Use of a neurovariational inversion for retrieving oceanic and atmospheric constituents from ocean colar imagery: A feasibility study

Journal of Atmospheric and Oceanic Technology

Volumn 22, Issue 4, 2005, Pages 460-475

  Jamet, C ^a,b   Thiria, Sylvie ^a   Moulin, C ^c   Crepon, M ^a  

^a SORBONNE UNIVERSITÉ   (France)

^b ACRI ST   (France)

^c LABORATOIRE DES SCIENCES DU CLIMAT ET DE L ENVIRONNEMENT   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CHLOROPHYLL; DATA REDUCTION; IMAGE SENSORS; METEOROLOGY; NEURAL NETWORKS; SATELLITES; SENSITIVITY ANALYSIS; SIGNAL PROCESSING;

ATMOSPHERIC PARAMETERS; NEUROVARIATIONAL INVERSION; OLIGOTROPHIC REGIONS; RADIATIVE TRANSFER EQUATIONS;

OCEANOGRAPHY;

OCEAN COLOR; RADIATIVE TRANSFER; REMOTE SENSING; SEAWIFS;

EID: 20444474440     PISSN: 07390572     EISSN: None     Source Type: Journal    
DOI: 10.1175/JTECH1688.1     Document Type: Article

References (23)

1. Badran, F., S. Thiria, and M. Crepon, 1991: Wind ambiguity removal by the use of neural networks techniques. J. Geophys. Res., 96C, 20 521-20 529.
2. Bishop, C. M., 1995: Neural Networks for Pattern Recognition. Oxford University Press, 482 pp.
3. Bricaud, A., E. Bosc, and D. Antoine, 2002: Algal biomass and sea surface temperature in the Mediterranean basin intercomparison of data from various satellite sensors, and implications for primary production estimates. Remote Sens. Environ., 81, 163-178.
4. Chomko, R., and H. R. Gordon, 1998: Atmospheric correction of ocean color imagery: Use of a Junge power-law size distribution with variable refractive index to handle aerosol absorption. Appl. Opt., 37, 5560-5572.
5. Claustre, H., and Coauthors, 2002: Is desert dust making oligotrophic water greener? Geophys. Res. Lett., 29, 1469, doi: 10.1029/ 2001GL014056.
6. Cybenko, G., 1989: Approximation by superposition of a sigmoidal function. Math. Control Signal Syst., 2, 303-313.
7. Deschamps, P. Y., M. Herman, and D. Tanre, 1983: Modeling of the atmospheric effects and its application of the remote sensing of ocean color. Appl. Opt., 22, 3751-3758.
8. D'Ortenzio, F., S. Marullo, M. Ragni, M. Ribera d'Alcala, and R. Santoleri, 2002: Validation of empirical SeaWiFS algorithms for chlorophyll-a retrieval in the Mediterranean Sea: A case study for oligotrophic seas. Remote Sens. Environ., 82, 79-94.
9. Gordon, H. R., 1997: Atmospheric correction of ocean color imagery in the Earth Observing System era. J. Geophys. Res., 102, 17 081-17 106.
10. Gordon, H. R.,and M. Wang, 1994a: Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm. Appl. Opt., 33, 443-452.
11. Gordon, H. R., and M. Wang, 1994b: Influence of oceanic whitecaps on atmospheric correction of ocean-color sensors. Appl. Opt., 33, 7754-7763.
12. Gordon, H. R., D. K. Clark, J. W. Brown, O. B. Brown, R. H. Evans, and W. W. Broenkow, 1983: Phytoplankton pigment concentrations in the Middle Atlantic Bight: Comparison between ship determinations and Coastal Zone Color Scanner. Appl. Opt., 22, 20-36.
13. Gordon, H. R., J. W. Brown, and R. H. Evans, 1988a: Exact Rayleigh scattering calculations for use of the Nimbus 7 coastal zone color scanner. Appl. Opt., 26, 2111-2122.
14. Gordon, H. R., O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, 1988b: A semi-analytic radiance model of ocean color. J. Geophys. Res., 93, 10 909-10 924.
15. Gordon, H. R., T. Du, and T. Zhang, 1997: Remote sensing of ocean color imagery and aerosol properties: Resolving the issue of aerosol absorption. Appl. Opt., 36, 8670-8684.
16. Hornik, K., M. Stinchcomb, and F. E. Muller-Karger, 1989: Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
17. Junge, C. E., 1958: Atmospheric Chemistry Advances in Geophysics, Vol. 4, Academic Press, 1-108.
18. McClain, C. R., E. J. Ainsworth, R. A. Barnes, R. E. Eplee Jr., F. S. Patt, W. D. Robinson, M. Wang, and S. W. Bailey, 2000: SeaWiFS postlaunch calibration and validation analyses, Part 1. NASA Tech. Memo 206982, 85 pp.
19. O'Reilly, J. R., and Coauthors, 1998: Ocean color chlorophyll algorithms for SeaWiFS. J. Geophys. Res., 103, 24 937-24 953.
20. Pinkus, A., 1999: Approximation theory of the MLP model in neural networks, Acta Numer., 143-195.
21. Shettle, E. P., and R. W. Fenn, 1979: Models of the atmospheric aerosols and their optical properties. Air Force Geophysical Laboratory (AFGL), Rep. AFGL-TR-79-0214, Hanscomb Airforce Base, MA.
22. Thiria, S., C. Mejia, F. Badran, and M. Crepon, 1993: A neural network approach for modelling transfer functions: Application for wind retrieval from spaceborne scatterometer data. J. Geophys. Res., 98, 22 827-22 841.
23. Yang, H., and H. R. Gordon, 1997: Remote sensing of ocean color: Assessment of the water-leaving radiance bidirectional effects on the atmospheric diffuse transmittance. Appl. Opt., 36, 7887-7897.