and phytoplankton pigment data and have been extensively used for satellite-derived Chl a (and other) algorithm development. For direct comparison between real-world and emergent within-model relationships, we therefore sub-sample the model "actual" Chl a and reflectance ratio, X, at locations and dates nearest in time and space to those in NOMAD.…”

Section: Approach 1: Global Coefficients Using Subsampled Fields (Gs)mentioning

confidence: 99%

“…There have also been many attempts to develop more mechanistically derived algorithms, using for instance known relationships between absorption, scattering and reflectance (e.g. Werdell et al, 2013a). Here we focus on the Chl a estimated from the blue/green reflectance as it is still the most commonly known product, and until very recently used in products downloaded from both NASA and the European Space Agency (ESA) data portals, as well as merged products such as the Ocean Colour Climate Change Initiative (OC-CCI).…”

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confidence: 99%

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Modelling ocean-colour-derived chlorophyll <i>a</i>

2018

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Abstract. This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called "derived Chl a" in this paper). We compare the derived Chl a to the "actual" model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that regionspecific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl a lags the actual Chl a by days and sometimes weeks. These results indicate that care should also be taken when studying phenology through satellite-derived products of Chl a. This study also reemphasizes that ocean-colourderived Chl a is not the same as the real in situ Chl a. In fact the model derived Chl a compares better to real-world satellite-derived Chl a than the model actual Chl a. Modellers should keep this is mind when evaluating model output with ocean colour Chl a and in particular when assimilating this product. Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.

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An operational model for filling the black strips of the MODIS 1640 band and application to atmospheric correction

Chen

Cui

Lin

2013

J. Geophys. Res. Oceans

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[1] In this study, a LIMM (linear interpolation model for rebuilding the black strips of MODIS 1640 nm) model is proposed to rebuild the black strips of MODIS (moderate resolution imaging spectroradiometer) 1640 images, and for improving the performance of TSWNR (traditional 1240 nm band-based SWIR-NIR atmospheric correction model) model in deriving remote sensing reflectance in turbid coastal waters. By comparison with the field measurements, both the L3MAC (LIMM model-derived MODIS 1640 band-based atmospheric correction model) and TSWNR models can be used to derive remote sensing reflectance in the green and red regions, but the former is superior to the latter. Especially in summer in the Bohai Sea, use of the L3MAC model in estimating remote sensing reflectance decreases the MRE (mean root mean square error) values of estimation by >14% from the TSWNR model. Due to the great amount of detector noise in the MODIS SWIR wavelengths, there is still a >19% residual uncertainty in the L3MAC model-derived remote sensing, particularly in the NIR (near-infrared) and shortest blue regions, and both the L3MAC and TSWNR models produce >60% uncertainty in remote sensing reflectance retrievals. The success of the application of the L3MAC model to satellite data depends heavily on the detector noise in the MODIS SWIR (shortwave infrared) wavelengths. Our study suggests that more attention should be paid to how to minimize the effects of detector noise on the atmospheric correction results in the future.Citation: Chen, J., T. Cui, and C. Lin (2013), An operational model for filling the black strips of the MODIS 1640 band and application to atmospheric correction,

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An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation

Cited by 601 publications

References 69 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

Modelling ocean-colour-derived chlorophyll <i>a</i>

An operational model for filling the black strips of the MODIS 1640 band and application to atmospheric correction

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An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation

Cited by 601 publications

References 69 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

Modelling ocean-colour-derived chlorophyll &lt;i&gt;a&lt;/i&gt;

An operational model for filling the black strips of the MODIS 1640 band and application to atmospheric correction

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Modelling ocean-colour-derived chlorophyll <i>a</i>