OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 23 — Nov. 18, 2013
  • pp: 27707–27733

Regression of in-water radiometric profile data

Davide D’Alimonte, Eugeny B. Shybanov, Giuseppe Zibordi, and Tamito Kajiyama  »View Author Affiliations

Optics Express, Vol. 21, Issue 23, pp. 27707-27733 (2013)

View Full Text Article

Enhanced HTML    Acrobat PDF (30200 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



This study addresses the regression of in-water radiometric profile data with the objective of investigating solutions to minimize uncertainties of derived products like subsurface radiance and irradiance (Lu0 and Ed0) and diffuse attenuation coefficients. Analyses are conducted using radiometric profiles generated through Monte Carlo simulations and field measurements. A nonlinear NL approach is presented as an alternative to the standard linear method LN. Results indicate that the LN method, relying on log-transformed data, tends to underestimate regression results with respect to NL operating on non-transformed data. The log-transformation is thus identified as the source of biases in data products. Observed differences between LN and NL regression results for Lu0 are of the order of 1–2%, that is well below the target uncertainty for data products from in situ measurements (i.e., 5%). For Ed0, instead, differences can easily exceed 5% as a result of more pronounced light focusing and defocusing effects due to wave perturbations. This work also remarks the importance of applying the multi-cast measurement scheme as a mean to increase the precision of data products.

© 2013 OSA

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(010.5620) Atmospheric and oceanic optics : Radiative transfer
(010.5630) Atmospheric and oceanic optics : Radiometry

ToC Category:
Atmospheric and Oceanic Optics

Original Manuscript: April 23, 2013
Revised Manuscript: June 13, 2013
Manuscript Accepted: June 13, 2013
Published: November 5, 2013

Virtual Issues
Vol. 9, Iss. 1 Virtual Journal for Biomedical Optics

Davide D’Alimonte, Eugeny B. Shybanov, Giuseppe Zibordi, and Tamito Kajiyama, "Regression of in-water radiometric profile data," Opt. Express 21, 27707-27733 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. W. B. Philip and C. D. Scott, “Modelling regional responses by marine pelagic ecosystems to global climate change,” Geophys. Res. Lett.29, 1806 (2002).
  2. J. L. Sarmiento, R. Slater, R. Barber, L. Bopp, S. C. Doney, A. C. Hirst, J. Kleypas, R. Matear, U. Mikolajewicz, P. Monfray, V. Soldatov, S. A. Spall, and R. Stouffer, “Response of ocean ecosystems to climate warming,” Global Biogeochem. Cycles18, 23 (2004). [CrossRef]
  3. M. J. Behrenfeld, R. T. ÓMalley, D. A. Siegel, C. R. McClain, J. L. Sarmiento, G. C. Feldman, A. J. Milligan, P. G. Falkowski, R. M. Letelier, and E. S. Boss, “Climate-driven trends in contemporary ocean productivity,” Nature444, 752–755 (2006). [CrossRef] [PubMed]
  4. B. A. Franz, S. W. Bailey, P. J. Werdell, and C. R. McClain, “Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry,” Appl. Optics46, 5068–5082 (2007). [CrossRef]
  5. T. Kajiyama, D. D’Alimonte, and G. Zibordi, “Match-up analysis of MERIS radiometric data in the Northern Adriatic Sea,” IEEE Geosci. Remote Sens. Lett. (2013). Accepted for publication. [CrossRef]
  6. G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti, “Assessment of MERIS ocean color data products for European seas,” Ocean Sci. Discuss.10, 219–259 (2013). [CrossRef]
  7. T. Kajiyama, D. D’Alimonte, and G. Zibordi, “Regional algorithms for European seas: a case study based on MERIS data,” IEEE Geosci. Remote Sens. Lett.10, 283–287 (2013). [CrossRef]
  8. D. D’Alimonte, G. Zibordi, J.-F. Berthon, E. Canuti, and T. Kajiyama, “Performance and applicability of bio-optical algorithms in different European seas,” Remote Sens. Environ.124, 402–412 (2012). [CrossRef]
  9. S. Sathyendranath, “Remote sensing of ocean colour in coastal, and other optically-complex waters,” International Ocean-Colour Coordinating Group, IOCCG Report NUMBER 3 (2000).
  10. S. Hooker, G. Zibordi, J.-F. Berthon, D. D’Alimonte, S. Maritorena, S. Mclean, and J. Sildam, Results of the Second SeaWiFS Data AnalysisRound Robin, (DARR-00)(NASAGSFC, Greenbelt, MD, USA, 2001), vol. 15 of SeaWiFS Technical Report SERIES, chap. 1, pp. 4–45.
  11. G. Zibordi and K. Voss, Field Radiometry and Ocean Colour Remote Sensing(Springer, 2010), chap. 18, pp. 307–334.
  12. P. J. Werdell and S. W. Bailey, “An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation,” Remote Sens. Environ.98, 122–140 (2005). [CrossRef]
  13. G. Zibordi, J.-F. Berthon, F. Mélin, and D. D’Alimonte, “Cross-site consistent in situ measurements for satellite ocean color applications: the BiOMaP radiometric dataset,” Remote Sens. Environ.115, 2104–2115 (2011). [CrossRef]
  14. J. R. Zaneveld, E. Boss, and P. Hwang, “The influence of coherent waves on the remotely sensed reflectance,” Opt. Express9, 260–266 (2001). [CrossRef] [PubMed]
  15. J. R. V. Zaneveld, E. Boss, and A. Barnard, “Influence of surface waves on measured and modeled irradiance profiles,” Appl. Optics40, 1442–1449 (2001). [CrossRef]
  16. G. Zibordi, D. D’Alimonte, and J.-F. Berthon, “An evaluation of depth resolution requirements for optical profiling in coastal waters,” J. of Atm. and Ocean. Tech.21, 1059–1073 (2004). [CrossRef]
  17. Y. You, D. Stramski, M. Darecki, and G. W. Kattawar, “Modeling of wave-induced irradiance fluctuations at near-surface depths in the ocean: a comparison with measurements,” Appl. Optics49, 1041–1053 (2010). [CrossRef]
  18. M. Hieronymi and A. Macke, “On the influence of wind and waves on underwater irradiance fluctuations,” Ocean Sci.8, 455–471 (2012). [CrossRef]
  19. M. Hieronymi, A. Macke, and O. Zielinski, “Modeling of wave-induced irradiance variability in the upper ocean mixed layer,” Ocean Sci.8, 103–120 (2012). [CrossRef]
  20. J. L. Muller and R. W. Austin, Ocean Optics Protocols SeaWiFS for Validation, Revision 1(NASA GSFC, Greenbelt, MD, USA, 1995), vol. 25 of SeaWiFS Technical Report SERIES, chap. 6, pp. 48–59.
  21. D. A. Siegel, Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)(NASA GSFC, Greenbelt, MD, USA, 1995), vol. 26 of SeaWiFS Technical Report SERIES, chap. 3, pp. 44–48.
  22. J. J. Beauchamp and J. S. Olson, “Corrections for bias in regression estimates after logarithmic transformation,” Ecology54, 1403–1407 (1973). [CrossRef]
  23. D. D’Alimonte, G. Zibordi, T. Kajiyama, and J. C. Cunha, “Monte Carlo code for high spatial resolution ocean color simulations,” Appl. Optics49, 4936–4950 (2010). [CrossRef]
  24. T. Kajiyama, D. D’Alimonte, J. Cunha, and G. Zibordi, “High-performance ocean color Monte Carlo simulation in the Geo-info project,” in “Parallel Processing and Applied Mathematics,”, vol. 6068 of Lecture Notes in Computer Science,R. Wyrzykowski, J. Dongarra, K. Karczewski, and J. Wasniewski, eds. (Springer, 2010), vol. 6068 of Lecture Notes in Computer Science, pp. 370–379.
  25. D. Schattschneider, “Proof without words: The arithmetic mean-geometric mean inequality,” Math. Mag.59, 11 (1986). [CrossRef]
  26. G. A. F. Seber and C. J. Wild, Nonlinear regression, Wiley series in probability and statistics (J. Wiley & Sons, 2003).
  27. P. E. Gill, W. Murray, and M. H. Wright, The Levenberg-Marquardt Method(Academic Press, 1981), chap. 4.7.3, pp. 136–137.
  28. Y. Yuan, “A Review of Trust Region Algorithms for Optimization,” in “ICIAM 99,” (Oxford University, 2000), pp. 271–282.
  29. G. R. Fournier and J. L. Forand, “Analytic phase function for ocean water,” in “Ocean Optics XII,” (1994), no. 2558 in SPIE, pp. 194–201.
  30. G. R. Fournier and M. Jonasz, “Computer-based underwater imaging analysis,” P. Soc. Photo-opt. Ins.3761, 62–70 (1999).
  31. T. Kajiyama, D. D’Alimonte, and J. C. Cunha, “Performance prediction of ocean color Monte Carlo simulations using multi-layer perceptron neural networks,” (2011), vol. 4, pp. 2186–2195. Proceedings of the International Conference on Computational Science, ICCS2011.
  32. T. Kajiyama, D. DAlimonte, and J. C. Cunha, “A high-performance computing framework for large-scale ocean color Monte Carlo simulations,” Concurrency Computat.: Pract. Exper pp. 1–22 (2011). Submitted for publications.
  33. T. Kajiyama, D. D’Alimonte, and J. C. Cunha, “A statistical approach to performance tuning of Monte Carlo ocean color simulations,” in “Parallel and Distributed Computing, Applications and Technologies 2012,” (Beijing, China, 2012).
  34. J. Escher and T. Schlurmann, “On the recovery of the free surface from the pressure within periodic traveling water waves,” J. Nonlinear Math. Phys.15, 50–57 (2008). [CrossRef]
  35. N. L. Jones and S. G. Monismith, “Measuring short-period wind waves in a tidally forced environment with a subsurface pressure gauge,” Limnol. Oceanogr.: Methods5, 317–327 (2007). [CrossRef]
  36. C. Tsai, M. Huang, F. Young, Y. Lin, and H. Li, “On the recovery of surface wave by pressure transfer function,” Ocean Eng.32, 1247–1259 (2005). [CrossRef]

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