OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 16 — Aug. 12, 2013
  • pp: 18849–18871

A hybrid approach to estimate chromophoric dissolved organic matter in turbid estuaries from satellite measurements: A case study for Tampa Bay

Chengfeng Le and Chuanmin Hu  »View Author Affiliations

Optics Express, Vol. 21, Issue 16, pp. 18849-18871 (2013)

View Full Text Article

Enhanced HTML    Acrobat PDF (6480 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Remote sensing of chromophoric dissolved organic matter (CDOM) from satellite measurements for estuaries has been problematic due to optical complexity of estuarine waters and uncertainties in satellite-derived remote sensing reflectance (Rrs, sr−1). Here we demonstrate a hybrid approach to combine empirical and semi-analytical algorithms to derive CDOM absorption coefficient at 443 nm (ag(443), m−1) in a turbid estuary (Tampa Bay) from MODIS Aqua (MODISA) and SeaWiFS measurements. The approach first used a validated empirical algorithm and a modified quasi-analytical algorithm (QAA) to derive chlorophyll-a concentration (Chla, mg m−3) and particulate backscattering coefficient at 443 nm (bbp(443), m−1), respectively, from which phytoplankton pigment and non-algal particulate absorption coefficient at 443 nm (aph(443) and ad(443), m−1) were derived with pre-determined bio-optical relationships. Then, the modified QAA was used to estimate the total absorption coefficient at 443 nm (at(443), m−1). Finally, ag(443) was estimated as (at(443) - aph(443) - ad(443) – aw(443)) where aw(443) is the absorption coefficient of pure water (a constant). Using data collected from 71 field stations and 33 near-concurrent satellite-field matchup data pairs covering a large dynamic range (0.3 – 8 m−1), the approach showed ~23% RMS uncertainties in retrieving ag(443) when in situ Rrs data (N = 71) were used. The same approach applied to satellite Rrs yielded much higher uncertainties of ag(443) (~85%) due to large errors in the satellite-retrieved Rrs(443). When the Rrs(443) was derived from the satellite-retrieved Rrs(550) and then used in the hybrid approach, uncertainties in the retrieved ag(443) reduced to ~30% (N = 33). Application of the approach to MODISA and SeaWiFS data led to a 15-year time series of monthly mean ag(443) distributions in Tampa Bay between 1998 and 2012. This time series showed significant seasonal and annual variations regulated mainly by river discharge. Testing of the approach over another turbid estuary (Chesapeake Bay, the largest estuary in the U.S.) demonstrated the potential (~25% uncertainties for a limited ag(443) range) of using this approach to establish long-term environmental data records (EDRs) of CDOM distributions in other estuaries with similar optical complexity.

© 2013 OSA

OCIS Codes
(160.4760) Materials : Optical properties
(200.4560) Optics in computing : Optical data processing
(010.0280) Atmospheric and oceanic optics : Remote sensing and sensors

ToC Category:
Atmospheric and Oceanic Optics

Original Manuscript: April 22, 2013
Revised Manuscript: June 20, 2013
Manuscript Accepted: July 8, 2013
Published: August 1, 2013

Virtual Issues
Vol. 8, Iss. 9 Virtual Journal for Biomedical Optics

Chengfeng Le and Chuanmin Hu, "A hybrid approach to estimate chromophoric dissolved organic matter in turbid estuaries from satellite measurements: A case study for Tampa Bay," Opt. Express 21, 18849-18871 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. J. T. O. Kirk, Light and Photosynthesis in Aquatic Ecosystems, 2nd ed., (Cambridge Univ. Press, Cambridge, U. K., 1994) p. 509.
  2. H. Gao and R. G. Zepp, “Factors influencing photoreactions of dissolved organic matter in a coastal river of the southeastern United States,” Environ. Sci. Technol.32(19), 2940–2946 (1998). [CrossRef]
  3. N. B. Nelson, D. A. Siegel, and A. F. Michaels, “Seasonal dynamics of colored dissolved material in the Sargasso Sea,” Deep Sea Res. Part I Oceanogr. Res. Pap.45(6), 931–957 (1998). [CrossRef]
  4. C. A. Stedmon, S. Markager, M. Søndergaard, T. Vang, A. Laubel, N. H. Borch, and A. Windelin, “Dissolved organic matter (DOM) export to a temperate estuary: seasonal variations and implications of land use,” Estuaries Coasts29, 388–400 (2006).
  5. P. G. Coble, “Marine optical biogeochemistry: The chemistry of ocean color,” Chem. Rev.107(2), 402–418 (2007). [CrossRef] [PubMed]
  6. G. M. Ferrari and M. D. Dowell, “CDOM absorption characteristics with relation to fluorescence and salinity in coastal areas of the Southern Baltic Sea,” Estuar. Coast. Shelf Sci.47(1), 91–105 (1998). [CrossRef]
  7. D. G. Bowers and H. L. Brett, “The relationship between CDOM and salinity in estuaries: an analytical and graphical solution,” J. Mar. Syst.73(1-2), 1–7 (2008). [CrossRef]
  8. Z. Chen, C. Hu, R. N. Conmy, F. E. Muller-Karger, and P. Swarzenski, “Colored dissolved organic matter in Tampa Bay, Florida,” Mar. Chem.104(1-2), 98–109 (2007a). [CrossRef]
  9. C. Le, C. Hu, D. English, J. Cannizzaro, Z. Chen, C. Kovach, C. J. Anastasiou, J. Zhao, and K. L. Carder, “Inherent and apparent optical properties of the complex estuarine waters of Tampa Bay: what controls light?” Estuar. Coast. Shelf Sci.117, 54–69 (2013a). [CrossRef]
  10. K. Oubelkheir, L. A. Clementson, I. T. Webster, P. W. Ford, A. G. Dekker, L. C. Radke, and P. Daniel, “Using inherent optical properties to investigate biogeochemical dynamic in a tropical macrotidal coastal system,” J. Geophys. Res.111(C7), C07021 (2006), doi:. [CrossRef]
  11. Z. Chen, C. Hu, F. E. Muller-Karger, and M. E. Luther, “Short-term variability of suspended sediment and phytoplankton in Tampa Bay, Florida: observations from a coastal oceanographic tower and ocean color satellites,” Estuar. Coast. Shelf Sci.89(1), 62–72 (2010). [CrossRef]
  12. J. E. Cloern, “Our evolving conceptual model of the coastal eutrophication problem,” Mar. Ecol. Prog. Ser.210, 223–253 (2001). [CrossRef]
  13. L. W. Harding, A. Magnuson, and M. E. Mallonee, “Bio-optical and remote sensing observations in Chesapeake Bay,” Estuar. Coast. Shelf Sci.62, 75–94 (2005). [CrossRef]
  14. J. Udy, M. Gall, B. Longstaff, K. Moore, C. Roelfsema, D. R. Spooner, and S. Albert, “Water quality monitoring: a combined approach to investigate gradients of change in the Great Barrier Reef, Australia,” Mar. Pollut. Bull.51(1-4), 224–238 (2005). [CrossRef] [PubMed]
  15. Z. Chen, F. E. Muller-Karger, and C. Hu, “Remote sensing of water clarity in Tampa Bay,” Remote Sens. Environ.109(2), 249–259 (2007b). [CrossRef]
  16. M. Wang, S. Son, and L. W. Harding., “Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,” J. Geophys. Res.114(C10), C10011 (2009), doi:. [CrossRef]
  17. C. Le, C. Hu, D. English, J. Cannizzaro, Z. Chen, L. Feng, R. Boler, and C. Kovach, “Towards a long-term chlorophyll-a data record in a turbid estuary using MODIS observations,” Prog. Oceanogr.109, 90–103 (2013b). [CrossRef]
  18. Z. P. Lee, K. L. Carder, T. G. Peacock, C. O. Davis, and J. L. Mueller, “Method to derive ocean absorption coefficients from remote-sensing reflectance,” Appl. Opt.35(3), 453–462 (1996). [CrossRef] [PubMed]
  19. C. C. Liu and R. L. Miller, “Spectrum matching method for estimating the chlorophyll-a concentration, CDOM ratio, and backscatter fraction from remote sensing of ocean color,” Can. J. Rem. Sens.34(4), 343–355 (2008). [CrossRef]
  20. J. Fischer, “On the information content of multispectral radiance measurements over an ocean,” Int. J. Remote Sens.6(5), 773–786 (1985). [CrossRef]
  21. R. Doerffer and H. Schiller, “Determination of case 2 water constituents using radiative transfer simulation and its inversion by neural networks”, in Proceedings of Ocean Optics XIV [CD-ROM], S. G. Ackleson and J. Campbell, Kailua-kona, ed. (academic 1998), 1–13.
  22. E. J. D’Sa and R. L. Miller, “Bio-optical properties in waters influenced by the Mississippi River during low flow conditions,” Remote Sens. Environ.84(4), 538–549 (2003). [CrossRef]
  23. D. Doxaran, R. C. N. Cherukuru, and S. J. Lavender, “Use of reflectance band ratios to estimate suspended and dissolved matter concentrations in estuarine waters,” Int. J. Remote Sens.26(8), 1763–1769 (2005). [CrossRef]
  24. P. Kowalczuk, L. Olszewski, M. Darecki, and S. Kaczmarek, “Empirical relationships between coloured dissolved organic matter (CDOM) absorption and apparent optical properties in Baltic Sea waters,” Int. J. Remote Sens.26(2), 345–370 (2005). [CrossRef]
  25. A. Mannino, M. E. Russ, and S. B. Hooker, “Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the US Middle Atlantic Bight,” J. Geophys. Res.113(C7), C07051 (2008), doi:. [CrossRef]
  26. S. P. Tiwari and P. Shanmugam, “An optical model for the remote sensing of coloured dissolved organic matter in coastal/ocean waters,” Estuar. Coast. Shelf Sci.93(4), 396–402 (2011). [CrossRef]
  27. N. C. Tehrani, E. J. D’Sa, C. L. Osburn, T. S. Bianchi, and B. A. Schaeffer, “Chromophoric dissolved organic matter and dissolved organic carbon from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and MERIS Sensors: case Study for the Northern Gulf of Mexico,” Remote Sens.5(3), 1439–1464 (2013). [CrossRef]
  28. International Ocean-Colour Coordinating Group (IOCCG), “Remote sensing of inherent optical properties: Fundamentals, tests of algorithms, and applications,” Z. P. Lee (Ed.), Reports of the International Ocean-Colour Coordinating Group, No. 5. Dartmouth, Canada: IOCCG (2006).
  29. K. L. Carder, F. R. Chen, Z. P. Lee, S. K. Hawes, and D. Kamykowski, “Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures,” J. Geophys. Res.104(C3), 5403–5421 (1999). [CrossRef]
  30. S. Maritorena and D. A. Siegel, “Consistent merging of satellite ocean color data sets using a bio-optical model,” Remote Sens. Environ.94, 429–440 (2005). [CrossRef]
  31. D. A. Siegel, S. Maritorena, N. B. Nelson, D. A. Hansell, and M. Lorenzi-Kayser, “Global distribution and dynamics of colored dissolved and detrital organic materials,” J. Geophys. Res. 107, 3228, DOI:. (2002). [CrossRef]
  32. Z. P. Lee, K. L. Carder, and R. A. Arnone, “Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters,” Appl. Opt.41(27), 5755–5772 (2002). [CrossRef] [PubMed]
  33. Q. Dong, S. Shang, and Z. Lee, “An algorithm to retrieve absorption coefficient of chromophoric dissolved organic matter from ocean color,” Remote Sens. Environ.128, 259–267 (2013). [CrossRef]
  34. W. Zhu, Q. Yu, Y. Tian, R. Chen, and G. B. Gardner, “Estimation of chromophoric dissolved organic matter in the Mississippi and Atchafalaya river plume regions using above-surface hyperspectral remote sensing,” J. Geophys. Res.116(C2), C02011 (2011), doi:. [CrossRef]
  35. Y. Qin, V. E. Brando, A. G. Dekker, and D. Blondeau-Patissier, “Validity of SeaDAS water constituents retrieval algorithms in Australian tropical coastal waters,” J. Geophys. Res. Lett.34(21), L21603 (2007), doi:. [CrossRef]
  36. C. Le, Y. Li, Y. Zha, D. Sun, and B. Yin, “Validation of a quasi-analytical algorithm for highly turbid eutrophic water of Meiliang Bay in Taihu Lake, China,” IEEE Trans. Geosci. Rem. Sens.8, 2490–2500 (2009).
  37. M. A. Harwell, J. S. Ault, and J. H. Gentile, Comparison of the ecological risks to the Tampa Bay ecosystem from spills of fuel #6 and Orimulsion. Comparative Ecological Risk Assessment, Volume 1, Center for Marine and Environmental Analyses. (University of Miami, Miami, Florida, 1995.)
  38. Z. Chen, C. Hu, and F. E. Muller-Karger, “Monitoring turbidity in Tampa Bay using MODIS/Aqua 250-m imagery,” Remote Sens. Environ.109(2), 207–220 (2007c). [CrossRef]
  39. C. Le, C. Hu, D. English, J. Cannizzaro, and C. Kovach, “Climate-driven chlorophyll-a changes in a turbid estuary: observations from satellites and implications for management’,” Remote Sens. Environ.130, 11–24 (2013c). [CrossRef]
  40. R. H. Weisberg and L. Zheng, “Circulation of Tampa Bay driven by buoyancy, tides, and winds, as simulated using a finite volume coastal ocean model,” J. Geophys. Res.111(C1), C01005 (2006), doi:. [CrossRef]
  41. C. Hu, Z. Chen, T. D. Clayton, P. Swarzenski, J. C. Brock, and F. E. Muller-Karger, “Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: initial results from Tampa Bay, FL,” Remote Sens. Environ.93(3), 423–441 (2004). [CrossRef] [PubMed]
  42. S. W. Bailey and P. J. Werdell, “A multi-sensor approach for the on-orbit validation of ocean color satellite data products,” Remote Sens. Environ.102(1-2), 12–23 (2006). [CrossRef]
  43. C. Hu, K. L. Carder, and F. E. Muller-Karger, “How precise are SeaWiFS ocean color estimates? Implications of digitization-noise errors,” Remote Sens. Environ.76(2), 239–249 (2001). [CrossRef]
  44. L. W. Harding, A. Magnuson, and M. E. Mallonee, “SeaWiFS retrievals of chlorophyll in Chesapeake Bay and the mid-Atlantic bight,” Estuar. Coast. Shelf Sci.62(1-2), 75–94 (2005b). [CrossRef]
  45. R. M. Pope and E. S. Fry, “Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements,” Appl. Opt.36(33), 8710–8723 (1997). [CrossRef] [PubMed]
  46. A. Morel, Optical properties of pure water and pure seawater. E. Steeman Nielsen ed. (Academic, 1974, pp 1–24).
  47. Z. P. Lee, B. Lubac, J. Werdell, and R. Arnone, “An update of the Quasi-Analytical Algorithm (QAA v5),” http://www. ioccg.org/groups/Software OCA/QAA v5.pdf (2009).
  48. S. B. Hooker, W. E. Esaias, G. C. Feldman, W. W. Gregg, and C. R. McClain, An overview of SeaWiFS and ocean color. NASA Tech. Memo., vol. 104566. (National Aeronautics and Space Administration, Goddard Space Flight CenterGreenbelt, MD, 1992).
  49. W. W. Gregg and N. W. Casey, “Global and regional evaluation of the SeaWiFS chlorophyll dataset,” Remote Sens. Environ.93(4), 463–479 (2004). [CrossRef]
  50. P. J. Werdell, S. W. Bailey, B. A. Franz, L. W. Harding, G. C. Feldman, and C. R. McClain, “Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua,” Remote Sens. Environ.113(6), 1319–1330 (2009). [CrossRef]
  51. S. Son and M. Wang, “Water properties in Chesapeake Bay from MODIS-Aqua measurements,” Remote Sens. Environ.123, 163–174 (2012). [CrossRef]
  52. N. Schmidt, E. K. Lipp, J. B. Rose, and M. E. Luther, “ENSO influences on Seasonal Rainfall and River Discharger in Florida,” J. Clim.14(4), 615–628 (2001). [CrossRef]
  53. K. Wolter and M. S. Timlin, “El Niño/Southern Oscillation behavior since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext),” Int. J. Climatol.31(7), 1074–1087 (2011). [CrossRef]
  54. M. Tzortziou, A. Subramanian, J. R. Herman, C. L. Gallegos, P. J. Neale, and L. W. Harding, “Remote sensing reflectance and inherent optical properties in the mid Chesapeake Bay,” Estuar. Coast. Shelf Sci.72(1-2), 16–32 (2007). [CrossRef]
  55. D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and L. Wang, “Parameterization of water component absorption in an inland eutrophic lake and its seasonal variability: a case study in Lake Taihu,” Int. J. Remote Sens.30(13), 3549–3571 (2009). [CrossRef]
  56. F. Shen, Y. X. Zhou, D. J. Li, W. J. Zhu, and M. S. Salama, “Medium resolution imaging spectrometer (MERIS) estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary,” Int. J. Remote Sens.31(17-18), 4635–4650 (2010). [CrossRef]
  57. V. R. Louis, E. Russek-Cohen, N. Choopun, I. N. G. Rivera, B. Gangle, S. C. Jiang, A. Rubin, J. A. Patz, A. Huq, and R. R. Colwell, “Predictability of Vibrio cholerae in Chesapeake Bay,” Appl. Environ. Microbiol.69(5), 2773–2785 (2003). [CrossRef] [PubMed]
  58. G. C. Magny, W. Long, C. W. Brown, R. R. Hood, A. Huq, R. Murtugudde, and R. R. Colwell, “Predicting the distribution of Vibrio spp. in the Chesapeake Bay: a vibrio cholera case study,” EcoHealth (2010), doi:. [CrossRef]
  59. J. M. Jacobs, M. Rhodes, C. W. Brown, R. R. Hood, A. Leigh, and W. L. R. Wood, “Predicting the distribution of Vibrio vulnificus in Chesapeake Bay,” NOAA Technical Memorandum NOS NCCOS 112. 1–12 (2010).
  60. E. A. Urquhat, B. F. Zaitchik, M. J. Hoffman, S. D. Guikema, and E. F. Geiger, “Remote sensed estimates of surface salinity in the Chesapeake Bay: a statistical approach,” Remote Sens. Environ.123, 522–531 (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