A general ocean color atmospheric correction scheme based on principal components analysis: Part I. Performance on Case 1 and Case 2 waters

Gross-Colzy, Lydwine; Colzy, Stéphane; Frouin, Robert; Henry, Patrice

doi:10.1117/12.738508

Cited by 12 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These requirements are not achieved with the present satellite systems and state-of-the-art standard algorithms. Some techniques are promising, however, such as those proposed by Steinmetz et al (2011) and Gross-Colzy et al (2007a), which either exploit the fact that Sun glint and cloud signals are smooth spectrally and can be well represented by a simple polynomial or select the principal components of the TOA signal that are less influenced by atmospheric and surface effects. Accurate retrievals may be obtained by relaxing the reflectance threshold in the near infrared to 0.2, with the potential of increasing daily spatial coverage by over 50% in many areas.…”

Section: Atmospheric Correction Issuesmentioning

confidence: 99%

“…Neural networks (NNs) and other machine-learning techniques have seen considerable and diverse use in addressing the atmospheric correction problem (Schiller and Doerffer, 1999;Jamet et al, 2004Jamet et al, , 2005Brajard et al, 2006Brajard et al, , 2008Brajard et al, , 2012Gross-Colzy et al, 2007a;Schroeder et al, 2007;Fan et al, 2017). This is because NNs and in particular multilayer feedforward networks with non-linear transfer functions provide a universal method to approximate arbitrary non-linear functions (Hornik, 1989).…”

Section: Statistical Algorithmsmentioning

confidence: 99%

“…They can therefore be used either to solve the atmospheric correction problem directly by having observed reflectance and viewing geometry as input and water reflectance as output (Schroeder et al, 2007), or to model the radiative transfer equation RTEitself, where they replace the time-consuming solution of the RTE in an optimal estimation scheme (e.g., Brajard and Jamet references). In Gross-Colzy et al (2007a) the TOA reflectance, after correction for gaseous absorption and molecular scattering, is decomposed into principal components (PCs), and the PCs sensitive to the water reflectance are combined to retrieve the PCs of the water reflectance. This allows a reconstruction, therefore estimation, of the water reflectance.…”

Section: Statistical Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

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

show abstract

Section: Atmospheric Correction Issuesmentioning

confidence: 99%

Section: Statistical Algorithmsmentioning

confidence: 99%

Section: Statistical Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In the algorithm proposed by Gross-Colzy et al (2007a), the satellite reflectance is first decomposed into principal components. The components sensitive to the ocean signal are then combined to retrieve the principal components of the marine reflectance.…”

Section: B5 Neural Network Approachmentioning

confidence: 99%

Interactive Online Maps Make Satellite Ocean Data Accessible

Mikelsons¹,

Wang²

2018

Eos

View full text Add to dashboard Cite

show abstract

“…VPCA was selected as an applicable multivariate statistical approach for its ability to decrease the dimensionality of the data, eliminate collinearity among the data, and transform large datasets into smaller datasets of unrelated indices. This approach has been applied successfully in previous ocean color modeling research studies, where strong correlation models for Chl-a prediction were produced (Sathyendranath et al, 1994;Gao et al, 2000;Gross-Colzy et al, 2007;Ortiz et al, 2013;Ali et al, 2013;Ryan et al, 2016). PLSR was developed in the 1980s by Herman Wold and has since gained acceptance in its use for spectral analysis.…”

Section: Blue Green Modelsmentioning

confidence: 99%

Field Spectroscopy as a Tool for Enhancing Water Quality Monitoring in the ACE Basin, SC

Mayer¹,

Ali²

2017

JSCWR

View full text Add to dashboard Cite

The Ashepoo, Combahee, Edisto (ACE) Basin in South Carolina is one of the largest undeveloped estuaries in the Southeastern United States. This system is monitored and protected by several government agencies to ensure its health and preservation. However, as populations in surrounding cities rapidly expand and land is urbanized, the surrounding water systems may decline from an influx of contaminants, leading to hypoxia, fish kills, and eutrophication. Conventional in situ water quality monitoring methods are timely and costly. Satellite remote sensing methods are used globally to monitor water systems and can produce an instantaneous synopsis of color-producing agents (CPAs), including chlorophyll-a, suspended matter (TSM), and colored-dissolved organic matter by applying bio-optical models. In this study, field, laboratory, and historical land use land cover (LULC) data were collected during the summers of 2002, 2011, 2015, and 2016. The results indicated higher levels of chlorophyll, ranging from 2.94 to 12.19 μg/L, and TSM values were from 60.4 to 155.2 mg/L between field seasons, with values increasing with time. A model was developed using multivariate, partial least squares regression (PLSR) to identify wavelengths that are more sensitive to chlorophyll-a (R2 = 0.49; RMSE = 1.8 μg/L) and TSM (R2 = 0.40; RMSE = 12.9 mg/L). The imbrication of absorption and reflectance features characterizing sediments and algal species in ACE Basin waters make it difficult for remote sensors to distinguish variations among in situ concentrations. The results from this study provide a strong foundation for the future of water quality monitoring and for the protection of biodiversity in the ACE basin.

show abstract

A general ocean color atmospheric correction scheme based on principal components analysis: Part I. Performance on Case 1 and Case 2 waters

Cited by 12 publications

References 0 publications

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

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

Interactive Online Maps Make Satellite Ocean Data Accessible

Field Spectroscopy as a Tool for Enhancing Water Quality Monitoring in the ACE Basin, SC

Contact Info

Product

Resources

About