Abstract

Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were Rrs(708)/Rrs(665) and Rrs(753)/Rrs(665). The three-band algorithm with MERIS bands was in the form R3 = [Rrs −1(665) − Rrs −1(708)] × Rrs(753). It is shown that the relationships of both Rrs(708)/Rrs(665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl] > 1 mg/m3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning.

© 2010 OSA

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. G. Moore, J. Aiken, and J. Lavender, “The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: application to MERIS,” Int. J. Remote Sens. 20(9), 1713–1733 (1999).
    [CrossRef]
  2. J. Aiken and G. Moore, “ATBD case 2 bright pixel atmospheric correction,” Center for Coastal & Marine Sciences, Plymouth Marine Laboratory, U.K., Rep, PO-TN-MEL-GS 4, 0005 (2000).
  3. M. Wang and W. Shi, “Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies,” Geophys. Res. Lett. 32(13), L13606 (2005), doi:.
    [CrossRef]
  4. M. Wang and W. Shi, “The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing,” Opt. Express 15(24), 15722–15733 (2007).
    [CrossRef] [PubMed]
  5. R. Doerffer and H. Schiller, “MERIS regional coastal and lake case 2 water project atmospheric correction ATBD,” Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany, Rep, GKSS-KOF-MERIS-ATB D01, 1 (2008).
  6. 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]
  7. A. A. Gitelson, G. Keydan, and V. Shishkin, “Inland waters quality assessment from satellite data in visible range of the spectrum,” Sov. Remote Sens. 6, 28–36 (1985).
  8. R. P. Stumpf and M. A. Tyler, “Satellite detection of bloom and pigment distributions in estuaries,” Remote Sens. Environ. 24(3), 385–404 (1988).
    [CrossRef]
  9. A. A. Gitelson, “The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration,” Int. J. Remote Sens. 13(17), 3367–3373 (1992).
    [CrossRef]
  10. A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
    [CrossRef]
  11. H. R. Gordon, “Diffuse reflectance of the ocean: the theory of its augmentation by chlorophyll a fluorescence at 685 nm,” Appl. Opt. 18(8), 1161–1166 (1979).
    [CrossRef] [PubMed]
  12. A. Vasilkov and O. Kopelevich, “Reasons for the appearance of the maximum near 700 nm in the radiance spectrum emitted by the ocean layer,” Oceanology (Mosc.) 22, 697–701 (1982).
  13. J. F. Schalles, “Optical Remote Sensing Techniques to Estimate Phytoplankton Chlorophyll a Concentrations in Coastal Waters with Varying Suspended Matter and CDOM Concentrations,” in Remote Sensing of Aquatic Coastal Ecosystem Processes: Science and Management Applications, L.L. Richardson and E.F. LeDrew, eds. (Springer, 2006), Chap. 3.
  14. G. Dall'Olmo, A. A. Gitelson, and D. C. Rundquist, “Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters,” Geophys. Res. Lett. 30(18), 1938 (2003), doi:.
    [CrossRef]
  15. L. Han and D. Rundquist, “Comparison of NIR/Red ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: a case study in a turbid reservoir,” Remote Sens. Environ. 62(3), 253–261 (1997).
    [CrossRef]
  16. C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
    [CrossRef]
  17. H. J. Gons, M. Rijkeboer, and K. G. Ruddick, “A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters,” J. Plankton Res. 24(9), 947–951 (2002).
    [CrossRef]
  18. K. G. Ruddick, H. J. Gons, M. Rijkeboer, and G. Tilstone, “Optical remote sensing of chlorophyll a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties,” Appl. Opt. 40(21), 3575–3585 (2001).
    [CrossRef]
  19. J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
    [CrossRef]
  20. G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl. Opt. 44(3), 412–422 (2005).
    [CrossRef] [PubMed]
  21. A. A. Gitelson, J. Schalles, and C. M. Hladik, “Remote chlorophyll-a retrieval in turbid productive estuarine: Chesapeake Bay case study,” Remote Sens. Environ. 109(4), 464–472 (2007).
    [CrossRef]
  22. G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of chlorophyll-a concentration in turbid productive waters: modeling results,” Appl. Opt. 45(15), 3577–3592 (2006).
    [CrossRef] [PubMed]
  23. C. D. Mobley, Light and Water. Radiative Transfer in Natural Waters (Academic Press, New York, 1994).
  24. A. Gilerson, J. Zhou, S. Hlaing, I. Ioannou, J. Schalles, B. Gross, F. Moshary, and S. Ahmed, “Fluorescence component in the reflectance spectra from coastal waters. Dependence on water composition,” Opt. Express 15(24), 15702–15721 (2007).
    [CrossRef] [PubMed]
  25. Z. P. Lee, http://www.ioccg.org/groups/OCAG_data.html .
  26. A. M. Ciotti, M. R. Lewis, and J. J. Cullen, “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]
  27. A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995).
    [CrossRef]
  28. A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters,” Environ. Res. Lett. 4(4), 045003 (2009), doi: 10.1088/1748-9326/4/4/045003.
    [CrossRef]
  29. W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study,” IEEE Geosci. Remote Sens. Lett. 6(4), 845–849 (2009).
    [CrossRef]
  30. H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
    [CrossRef]
  31. R. C. Smith and K. S. Baker, “Optical properties of the clearest natural waters (200-800 nm),” Appl. Opt. 20(2), 177–184 (1981).
    [CrossRef] [PubMed]
  32. J. Zhou, A. Gilerson, I. Ioannou, S. Hlaing, J. Schalles, B. Gross, F. Moshary, and S. Ahmed, “Retrieving quantum yield of sun-induced chlorophyll fluorescence near surface from hyperspectral in-situ measurement in productive water,” Opt. Express 16(22), 17468–17483 (2008).
    [CrossRef] [PubMed]
  33. P. Gege, and A. Albert, “A tool for inverse modeling of spectral measurements in deep and shallow waters,” in Remote Sensing of Aquatic Coastal Ecosystem Processes: Science and Management Applications, L.L. Richardson and E.F. LeDrew, eds. Chap. 4, Springer, 2006.
  34. J. E. O'Reilly, and 24 Coauthors, “SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3,” in NASA Tech. Memo. 2000–206892, Vol. 11, S. B. Hooker and E. R. Firestone, eds., (NASA Goddard Space Flight Center, Greenbelt, MD, 2000) 49 pp.

2009 (4)

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]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters,” Environ. Res. Lett. 4(4), 045003 (2009), doi: 10.1088/1748-9326/4/4/045003.
[CrossRef]

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study,” IEEE Geosci. Remote Sens. Lett. 6(4), 845–849 (2009).
[CrossRef]

2008 (3)

J. Zhou, A. Gilerson, I. Ioannou, S. Hlaing, J. Schalles, B. Gross, F. Moshary, and S. Ahmed, “Retrieving quantum yield of sun-induced chlorophyll fluorescence near surface from hyperspectral in-situ measurement in productive water,” Opt. Express 16(22), 17468–17483 (2008).
[CrossRef] [PubMed]

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

R. Doerffer and H. Schiller, “MERIS regional coastal and lake case 2 water project atmospheric correction ATBD,” Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany, Rep, GKSS-KOF-MERIS-ATB D01, 1 (2008).

2007 (3)

2006 (1)

2005 (3)

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[CrossRef]

G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl. Opt. 44(3), 412–422 (2005).
[CrossRef] [PubMed]

M. Wang and W. Shi, “Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies,” Geophys. Res. Lett. 32(13), L13606 (2005), doi:.
[CrossRef]

2003 (1)

G. Dall'Olmo, A. A. Gitelson, and D. C. Rundquist, “Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters,” Geophys. Res. Lett. 30(18), 1938 (2003), doi:.
[CrossRef]

2002 (2)

H. J. Gons, M. Rijkeboer, and K. G. Ruddick, “A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters,” J. Plankton Res. 24(9), 947–951 (2002).
[CrossRef]

A. M. Ciotti, M. R. Lewis, and J. J. Cullen, “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]

2001 (1)

2000 (1)

J. Aiken and G. Moore, “ATBD case 2 bright pixel atmospheric correction,” Center for Coastal & Marine Sciences, Plymouth Marine Laboratory, U.K., Rep, PO-TN-MEL-GS 4, 0005 (2000).

1999 (1)

G. Moore, J. Aiken, and J. Lavender, “The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: application to MERIS,” Int. J. Remote Sens. 20(9), 1713–1733 (1999).
[CrossRef]

1997 (1)

L. Han and D. Rundquist, “Comparison of NIR/Red ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: a case study in a turbid reservoir,” Remote Sens. Environ. 62(3), 253–261 (1997).
[CrossRef]

1995 (1)

A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995).
[CrossRef]

1992 (1)

A. A. Gitelson, “The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration,” Int. J. Remote Sens. 13(17), 3367–3373 (1992).
[CrossRef]

1988 (2)

R. P. Stumpf and M. A. Tyler, “Satellite detection of bloom and pigment distributions in estuaries,” Remote Sens. Environ. 24(3), 385–404 (1988).
[CrossRef]

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

1985 (1)

A. A. Gitelson, G. Keydan, and V. Shishkin, “Inland waters quality assessment from satellite data in visible range of the spectrum,” Sov. Remote Sens. 6, 28–36 (1985).

1982 (1)

A. Vasilkov and O. Kopelevich, “Reasons for the appearance of the maximum near 700 nm in the radiance spectrum emitted by the ocean layer,” Oceanology (Mosc.) 22, 697–701 (1982).

1981 (1)

1979 (1)

Ahmed, S.

Aiken, J.

J. Aiken and G. Moore, “ATBD case 2 bright pixel atmospheric correction,” Center for Coastal & Marine Sciences, Plymouth Marine Laboratory, U.K., Rep, PO-TN-MEL-GS 4, 0005 (2000).

G. Moore, J. Aiken, and J. Lavender, “The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: application to MERIS,” Int. J. Remote Sens. 20(9), 1713–1733 (1999).
[CrossRef]

Babin, M.

A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995).
[CrossRef]

Bailey, S. W.

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]

Baker, K. S.

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

R. C. Smith and K. S. Baker, “Optical properties of the clearest natural waters (200-800 nm),” Appl. Opt. 20(2), 177–184 (1981).
[CrossRef] [PubMed]

Barrow, T.

A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters,” Environ. Res. Lett. 4(4), 045003 (2009), doi: 10.1088/1748-9326/4/4/045003.
[CrossRef]

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

Berdnikov, S.

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study,” IEEE Geosci. Remote Sens. Lett. 6(4), 845–849 (2009).
[CrossRef]

Borstad, G.

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[CrossRef]

Bricaud, A.

A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995).
[CrossRef]

Brown, J. W.

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

Brown, L.

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[CrossRef]

Brown, O. B.

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

Ciotti, A. M.

A. M. Ciotti, M. R. Lewis, and J. J. Cullen, “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]

Clark, D. K.

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

Claustre, H.

A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995).
[CrossRef]

Cullen, J. J.

A. M. Ciotti, M. R. Lewis, and J. J. Cullen, “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]

Dall’Olmo, G.

Dall'Olmo, G.

G. Dall'Olmo, A. A. Gitelson, and D. C. Rundquist, “Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters,” Geophys. Res. Lett. 30(18), 1938 (2003), doi:.
[CrossRef]

Doerffer, R.

R. Doerffer and H. Schiller, “MERIS regional coastal and lake case 2 water project atmospheric correction ATBD,” Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany, Rep, GKSS-KOF-MERIS-ATB D01, 1 (2008).

Evans, R. H.

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

Feldman, G. C.

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]

Fisher, T. R.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

Franz, B. A.

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]

Gilerson, A.

Gitelson, A. A.

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study,” IEEE Geosci. Remote Sens. Lett. 6(4), 845–849 (2009).
[CrossRef]

A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters,” Environ. Res. Lett. 4(4), 045003 (2009), doi: 10.1088/1748-9326/4/4/045003.
[CrossRef]

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

A. A. Gitelson, J. Schalles, and C. M. Hladik, “Remote chlorophyll-a retrieval in turbid productive estuarine: Chesapeake Bay case study,” Remote Sens. Environ. 109(4), 464–472 (2007).
[CrossRef]

G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of chlorophyll-a concentration in turbid productive waters: modeling results,” Appl. Opt. 45(15), 3577–3592 (2006).
[CrossRef] [PubMed]

G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl. Opt. 44(3), 412–422 (2005).
[CrossRef] [PubMed]

G. Dall'Olmo, A. A. Gitelson, and D. C. Rundquist, “Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters,” Geophys. Res. Lett. 30(18), 1938 (2003), doi:.
[CrossRef]

A. A. Gitelson, “The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration,” Int. J. Remote Sens. 13(17), 3367–3373 (1992).
[CrossRef]

A. A. Gitelson, G. Keydan, and V. Shishkin, “Inland waters quality assessment from satellite data in visible range of the spectrum,” Sov. Remote Sens. 6, 28–36 (1985).

Gons, H. J.

H. J. Gons, M. Rijkeboer, and K. G. Ruddick, “A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters,” J. Plankton Res. 24(9), 947–951 (2002).
[CrossRef]

K. G. Ruddick, H. J. Gons, M. Rijkeboer, and G. Tilstone, “Optical remote sensing of chlorophyll a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties,” Appl. Opt. 40(21), 3575–3585 (2001).
[CrossRef]

Gordon, H. R.

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

H. R. Gordon, “Diffuse reflectance of the ocean: the theory of its augmentation by chlorophyll a fluorescence at 685 nm,” Appl. Opt. 18(8), 1161–1166 (1979).
[CrossRef] [PubMed]

Gower, J.

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[CrossRef]

Gross, B.

Gurlin, D.

A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters,” Environ. Res. Lett. 4(4), 045003 (2009), doi: 10.1088/1748-9326/4/4/045003.
[CrossRef]

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

Han, L.

L. Han and D. Rundquist, “Comparison of NIR/Red ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: a case study in a turbid reservoir,” Remote Sens. Environ. 62(3), 253–261 (1997).
[CrossRef]

Harding, L. W.

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]

Hladik, C. M.

A. A. Gitelson, J. Schalles, and C. M. Hladik, “Remote chlorophyll-a retrieval in turbid productive estuarine: Chesapeake Bay case study,” Remote Sens. Environ. 109(4), 464–472 (2007).
[CrossRef]

Hlaing, S.

Holz, J.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

Huang, C.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

Ioannou, I.

Keydan, G.

A. A. Gitelson, G. Keydan, and V. Shishkin, “Inland waters quality assessment from satellite data in visible range of the spectrum,” Sov. Remote Sens. 6, 28–36 (1985).

King, S.

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[CrossRef]

Kopelevich, O.

A. Vasilkov and O. Kopelevich, “Reasons for the appearance of the maximum near 700 nm in the radiance spectrum emitted by the ocean layer,” Oceanology (Mosc.) 22, 697–701 (1982).

Lavender, J.

G. Moore, J. Aiken, and J. Lavender, “The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: application to MERIS,” Int. J. Remote Sens. 20(9), 1713–1733 (1999).
[CrossRef]

Le, C.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

Lewis, M. R.

A. M. Ciotti, M. R. Lewis, and J. J. Cullen, “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]

Li, Y.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

Lu, H.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

McClain, C. R.

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]

Moore, G.

J. Aiken and G. Moore, “ATBD case 2 bright pixel atmospheric correction,” Center for Coastal & Marine Sciences, Plymouth Marine Laboratory, U.K., Rep, PO-TN-MEL-GS 4, 0005 (2000).

G. Moore, J. Aiken, and J. Lavender, “The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: application to MERIS,” Int. J. Remote Sens. 20(9), 1713–1733 (1999).
[CrossRef]

Morel, A.

A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995).
[CrossRef]

Moses, W.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

Moses, W. J.

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study,” IEEE Geosci. Remote Sens. Lett. 6(4), 845–849 (2009).
[CrossRef]

A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters,” Environ. Res. Lett. 4(4), 045003 (2009), doi: 10.1088/1748-9326/4/4/045003.
[CrossRef]

Moshary, F.

Povazhnyy, V.

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study,” IEEE Geosci. Remote Sens. Lett. 6(4), 845–849 (2009).
[CrossRef]

Rijkeboer, M.

H. J. Gons, M. Rijkeboer, and K. G. Ruddick, “A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters,” J. Plankton Res. 24(9), 947–951 (2002).
[CrossRef]

K. G. Ruddick, H. J. Gons, M. Rijkeboer, and G. Tilstone, “Optical remote sensing of chlorophyll a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties,” Appl. Opt. 40(21), 3575–3585 (2001).
[CrossRef]

Ruddick, K. G.

H. J. Gons, M. Rijkeboer, and K. G. Ruddick, “A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters,” J. Plankton Res. 24(9), 947–951 (2002).
[CrossRef]

K. G. Ruddick, H. J. Gons, M. Rijkeboer, and G. Tilstone, “Optical remote sensing of chlorophyll a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties,” Appl. Opt. 40(21), 3575–3585 (2001).
[CrossRef]

Rundquist, D.

L. Han and D. Rundquist, “Comparison of NIR/Red ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: a case study in a turbid reservoir,” Remote Sens. Environ. 62(3), 253–261 (1997).
[CrossRef]

Rundquist, D. C.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

G. Dall'Olmo, A. A. Gitelson, and D. C. Rundquist, “Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters,” Geophys. Res. Lett. 30(18), 1938 (2003), doi:.
[CrossRef]

Schalles, J.

Schiller, H.

R. Doerffer and H. Schiller, “MERIS regional coastal and lake case 2 water project atmospheric correction ATBD,” Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany, Rep, GKSS-KOF-MERIS-ATB D01, 1 (2008).

Shi, W.

M. Wang and W. Shi, “The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing,” Opt. Express 15(24), 15722–15733 (2007).
[CrossRef] [PubMed]

M. Wang and W. Shi, “Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies,” Geophys. Res. Lett. 32(13), L13606 (2005), doi:.
[CrossRef]

Shishkin, V.

A. A. Gitelson, G. Keydan, and V. Shishkin, “Inland waters quality assessment from satellite data in visible range of the spectrum,” Sov. Remote Sens. 6, 28–36 (1985).

Smith, R. C.

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

R. C. Smith and K. S. Baker, “Optical properties of the clearest natural waters (200-800 nm),” Appl. Opt. 20(2), 177–184 (1981).
[CrossRef] [PubMed]

Stumpf, R. P.

R. P. Stumpf and M. A. Tyler, “Satellite detection of bloom and pigment distributions in estuaries,” Remote Sens. Environ. 24(3), 385–404 (1988).
[CrossRef]

Sun, D.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

Tilstone, G.

Tyler, M. A.

R. P. Stumpf and M. A. Tyler, “Satellite detection of bloom and pigment distributions in estuaries,” Remote Sens. Environ. 24(3), 385–404 (1988).
[CrossRef]

Vasilkov, A.

A. Vasilkov and O. Kopelevich, “Reasons for the appearance of the maximum near 700 nm in the radiance spectrum emitted by the ocean layer,” Oceanology (Mosc.) 22, 697–701 (1982).

Wang, M.

M. Wang and W. Shi, “The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing,” Opt. Express 15(24), 15722–15733 (2007).
[CrossRef] [PubMed]

M. Wang and W. Shi, “Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies,” Geophys. Res. Lett. 32(13), L13606 (2005), doi:.
[CrossRef]

Werdell, P. J.

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]

Zha, Y.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

Zhou, J.

Appl. Opt. (5)

Center for Coastal & Marine Sciences, Plymouth Marine Laboratory, U.K., Rep, PO-TN-MEL-GS (1)

J. Aiken and G. Moore, “ATBD case 2 bright pixel atmospheric correction,” Center for Coastal & Marine Sciences, Plymouth Marine Laboratory, U.K., Rep, PO-TN-MEL-GS 4, 0005 (2000).

Environ. Res. Lett. (1)

A. A. Gitelson, D. Gurlin, W. J. Moses, and T. Barrow, “A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters,” Environ. Res. Lett. 4(4), 045003 (2009), doi: 10.1088/1748-9326/4/4/045003.
[CrossRef]

Geophys. Res. Lett. (2)

M. Wang and W. Shi, “Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies,” Geophys. Res. Lett. 32(13), L13606 (2005), doi:.
[CrossRef]

G. Dall'Olmo, A. A. Gitelson, and D. C. Rundquist, “Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters,” Geophys. Res. Lett. 30(18), 1938 (2003), doi:.
[CrossRef]

IEEE Geosci. Remote Sens. Lett. (1)

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study,” IEEE Geosci. Remote Sens. Lett. 6(4), 845–849 (2009).
[CrossRef]

Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany, Rep, GKSS-KOF-MERIS-ATB (1)

R. Doerffer and H. Schiller, “MERIS regional coastal and lake case 2 water project atmospheric correction ATBD,” Institute for Coastal Research, GKSS Research Center, Geesthacht, Germany, Rep, GKSS-KOF-MERIS-ATB D01, 1 (2008).

Int. J. Remote Sens. (3)

G. Moore, J. Aiken, and J. Lavender, “The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: application to MERIS,” Int. J. Remote Sens. 20(9), 1713–1733 (1999).
[CrossRef]

A. A. Gitelson, “The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration,” Int. J. Remote Sens. 13(17), 3367–3373 (1992).
[CrossRef]

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[CrossRef]

J. Geophys. Res. (2)

H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93(D9), 10909–10924 (1988).
[CrossRef]

A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995).
[CrossRef]

J. Plankton Res. (1)

H. J. Gons, M. Rijkeboer, and K. G. Ruddick, “A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters,” J. Plankton Res. 24(9), 947–951 (2002).
[CrossRef]

Limnol. Oceanogr. (1)

A. M. Ciotti, M. R. Lewis, and J. J. Cullen, “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]

Oceanology (Mosc.) (1)

A. Vasilkov and O. Kopelevich, “Reasons for the appearance of the maximum near 700 nm in the radiance spectrum emitted by the ocean layer,” Oceanology (Mosc.) 22, 697–701 (1982).

Opt. Express (3)

Remote Sens. Environ. (6)

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]

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[CrossRef]

R. P. Stumpf and M. A. Tyler, “Satellite detection of bloom and pigment distributions in estuaries,” Remote Sens. Environ. 24(3), 385–404 (1988).
[CrossRef]

L. Han and D. Rundquist, “Comparison of NIR/Red ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: a case study in a turbid reservoir,” Remote Sens. Environ. 62(3), 253–261 (1997).
[CrossRef]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[CrossRef]

A. A. Gitelson, J. Schalles, and C. M. Hladik, “Remote chlorophyll-a retrieval in turbid productive estuarine: Chesapeake Bay case study,” Remote Sens. Environ. 109(4), 464–472 (2007).
[CrossRef]

Sov. Remote Sens. (1)

A. A. Gitelson, G. Keydan, and V. Shishkin, “Inland waters quality assessment from satellite data in visible range of the spectrum,” Sov. Remote Sens. 6, 28–36 (1985).

Other (5)

J. F. Schalles, “Optical Remote Sensing Techniques to Estimate Phytoplankton Chlorophyll a Concentrations in Coastal Waters with Varying Suspended Matter and CDOM Concentrations,” in Remote Sensing of Aquatic Coastal Ecosystem Processes: Science and Management Applications, L.L. Richardson and E.F. LeDrew, eds. (Springer, 2006), Chap. 3.

P. Gege, and A. Albert, “A tool for inverse modeling of spectral measurements in deep and shallow waters,” in Remote Sensing of Aquatic Coastal Ecosystem Processes: Science and Management Applications, L.L. Richardson and E.F. LeDrew, eds. Chap. 4, Springer, 2006.

J. E. O'Reilly, and 24 Coauthors, “SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3,” in NASA Tech. Memo. 2000–206892, Vol. 11, S. B. Hooker and E. R. Firestone, eds., (NASA Goddard Space Flight Center, Greenbelt, MD, 2000) 49 pp.

Z. P. Lee, http://www.ioccg.org/groups/OCAG_data.html .

C. D. Mobley, Light and Water. Radiative Transfer in Natural Waters (Academic Press, New York, 1994).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (13)

Fig. 1
Fig. 1

Phytoplankton specific absorption spectra used in the simulations.

Fig. 8
Fig. 8

Phytoplankton specific absorption coefficient at (a) 440 nm and (b) 675 nm plotted against [Chl] for the field data collected from inland and coastal waters.

Fig. 2
Fig. 2

Comparison of estimates of chlorophyll-a concentrations calculated using Eq. (3) (a) and Eq. (4) (b) and by synthetic reflectance spectra with a p h * ( 675 ) = 0.0156 m2/(mg Chl a) (blue dots): a) two-band algorithm, b) three-band algorithm.

Fig. 3
Fig. 3

Chlorophyll-a concentration vs. two-band red-NIR model calculated using synthetic data, the impact of the shape of the phytoplankton specific absorption coefficient.

Fig. 4
Fig. 4

Chlorophyll-a concentration vs. two-band red-NIR ratio with variations in (a) CDOM absorption, 0<ay(400)<5m−1, (b) CNAP concentration, 0<CNAP<10 mg/l, and (c) fluorescence quantum yield, η = 0.25, 0.5 and 1.0.

Fig. 5
Fig. 5

Contributions of the main components of Eq. (9) to the relationship [Chl] vs. Rrs(708)/Rrs(665): The ranges of concentrations considered were, [Chl] = 1-40 mg/m3, ay = 0-3m−1, CNAP = 0-10 mg/l, for (a) a*ph(675) = 0.0142 m2/(mg Chl a) and (b) a*ph(675) = 0.02 m2/(mg Chl a). “chl” is the [Chl] from the synthetic data set, “chl calc” is the [Chl] calculated using the Eq. (9), “acdom” is [ a y ( λ 1 ) R 2 a y ( λ 2 ) ] / a p h * ( λ 2 ) , the CDOM absorption term from Eq. (9), “awater” = [ a w ( λ 1 ) R 2 a w ( λ 2 ) ] / a p h * ( λ 2 ) , the water absorption term from Eq. (9), and “b/scatter” is [ b b ( λ 1 ) R 2 b b ( λ 2 ) ] / a p h * ( λ 2 ) , the backscattering term from Eq. (9).

Fig. 6
Fig. 6

Contributions of the main components of Eq. (9) to the relationship [Chl] vs. Rrs(753)/Rrs(665): a) a*ph(675) = 0.0142 m2/(mg Chl a), b) a*ph(675) = 0.02 m2/(mg Chl a). Notation and the ranges of values for [Chl], ay, and CNAP are the same as for Fig. 5.

Fig. 7
Fig. 7

Contributions of main components of Eq. (12) to the [Chl] vs R3 relationship: a) a*ph(675) = 0.0142 m2/(mg Chl a), b) a*ph(675) = 0.0142 m2/(mg Chl a). “chl” is the [Chl] from the data set, “chl calc” is the [Chl] from the Eq. (12), “acdom” is [ a y ( λ 2 ) a y ( λ 1 ) ] / a p h * ( λ 2 ) , the CDOM absorption term from (12), “awater” is [ a w ( λ 3 ) R 3 a w ( λ 1 ) + a w ( λ 2 ) ] / a p h * ( λ 2 ) , the water absorption term from (12) and “b/scatter” is [ b b ( λ 2 ) b b ( λ 1 ) ] / a p h * ( λ 2 ) , the backscattering term from (12). The ranges of values for [Chl], ay, and CNAP are the same as for Fig. 5.

Fig. 9
Fig. 9

a) power fit for all points of Fig. 8(b) for a*ph (675) vs. [Chl]; R2 = 0.2265; the brown and pink lines represent bounds for the 95% confidence interval. b) power fit for a*ph (675) vs. a*ph (665); R2 = 0.965. Inset: a*ph spectra with higher a*ph (675) values – specific absorption coefficient spectra of: Cryptophyta “H” (1), Diatoms (2) and Green algae (3) from Gege et al. [33].

Fig. 10
Fig. 10

Comparison of analytical relationships Eqs. (17.1) and (19.1) with the field data (Nebraska 2008 [28]) and empirical algorithms from MERIS –field data [29]: a) R2: Rrs(708)/Rrs(665), b) R2: Rrs(753)/Rrs(665), c) R3 = [Rrs(665)−1 – Rrs(708)−1]*Rrs(753). Red lines correspond to the Eq. (17.1) and (17.2) for Rrs(708)/Rrs(665) algorithm [Fig. 10(a)], the Eq. (17.1) for Rrs(753)/Rrs(665) algorithm [Fig. 10(b)] and to the Eqs. (19.1) and (19.2) for [Rrs(665)−1 –Rrs(708)−1] *Rrs(753) algorithm [Fig. 10(c)]. Lower and upper a*ph bounds correspond to the bounds in Fig. 9(a), MERIS – field algorithm – cyan lines [Eq. (3)] for Rrs(708)/Rrs(665) algorithm [Fig. 10(a)] and Eq. (4) for 3 bands algorithm [Fig. 10(c)].

Fig. 11
Fig. 11

Correlations between [Chl] measured in the Nebraska and [Chl] from the analytically-derived [Chl] from (a) Eq. (17.2): Rrs(708)/Rrs(665), and (b) Eq. (19.2): [Rrs(665)−1 – Rrs(708)−1]*Rrs(753).

Fig. 12
Fig. 12

Performance of OC3M algorithm on synthetic and field data. On the x-axis is the blue-green ratio, which is the primary element of the OC3M algorithm. The red curve represents the [Chl] values estimated by the OC3M algorithm.

Fig. 13
Fig. 13

Plots of OC3M-derived [Chl] versus (a) [Chl] from the Nebraska field data set and (b) synthetically derived [Chl]. Concentrations of non-algal particles, CNAP, ranged from 0 to 10 mg/l, CDOM absorption was in the range of 0<ay(400)<3 m−1 .

Equations (21)

Equations on this page are rendered with MathJax. Learn more.

a p h ( λ ) = [ C h l ] a p h * ( λ )
a p h * ( λ ) = S f a p i c o * ( λ ) + ( 1 S f ) a m i c r o * ( λ )
[Chl]  =   61.324 * R r s ( 708 ) / R r s ( 665 ) 37.94
[Chl]  = 232 .329[ ( R r s ( 665 ) 1 R r s ( 708 ) 1 ) * R r s ( 753 ) ] + 23.174
R rs ( λ ) = 0.53 f Q b b ( λ ) a ( λ ) + b b ( λ )
a ( λ ) = a p h ( λ ) + a y ( λ ) + a N A P ( λ ) + a w ( λ )
R2  =  R rs ( λ 1 ) / R rs ( λ 2 ) = [ b b ( λ 1 ) / b b ( λ 2 ) ] * [ a p h ( λ 2 ) + a y ( λ 2 ) + a w ( λ 2 ) + b b ( λ 2 ) ] / [ a p h ( λ 1 ) + a y ( λ 1 ) + a w ( λ 1 ) + b b ( λ 1 ) ]
[ a p h ( λ 1 ) + a y ( λ 1 ) + a w ( λ 1 ) + b b ( λ 1 ) ] * R 2 = [ a p h ( λ 2 ) + a y ( λ 2 ) + a w ( λ 2 ) + b b ( λ 2 ) ]
[ Chl ] = { [ a y ( λ 1 ) R 2 a y ( λ 2 ) ] + [ a w ( λ 1 ) R 2 a w ( λ 2 ) ] + [ b b ( λ 1 ) R 2 b b ( λ 2 ) ] } / [ a p h * ( λ 2 ) a p h * ( λ 1 ) R 2 ]
R3  = [ R r s ( λ 1 ) 1 R r s ( λ 2 ) 1 ] * R r s ( λ 3 )
R3  = { [ a p h ( λ 1 ) + a y ( λ 1 ) + a w ( λ 1 ) + b b ( λ 1 ) ] / b b ( λ 1 ) [ a p h ( λ 2 ) + a y ( λ 2 ) + a w ( λ 2 ) + b b ( λ 2 ) ] / b b ( λ 2 ) } * { b b ( λ 3 ) / [ a p h ( λ 3 ) + a y ( λ 3 ) + a w ( λ 3 ) + b b ( λ 3 ) ] }
[ Chl ] = { [ a w ( λ 3 ) R 3 a w ( λ 1 ) + a w ( λ 2 ) ] + [ a y ( λ 2 ) a y ( λ 1 ) ] + [ b b ( λ 2 ) b b ( λ 1 ) ] } / [ a p h * ( λ 1 ) a p h * ( λ 2 ) ]
a p h * ( 675 ) = 0.03 [ C h l ] 0.2
a p h * ( 665 ) = 0.412 a p h * ( 675 ) ] 0.8373
a p h * ( 665 ) = 0.022 [ C h l ] 0.1675
[ Chl ] = [ a w ( λ 1 ) R 2 a w ( λ 2 ) ] / a p h * ( 665 )
[ Chl ] = { [ a w ( λ 1 ) R 2 a w ( λ 2 ) ] / 0.022   ​ } 1 / p
[ Chl ] = [ 35.75 * R 2 19.30 ] 1.124
[ Chl ] = [ a w ( λ 3 ) R 3 a w ( λ 1 ) + a w ( λ 2 ) ] / a p h * ( 665 )
[ Chl ] = { [ a w ( λ 3 ) R 3 a w ( λ 1 ) + a w ( λ 2 ) ] / 0.022 } ( 1 / p )
[ Chl ] = [ 113.36 * R 3 + 16.45 ] 1.124

Metrics