OSA's Digital Library

Applied Optics

Applied Optics


  • Vol. 41, Iss. 30 — Oct. 20, 2002
  • pp: 6260–6275

Inversion of oceanic constituents in case I and II waters with genetic programming algorithms

Malik Chami and Denis Robilliard  »View Author Affiliations

Applied Optics, Vol. 41, Issue 30, pp. 6260-6275 (2002)

View Full Text Article

Enhanced HTML    Acrobat PDF (343 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



A stochastic inverse technique based on a genetic programming (GP) algorithm was developed to invert oceanic constituents from simulated data for case I and case II water applications. The simulations were carried out with the Ordre Successifs Ocean Atmosphere (OSOA) radiative transfer model. They include the effects of oceanic substances such as algal-related chlorophyll, nonchlorophyllous suspended matter, and dissolved organic matter. The synthetic data set also takes into account the directional effects of particles through a variation of their phase function that makes the simulated data realistic. It is shown that GP can be successfully applied to the inverse problem with acceptable stability in the presence of realistic noise in the data. GP is compared with neural network methodology for case I waters; GP exhibits similar retrieval accuracy, which is greater than for traditional techniques such as band ratio algorithms. The application of GP to real satellite data [a Sea-viewing Wide Field-of-view Sensor (SeaWiFS)] was also carried out for case I waters as a validation. Good agreement was obtained when GP results were compared with the SeaWiFS empirical algorithm. For case II waters the accuracy of GP is less than 33%, which remains satisfactory, at the present time, for remote-sensing purposes.

© 2002 Optical Society of America

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(030.5620) Coherence and statistical optics : Radiative transfer
(200.4260) Optics in computing : Neural networks
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(350.4990) Other areas of optics : Particles

Original Manuscript: December 13, 2001
Published: October 20, 2002

Malik Chami and Denis Robilliard, "Inversion of oceanic constituents in case I and II waters with genetic programming algorithms," Appl. Opt. 41, 6260-6275 (2002)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. A. Morel, H. R. Gordon, “Report of the working group on water color,” Boundary-Layer Meteorol. 18, 343–355 (1980). [CrossRef]
  2. H. R. Gordon, D. K. Clark, J. W. Brown, O. B. Evans, W. W. Broenkow, “Phytoplankton pigment concentration in the middle Atlantic bight, comparison of ship determination and CZCS estimates,” Appl. Opt. 22, 20–36 (1983). [CrossRef] [PubMed]
  3. J. Aiken, G. F. Moore, D. K. Clark, C. C. Trees, The SeaWiFS CZCS-Type Pigment Algorithm, Vol. 29 of SeaWiFS Tech. Rep. Series, S. B. Hooker, E. R. Firestone, eds., (NASA, Washington, D.C., 1995).
  4. J. E. O’Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, C. McClain, “Ocean color algorithms for SeaWiFS,” J. Geophys. Res. 103, 24,937–24,953 (1998). [CrossRef]
  5. H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, D. K. Clark, “A semi-analytic radiance model of ocean color,” J. Geophys. Res. 93, 10,909–10,924 (1988). [CrossRef]
  6. A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (Case I waters),” J. Geophys. Res. 93, 10,749–10,768 (1988). [CrossRef]
  7. A. Morel, Optics of Marine Particles and Marine Optics, Vol. G27 of NATO ASI Series on Particle Analysis in Oceanography, S. Demers, ed. (Springer-Verlag, Berlin, 1991).
  8. L. Prieur, S. Sathyendranath, “An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments, dissolved organic matter and other particulate materials,” Limnol. Oceanogr. 26, 671–689 (1981). [CrossRef]
  9. R. M. Pope, E. S. Fry, “Absorption spectrum (380–700 nm) of pure water. II. Integrating measurements,” Appl. Opt. 36, 8710–8723 (1997). [CrossRef]
  10. A. Morel, “Optical properties of pure water and pure seawater,” in Optical Aspects of Oceanography, N. G. Jerlov, E. S. Nielsen, eds. (Academic, San Diego, Calif., 1974), pp. 1–24.
  11. Z. Lee, K. L. Carder, S. K. Hawes, R. G. Steward, T. G. Peacock, C. O. Davis, “Model for the interpretation of hyperspectral remote sensing reflectance,” Appl. Opt. 33, 5721–5732 (1994). [CrossRef] [PubMed]
  12. K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, B. G. Mitchell, “Reflectance model for quantifying chlorophyll ‘a’ in the presence of productivity degradation products,” J. Geophys. Res. 96, 20,599–20,611 (1991). [CrossRef]
  13. S. Tassan, “Local algorithm using SeaWiFS data for the retrieval of phytoplankton pigments, suspended matter, and yellow substance in coastal waters,” Appl. Opt. 33, 2369–2378 (1994). [CrossRef] [PubMed]
  14. G. F. Moore, J. Aiken, S. Lavender, “The atmospheric correction of water color and the quantitative retrieval of suspended particulate matter in case II waters: application to MERIS,” Int. J. Remote Sens. 20, 1713–1733 (1999). [CrossRef]
  15. K. L. Carder, F. R. Chen, Z. P. Lee, S. K. Hawes, “Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll ‘a’ and absorption with bio-optical domains based on nitrate depletion temperatures,” J. Geophys. Res. 104, 5403–5421 (1999). [CrossRef]
  16. S. Sathyendranath, T. Platt, “Analytic model of ocean color,” Appl. Opt. 36, 2620–2629 (1997). [CrossRef] [PubMed]
  17. R. Doerffer, J. Fischer, “Concentration of chlorophyll, suspended matter, and gelbstoff in case II waters derived from Coastal Zone Color Scanner data with inverse modeling methods,” J. Geophys. Res. 99, 7457–7466 (1994). [CrossRef]
  18. H. Schiller, R. Doerffer, “Neural network for emulation of an inverse model—operational derivation of case II water properties from MERIS data,” Int. J. Remote Sens. 20, 1735–1746 (1999). [CrossRef]
  19. H. Krawczyk, A. Neumann, T. Walzel, G. Zimmermann, “Investigation of interpretation possibilities of spectral high dimensional measurements by means of principal component analysis—a concept for physical interpretation of those measurements,” in Recent Advances in Sensors, Radiometric Calibration, and Processing of Remotely Sensed Data, P. S. Chavez, R. A. Schowengerdt, eds., Proc. SPIE1938, 401–411 (1993).
  20. H. Krawczyk, A. Neumann, M. Hetscher, “Mathematical and physical background of principal component inversion,” in Proceedings of the 3rd International Workshop on MOS-IRS and Ocean Color (Wissenschaft und Technik-Verlag, Berlin, 1999), pp. 83–92.
  21. A. Neumann, H. Krawczyk, T. Walzel, “A complex approach to quantitative interpretation of spectral high resolution imagery,” in Proceedings of the Third Thematic Conference on Remote Sensing for Marine and Coastal Environments, by P. Bank, ed. (Environmental Research Institute of Michigan, Ann Arbor, Michigan, 1995), pp. II-641–II-652.
  22. A. Neumann, M. Hetscher, H. Krawczyk, C. Tschentscher, “Methodological aspects of principal component inversion for case II applications,” in Proceedings of the 2nd International workshop on MOS–IRS and Ocean Color, (Institute of Space Sensor Technology, Berlin, 1998), pp. 163–170.
  23. M. Chami, R. Santer, E. Dilligeard, “Radiative transfer model for the computation of radiance and polarization in an ocean-atmosphere system: polarization properties of suspended matter for remote sensing,” Appl. Opt. 40, 2398–2416 (2001). [CrossRef]
  24. J. D. Bagley, “The behavior of adaptative system which employ genetic and correlation algorithms,” Diss. Abstr. Int. B28, 5106 B, University of Michigan microfilms 068-7556 (1967).
  25. J. H. Holland, Adaptation in Natural and Artificial System (U. Michigan Press, Ann Arbor, Mich., 1975).
  26. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, Mass., 1989).
  27. R. V. Davalos, B. Rubinsky, “An evolutionary-genetic approach to heat transfer analysis,” J. Heat Transfer 118, 528–531 (1997). [CrossRef]
  28. M. R. Jones, M. Q. Brewster, Y. Yamada, “Application of a genetic algorithm to the optical characterization of propellant smoke,” J. Thermophys. Heat Transfer 10, 372–377 (1996). [CrossRef]
  29. M. Ye, S. Wang, Y. Lu, T. Hu, Z. Zhu, Y. Xu, “Inversion of particle size distribution from angular light scattering data with genetic algorithms,” Appl. Opt. 38, 2677–2685 (1999). [CrossRef]
  30. J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, Mass., 1992).
  31. W. Banzhaf, P. Nordin, R. Keller, F. Francone, Genetic Programming, An Introduction (Morgan Kaufmann, Los Altos, Calif., 1999).
  32. J. Daida, T. F. Bersano-Begey, S. J. Ross, J. F. Vesecky, “Computer-assisted design classification algorithms: dynamic and static fitness evaluations in a scaffolded genetic programming environment,” in Proceedings of the First Annual Conference on Genetic Programming, J. R. Koza, D. E. Goldberg, D. B. Fogel, R. L. Riolo, eds. (MIT Press, Cambridge, Mass., 1996), pp. 279–284.
  33. J. Daida, J. D. Hommes, T. F. Bersano-Begey, S. J. Ross, J. F. Vesecky, “Algorithm discovery using GP paradigm,” in Advances in Genetic Programming 2, P. Angeline, K. Kinnear, eds. (MIT Press, Cambridge, Mass., 1996), Chap. 2, Part IV, pp. 417–442.
  34. W. A. Tackett, “Genetic programming for feature discovery and image discrimination,” in Proceedings of the 5th International Conference on Genetic Algorithms, S. Forrest, ed. (Morgan Kaufmann, San Mateo, Calif., 1993), pp. 303–309.
  35. D. Robilliard, M. Chami, C. Fonlupt, R. Santer, “Using genetic programming to tackle the ocean color problem,” presented at the Ocean Optics XV meeting, Monaco, 16–20 October 2000.
  36. D. Zhongker, B. Punch, B. Rand, “Lilgp 1.01 user’s manual,” (Michigan State U. East Lansing, Mich.1996).
  37. C. Fonlupt, D. Robilliard, “Genetic programming with dynamics fitness for a remote sensing application,” in Proceedings of Parallel Problem Solving from Nature (PPSN), M. Schoenauer, ed., Vol. 1917 of Lectures Notes in Computer Science (Springer-Verlag, Berlin, 2001), pp. 191–200.
  38. L. Gross, S. Thiria, R. Frouin, “Applying artificial neural network methodology to ocean color remote sensing,” Ecol. Modelling 120, 237–246 (1999). [CrossRef]
  39. L. Gross, S. Thiria, R. Frouin, B. G. Mitchell, “Artificial neural networks for modeling the transfer function between marine reflectances and phytoplankton pigment concentration,” J. Geophys. Res. 105, 3483–3495 (2000). [CrossRef]
  40. D. Antoine, A. Morel, “A multiple scattering algorithm for atmospheric correction of remotely sensed ocean color (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones,” Int. J. Remote Sens. 20, 1875–1916 (1999). [CrossRef]
  41. H. R. Gordon, M. Wang, “Retrieval of water leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm,” Appl. Opt. 33, 443–452 (1994). [CrossRef] [PubMed]
  42. H. R. Gordon, “Atmospheric correction of ocean color imagery in the Earth Observing System era,” J. Geophys. Res. 102, 17,081–17,106 (1997). [CrossRef]
  43. A. Bricaud, A. Morel, L. Prieur, “Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains,” Limnol. Oceanogr. 26, 43–53 (1981). [CrossRef]
  44. A. Bricaud, A. Morel, M. Babin, K. Allali, H. Claustre, “Variations of light absorption by suspended particles with chlorophyll ‘a’ concentration in oceanic (case 1) waters: analysis and implications for bio-optical models,” J. Geophys. Res. 103, 31,033–31,044 (1998). [CrossRef]
  45. H. R. Gordon, A. Morel, Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: a Review, Vol. 4 of Lecture Notes on Coastal on Estuarine Studies, R. T. Barbers, N. K. Mooers, M. J. Bowman, B. Zeitzschel, eds. (Springer-Verlag, New York, 1983).
  46. H. C. van de Hulst, Light Scattering by Small Particles (Dover, New York, 1957).
  47. D. Buckton, E. O’Mongain, S. Danaher, “The use of neural networks for the estimation of oceanic constituents based on the MERIS instrument,” Int. J. Remote Sens. 20, 1841–1851 (1999). [CrossRef]
  48. S. B. Hooker, W. E. Esaias, G. C. Feldman, W. W. Gregg, C. R. McClain, in An Overview of SeaWiFS and Ocean Color, S. B. Hooker, E. R. Firestone, eds., NASA Tech. Memo. 104566 (NASA Goddard Space Flight Center, Greenbelt, Md., 1992), Vol. 1.
  49. A. Morel, B. Gentili, “Diffuse reflectance of oceanic waters. II. Bidirectional aspects,” Appl. Opt. 32, 6864–6979 (1993). [CrossRef] [PubMed]
  50. G. Paris, D. Robilliard, C. Fonlupt, “Applying boosting techniques to genetic programming,” in Proceedings of Artificial Evolution 2001, P. Collet, C. Fonlupt, J. K. Hao, E. Lutton, M. Schoenauer, eds., Vol. 2310 of Lecture Notes in Computer Science (Springer-Verlag, Berlin, 2002), pp. 267–280.
  51. M. Viollier, D. Tanré, P. Y. Deschamps, “An algorithm for remote sensing of water color from space,” Bound. Layer Meteorol. 18, 247–267 (1980). [CrossRef]
  52. World Meteorological Organization, “A preliminary cloudless standard atmosphere for radiation computation,” Rep. WCP-112 (World Meteorological Organisation, Geneva, 1986).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited