OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 22, Iss. 10 — May. 19, 2014
  • pp: 11536–11551

Sensitivity in reflectance attributed to phytoplankton cell size: forward and inverse modelling approaches

Hayley Evers-King, Stewart Bernard, Lisl Robertson Lain, and Trevor A. Probyn  »View Author Affiliations


Optics Express, Vol. 22, Issue 10, pp. 11536-11551 (2014)
http://dx.doi.org/10.1364/OE.22.011536


View Full Text Article

Enhanced HTML    Acrobat PDF (2749 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Synoptic scale knowledge of the size structure of phytoplankton communities can offer insight in to primary ecosystem diversity and biogeochemical variability from operational to the decadal scales. Accordingly, obtaining estimates of size and other phytoplankton functional type descriptors within known confidence limits from remotely sensed data has become a major objective to extend the use of ocean colour data beyond chlorophyll a retrievals. Here, a new forward and inverse modelling structure is proposed to determine information about the cell size of phytoplankton communities using Standard size distributions of two layered spheres to derive a full suite of algal inherent optical properties for a coupled radiative transfer model. This new capability allows explicit quantification of the remote sensing reflectance signal attributable to changes in phytoplankton cell size. Inversion of this model reveals regions within the parameter space where ambiguity may limit potential of inversion algorithms. Validation of the algorithm within the Benguela upwelling system using independent data shows promise for ecosystem applications and further investigation of the interaction between phytoplankton functional types and optical signals. The results here suggest that the utility of assemblage related signals in spectral reflectance is highly sensitive to algal biomass, the presence of other absorbing and scattering constituents and the resultant constituent-specific inherent optical property budget. As such, optimal methods for determining phytoplankton size from (in situ or satellite) ocean colour data will likely rely on appropriately spectrally dense and optimised sensors, well characterised measurement errors including those from atmospheric correction, and an ability to appropriately limit ambiguity within the context of regional inherent optical properties.

© 2014 Optical Society of America

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(010.1690) Atmospheric and oceanic optics : Color
(010.5620) Atmospheric and oceanic optics : Radiative transfer

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: December 17, 2013
Revised Manuscript: February 6, 2014
Manuscript Accepted: February 6, 2014
Published: May 6, 2014

Virtual Issues
Vol. 9, Iss. 7 Virtual Journal for Biomedical Optics

Citation
Hayley Evers-King, Stewart Bernard, Lisl Robertson Lain, and Trevor A. Probyn, "Sensitivity in reflectance attributed to phytoplankton cell size: forward and inverse modelling approaches," Opt. Express 22, 11536-11551 (2014)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-22-10-11536


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. S. Bernard, R. Kudela, P. J. S. Franks, W. Fennel, A. Kemp, A. Fawcett, G. C. Pitcher, “The requirements for Forecasting Harmful Algal Blooms in the Benguela,” Large Mar. Ecosyst. 14, 281–302 (2006).
  2. E. Marañón, “Inter-specific scaling of phytoplankton production and cell size in the field,” J. Plankton Res. 30(2), 157–163 (2008). [CrossRef]
  3. J. Uitz, H. Claustre, A. Morel, S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” J. Geophys. Res. 111, C08005 (2006).
  4. S. Alvain, C. Moulin, Y. Dandonneau, “Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery,” Deep Sea Res. I 52, 1989–2004 (2005). [CrossRef]
  5. A. Ciotti, M. R. Lewis, “Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient,” Limnol. Oceanogr. 47(2), 404–417 (2002). [CrossRef]
  6. T. Konstadinov, D. A. Siegel, S. Maritorena, “Global variability of phytoplankton functional types from space: assessment via the particle size distribution,” Biogeosciences 7, 3239–3257 (2010). [CrossRef]
  7. R. J. W. Brewin, N. J. Hardman-Mountford, S. J. Lavender, D. Raitsos, T. Hirata, J. Uitz, E. Devred, A. Bricaud, B. Gentili, “An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing,” Remote Sens. Environ. 115(2), 325–339 (2011). [CrossRef]
  8. J. R. V. Zaneveld, “A theoretical derivation of the dependence of the remotely-sensed reflectance of the ocean on the inherent optical properties,” J. Geophys. Res. 100(C7), 13135–13142 (1995). [CrossRef]
  9. A. Morel, D. Antoine, B. Gentili, “Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function,” Appl. Opt. 41(30), 6289–6306 (2002). [CrossRef] [PubMed]
  10. A. Bricaud, M. Babin, A. Morel, H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995). [CrossRef]
  11. A. Bricaud, A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25(4), 571–580 (1986). [CrossRef] [PubMed]
  12. A. Quirantes, S. Bernard, “Light scattering by marine algae: two-layer spherical and nonspherical models,” J. Quant. Spectrosc. Radiat. Transfer, 89(1–4), 311–321 (2004). [CrossRef]
  13. Y.-H. Ahn, A. Bricaud, A. Morel, “Light backscattering efficiency and related properties of some phyto-plankters,” Deep Sea Res. 39(11–12), 1835–1855 (1992). [CrossRef]
  14. A. L. Whitmire, W. S. Pegau, L. Karp-Boss, E. Boss, T. J. Cowles, “Spectral backscattering properties of marine phytoplankton cultures,” Opt. Express 18(14), 15073–15093 (2010). [CrossRef] [PubMed]
  15. W. Zhou, G. Wang, Z. Sun, W. Cao, Z. Xu, S. Hu, “Variations in the optical scattering properties of phytoplankton cultures,” Opt. Express 20, 11189–11206 (2012). [CrossRef] [PubMed]
  16. J. C. Kitchen, J. R. Zaneveld, “A three-layered sphere model of the optical properties of phytoplankton,” Limnol. Oceanogr. 37(8), 1680–1690 (1992). [CrossRef]
  17. S. Bernard, T. A. Probyn, A. Quirantes, “Simulating the optical properties of phytoplankton cells using a two-layered spherical geometry,” Biogeosci. Discuss. 6, 1–67 (2009). [CrossRef]
  18. S. Bernard, F. A. Shillington, T. A. Probyn, “The use of equivalent size distributions of natural phytoplankton assemblages for optical modeling,” Opt. Express 15(5), 1995–2007 (2007). [CrossRef] [PubMed]
  19. M. Matthews, S. Bernard, “Using a two-layered sphere model to investigate the impact of gas vacuoles on the inherent optical properties of M. aeruginosa,” Biogeosciences 7, 3239–3257 (2013).
  20. M. Defoin-Platel, M. Chami, “How ambiguous is the inverse problem of ocean color in coastal waters?” J. Geophys. Res. 112, C03004 (2007).
  21. A. Morel, L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977). [CrossRef]
  22. R. G. Barlow, H. Sessions, N. Siliulwane, H. Engel, S. Hooker, J. Aiken, J. Fishwick, V. Vicente, A. Morel, M. Chami, J. Ras, S. Bernard, M. Pfaff, J. W. Brown, A. Fawcett, “2003: BENCAL Cruise Report, NASA/TM 2003-206892,” Tech. rep. (2003).
  23. C. S. Yentsch, “Measurement of visible light absorption by particulate matter in the ocean,” Limnol. Oceanogr. 7, 207–217 (1962). [CrossRef]
  24. C. S. Roesler, “Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique,” Limnol. Oceanogr. 43(7), 1649–1660 (1998). [CrossRef]
  25. T. R. Parsons, Y. Maita, C. M. Lalli, A Manual of Chemical and Biological Methods for Seawater Analysis (Pergamon, 1984).
  26. J. E. Hansen, L. D. Travis, “Light scattering in planetary atmospheres,” Space Sci. Rev. 16, 527–610 (1974). [CrossRef]
  27. C. S. Roesler, M. J. Perry, “In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance,” J. Geophys. Res. 100(C7), 13279–13294 (1995). [CrossRef]
  28. S. Bernard, T. A. Probyn, F. A. Shillington, “Towards the validation of SeaWiFS in southern African waters: the effects of gelbstoff,” S. Afr. J. Mar. Sci. 19(1), 15–25 (1998). [CrossRef]
  29. C. D. Mobley, L. K. Sundman, HydroLight 5.0, Technical Documentation (Sequoia Scientific, Inc., 2008).
  30. H. Dierssen, R. Kudela, J. Ryan, “Red and black tides: Quantitative analysis of water-leaving radiance and perceived color for phytoplankton, colored dissolved organic matter, and suspended sediments,” Limnol. Oceanogr. 51(6), 2646–2659 (2006). [CrossRef]
  31. C. D. Mobley, “Fast light calculations for ocean ecosystem and inverse models,” Opt. Express 19(20), 18927–18944 (2011). [CrossRef] [PubMed]
  32. A. Morel, S. Maritorena, “Bio-optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. 106, 7163–7180 (2001). [CrossRef]
  33. A. Albert, C. D. Mobley, “An analytical model for subsurface irradiance and remote sensing reflectance in deep and shallow case-2 waters,” Opt. Express 11(22), 2873–2890 (2003). [CrossRef] [PubMed]
  34. I. Reda, A. Andreas, “Solar position algorithm for solar radiation applications,” Sol. Energy 76(5), 577–589 (2004). [CrossRef]
  35. P. Werdell, S. Bailey, “An improved bio-optical data set for ocean color algorithm development and satellite data product validation,” Remote Sens. Environ. 98, 122–140 (2005). [CrossRef]
  36. J. A. Nelder, R. Mead, “A Simplex Method for Function Minimization,” Comput. J. 7(4), 308–313 (1965). [CrossRef]
  37. J. O’Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res. 103, 24937–24953 (1998). [CrossRef]
  38. G. Zibordi, F. Mélin, S. Hooker, D. D’Alimonte, B. Holben, “An autonomous above-water system for the validation of ocean color radiance data,” IEEE Trans. Geosci. Remote Sens. 42(2), 401–415 (2004). [CrossRef]
  39. M. Matthews, S. Bernard, L. Robertson, “An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters,” Remote Sens. Environ. 124, 637–652 (2012). [CrossRef]
  40. S. Agusti, C. M. Duarte, J. Kalff, “Algal cell size and the maximum density and biomass of phytoplankton,” Limnol. Oceanogr. 32(4), 983–986 (1987). [CrossRef]
  41. M. J. Sauer, C. S. Roesler, P. J. Werdell, A. Barnard, “Under the hood of satellite empirical chlorophyll a algorithms: revealing the dependencies of maximum band ratio algorithms on inherent optical properties,” Opt. Express 20(19), 20920–20933 (2012). [CrossRef] [PubMed]
  42. E. Rehm, C. D. Mobley, “Estimation of hyperspectral inherent optical properties from in-water radiometry: error analysis and application to in situ data,” Appl. Opt. 52(4), 795–817 (2013). [CrossRef] [PubMed]
  43. L. Robertson Lain, S. Bernard, H. Evers-King, “Biophysical modelling of phytoplankton communities from first principles using two-layered spheres: Equivalent Algal Populations (EAP) model,” Opt. Express (to be published).
  44. D. A. Aurin, H. M. Dierssen, “Advantages and limitations of ocean color remote sensing in CDOM-dominated, mineral-rich coastal and estuarine waters,” Remote Sens. Environ. 125, 181–197 (2012). [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