Abstract

Errors in the estimated constituent concentrations in optically complex waters due solely to sensor noise in a spaceborne hyperspectral sensor can be as high as 80%. The goal of this work is to elucidate the effect of signal-to-noise ratio (SNR) on the accuracy of retrieved constituent concentrations. Large variations in the magnitude and spectral shape of the reflectances from coastal waters complicate the impact of SNR on the accuracy of estimation. Due to the low reflectance of water, the actual SNR encountered for a water target is usually quite lower than the prescribed SNR. The low SNR can be a significant source of error in the estimated constituent concentrations. Simulated and measured at-surface reflectances were used in this study. A radiative transfer code, Tafkaa, was used to propagate the at-surface reflectances up and down through the atmosphere. A sensor noise model based on that of the spaceborne hyperspectral sensor HICO was applied to the at-sensor radiances. Concentrations of chlorophyll-a, colored dissolved organic matter, and total suspended solids were estimated using an optimized error minimization approach and a few semi-analytical algorithms. Improving the SNR by reasonably modifying the sensor design can reduce estimation uncertainties by 10% or more.

© 2012 OSA

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2011 (2)

R. L. Lucke, M. Corson, N. R. McGlothlin, S. D. Butcher, D. L. Wood, D. R. Korwan, R. R. Li, W. A. Snyder, C. O. Davis, and D. T. Chen, “Hyperspectral Imager for the Coastal Ocean: instrument description and first images,” Appl. Opt. 50(11), 1501–1516 (2011).
[CrossRef] [PubMed]

D. Gurlin, A. A. Gitelson, and W. J. Moses, “Remote estimation of chl-a concentration in turbid productive waters – return to a simple two-band NIR-red model?” Remote Sens. Environ. 115(12), 3479–3490 (2011).
[CrossRef]

2010 (2)

2009 (4)

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data - successes and challenges,” Environ. Res. Lett. 4(045005), 8 (2009).

A. Gitelson, D. Gurlin, W. 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(045003), 5 (2009).

M. S. Salama and A. Stein, “Error decomposition and estimation of inherent optical properties,” Appl. Opt. 48(26), 4947–4962 (2009).
[CrossRef] [PubMed]

W. Moses, 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 (1)

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]

2007 (2)

2006 (1)

R. L. Lucke and R. A. Kessel, “Signal-to-noise ratio, contrast-to-noise ratio and exposure time for imaging systems with photon-limited noise,” Opt. Eng. 45(5), 056403 (2006).
[CrossRef]

2005 (2)

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, D. C. Rundquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands,” Remote Sens. Environ. 96(2), 176–187 (2005).
[CrossRef]

2004 (5)

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

M. Sydor, R. W. Gould, R. A. Arnone, V. I. Haltrin, and W. Goode, “Uniqueness in remote sensing of the inherent optical properties of ocean water,” Appl. Opt. 43(10), 2156–2162 (2004).
[CrossRef] [PubMed]

P. W. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. R. Kohler, and R. W. Gould, “From meters to kilometers - a look at ocean color scales of variability, spatial coherence, and the need for fine scale remote sensing in coastal ocean optics,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).

K. L. Carder, F. R. Chen, J. P. Cannizzaro, J. W. Campbell, and B. G. Mitchell, “Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a,” Adv. Space Res. 33(7), 1152–1159 (2004).
[CrossRef]

M. Darecki and D. Stramski, “An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea,” Remote Sens. Environ. 89(3), 326–350 (2004).
[CrossRef]

2003 (3)

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

V. E. Brando and A. G. Dekker, “Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality,” IEEE Trans. Geosci. Rem. Sens. 41(6), 1378–1387 (2003).
[CrossRef]

F. A. Kruse, J. W. Boardman, and J. F. Huntington, “Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping,” IEEE Trans. Geosci. Rem. Sens. 41(6), 1388–1400 (2003).
[CrossRef]

2000 (2)

B. C. Gao, M. J. Montes, Z. Ahmad, and C. O. Davis, “Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space,” Appl. Opt. 39(6), 887–896 (2000).
[CrossRef] [PubMed]

C. Hu, K. L. Carder, and F. E. Müller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: a practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

1999 (1)

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]

1998 (1)

J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
[CrossRef]

1992 (1)

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]

1991 (2)

K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B. G. Mitchell, “Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products,” J. Geophys. Res. 96(C11), 20599–20611 (1991).
[CrossRef]

F. E. Müller-Karger, J. J. Walsh, R. H. Evans, and M. B. Meyers, “On the seasonal phytoplankton concentration and sea surface temperature cycles of the Gulf of Mexico as determined by satellites,” J. Geophys. Res. 96(C7), 12645–12665 (1991).
[CrossRef]

1989 (1)

C. D. Mobley, “A numerical model for the computation of radiance distributions in natural waters with wind roughened surfaces,” Limnol. Oceanogr. 34(8), 1473–1483 (1989).
[CrossRef]

1980 (1)

A. Morel, “In-water and remote measurement of ocean color,” Boundary-Layer Meterol. 18(2), 177–201 (1980).
[CrossRef]

1977 (1)

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[CrossRef]

1963 (1)

D. W. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters,” SIAM J. Appl. Math. 11(2), 431–441 (1963).
[CrossRef]

1944 (1)

K. Levenberg, “A method for the solution of certain non-linear problems in least squares,” Q. Appl. Math. 2, 164–168 (1944).

Ahmad, Z.

Arnone, R.

Arnone, R. A.

M. Sydor, R. W. Gould, R. A. Arnone, V. I. Haltrin, and W. Goode, “Uniqueness in remote sensing of the inherent optical properties of ocean water,” Appl. Opt. 43(10), 2156–2162 (2004).
[CrossRef] [PubMed]

P. W. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. R. Kohler, and R. W. Gould, “From meters to kilometers - a look at ocean color scales of variability, spatial coherence, and the need for fine scale remote sensing in coastal ocean optics,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).

Babin, M.

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

Baker, K. A.

K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B. G. Mitchell, “Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products,” J. Geophys. Res. 96(C11), 20599–20611 (1991).
[CrossRef]

Barrow, T.

A. Gitelson, D. Gurlin, W. 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(045003), 5 (2009).

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, D. C. Rundquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands,” Remote Sens. Environ. 96(2), 176–187 (2005).
[CrossRef]

Berdnikov, S.

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data - successes and challenges,” Environ. Res. Lett. 4(045005), 8 (2009).

W. Moses, 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]

Bissett, P. W.

P. W. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. R. Kohler, and R. W. Gould, “From meters to kilometers - a look at ocean color scales of variability, spatial coherence, and the need for fine scale remote sensing in coastal ocean optics,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).

Boardman, J. W.

F. A. Kruse, J. W. Boardman, and J. F. Huntington, “Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping,” IEEE Trans. Geosci. Rem. Sens. 41(6), 1388–1400 (2003).
[CrossRef]

Boss, E.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Brando, V. E.

V. E. Brando and A. G. Dekker, “Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality,” IEEE Trans. Geosci. Rem. Sens. 41(6), 1378–1387 (2003).
[CrossRef]

Bricaud, A.

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

Briggs-Whitmire, A.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Butcher, S. D.

Campbell, J. W.

K. L. Carder, F. R. Chen, J. P. Cannizzaro, J. W. Campbell, and B. G. Mitchell, “Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a,” Adv. Space Res. 33(7), 1152–1159 (2004).
[CrossRef]

Cannizzaro, J. P.

K. L. Carder, F. R. Chen, J. P. Cannizzaro, J. W. Campbell, and B. G. Mitchell, “Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a,” Adv. Space Res. 33(7), 1152–1159 (2004).
[CrossRef]

Carder, K. L.

K. L. Carder, F. R. Chen, J. P. Cannizzaro, J. W. Campbell, and B. G. Mitchell, “Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a,” Adv. Space Res. 33(7), 1152–1159 (2004).
[CrossRef]

C. Hu, K. L. Carder, and F. E. Müller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: a practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

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]

J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
[CrossRef]

K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B. G. Mitchell, “Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products,” J. Geophys. Res. 96(C11), 20599–20611 (1991).
[CrossRef]

Chami, M.

M. Defoin-Platel and M. Chami, “How ambiguous is the inverse problem of ocean color in coastal waters?” J. Geophys. Res. 112(C3), C03004 (2007), doi:.
[CrossRef]

Chang, G.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Chen, D. T.

Chen, F. R.

K. L. Carder, F. R. Chen, J. P. Cannizzaro, J. W. Campbell, and B. G. Mitchell, “Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a,” Adv. Space Res. 33(7), 1152–1159 (2004).
[CrossRef]

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]

Claustre, H.

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

Corson, M.

Dall’Olmo, G.

Dall'Olmo, G.

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, D. C. Rundquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands,” Remote Sens. Environ. 96(2), 176–187 (2005).
[CrossRef]

Darecki, M.

M. Darecki and D. Stramski, “An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea,” Remote Sens. Environ. 89(3), 326–350 (2004).
[CrossRef]

Davis, C. O.

Defoin-Platel, M.

M. Defoin-Platel and M. Chami, “How ambiguous is the inverse problem of ocean color in coastal waters?” J. Geophys. Res. 112(C3), C03004 (2007), doi:.
[CrossRef]

Dekker, A. G.

V. E. Brando and A. G. Dekker, “Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality,” IEEE Trans. Geosci. Rem. Sens. 41(6), 1378–1387 (2003).
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Dickey, T. D.

P. W. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. R. Kohler, and R. W. Gould, “From meters to kilometers - a look at ocean color scales of variability, spatial coherence, and the need for fine scale remote sensing in coastal ocean optics,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Dye, D.

P. W. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. R. Kohler, and R. W. Gould, “From meters to kilometers - a look at ocean color scales of variability, spatial coherence, and the need for fine scale remote sensing in coastal ocean optics,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).

Evans, R. H.

F. E. Müller-Karger, J. J. Walsh, R. H. Evans, and M. B. Meyers, “On the seasonal phytoplankton concentration and sea surface temperature cycles of the Gulf of Mexico as determined by satellites,” J. Geophys. Res. 96(C7), 12645–12665 (1991).
[CrossRef]

Ferrari, G. M.

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

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]

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Garver, S. A.

J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
[CrossRef]

Gitelson, A.

W. Moses, 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. Gitelson, D. Gurlin, W. 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(045003), 5 (2009).

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).
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Gitelson, A. A.

D. Gurlin, A. A. Gitelson, and W. J. Moses, “Remote estimation of chl-a concentration in turbid productive waters – return to a simple two-band NIR-red model?” Remote Sens. Environ. 115(12), 3479–3490 (2011).
[CrossRef]

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data - successes and challenges,” Environ. Res. Lett. 4(045005), 8 (2009).

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 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, D. C. Rundquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands,” Remote Sens. Environ. 96(2), 176–187 (2005).
[CrossRef]

Goode, W.

Gould, R. W.

M. Sydor, R. W. Gould, R. A. Arnone, V. I. Haltrin, and W. Goode, “Uniqueness in remote sensing of the inherent optical properties of ocean water,” Appl. Opt. 43(10), 2156–2162 (2004).
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P. W. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. R. Kohler, and R. W. Gould, “From meters to kilometers - a look at ocean color scales of variability, spatial coherence, and the need for fine scale remote sensing in coastal ocean optics,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).

Gurlin, D.

D. Gurlin, A. A. Gitelson, and W. J. Moses, “Remote estimation of chl-a concentration in turbid productive waters – return to a simple two-band NIR-red model?” Remote Sens. Environ. 115(12), 3479–3490 (2011).
[CrossRef]

A. Gitelson, D. Gurlin, W. 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(045003), 5 (2009).

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]

Haltrin, V. I.

Hawes, S. K.

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]

K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B. G. Mitchell, “Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products,” J. Geophys. Res. 96(C11), 20599–20611 (1991).
[CrossRef]

Hoepffner, N.

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

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]

Holz, J. C.

G. Dall'Olmo, A. A. Gitelson, D. C. Rundquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands,” Remote Sens. Environ. 96(2), 176–187 (2005).
[CrossRef]

Hu, C.

Z. P. Lee, R. Arnone, C. Hu, P. J. Werdell, and B. Lubac, “Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm,” Appl. Opt. 49(3), 369–381 (2010).
[CrossRef] [PubMed]

C. Hu, K. L. Carder, and F. E. Müller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: a practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

Huntington, J. F.

F. A. Kruse, J. W. Boardman, and J. F. Huntington, “Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping,” IEEE Trans. Geosci. Rem. Sens. 41(6), 1388–1400 (2003).
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J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
[CrossRef]

Kamykowski, D.

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).
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R. L. Lucke and R. A. Kessel, “Signal-to-noise ratio, contrast-to-noise ratio and exposure time for imaging systems with photon-limited noise,” Opt. Eng. 45(5), 056403 (2006).
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G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Kohler, D. D. R.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

P. W. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. R. Kohler, and R. W. Gould, “From meters to kilometers - a look at ocean color scales of variability, spatial coherence, and the need for fine scale remote sensing in coastal ocean optics,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).

Korwan, D. R.

Kruse, F. A.

F. A. Kruse, J. W. Boardman, and J. F. Huntington, “Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping,” IEEE Trans. Geosci. Rem. Sens. 41(6), 1388–1400 (2003).
[CrossRef]

Leavitt, B.

G. Dall'Olmo, A. A. Gitelson, D. C. Rundquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands,” Remote Sens. Environ. 96(2), 176–187 (2005).
[CrossRef]

Lee, Z. P.

Z. P. Lee, R. Arnone, C. Hu, P. J. Werdell, and B. Lubac, “Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm,” Appl. Opt. 49(3), 369–381 (2010).
[CrossRef] [PubMed]

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).
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Lewis, M.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Li, R. R.

Lubac, B.

Lucke, R. L.

Mahoney, K.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Maritorena, S.

J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
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J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
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McGlothlin, N. R.

Meyers, M. B.

F. E. Müller-Karger, J. J. Walsh, R. H. Evans, and M. B. Meyers, “On the seasonal phytoplankton concentration and sea surface temperature cycles of the Gulf of Mexico as determined by satellites,” J. Geophys. Res. 96(C7), 12645–12665 (1991).
[CrossRef]

Mitchell, B. G.

K. L. Carder, F. R. Chen, J. P. Cannizzaro, J. W. Campbell, and B. G. Mitchell, “Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a,” Adv. Space Res. 33(7), 1152–1159 (2004).
[CrossRef]

J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
[CrossRef]

K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B. G. Mitchell, “Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products,” J. Geophys. Res. 96(C11), 20599–20611 (1991).
[CrossRef]

Mobley, C. D.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

C. D. Mobley, “A numerical model for the computation of radiance distributions in natural waters with wind roughened surfaces,” Limnol. Oceanogr. 34(8), 1473–1483 (1989).
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Moline, M. A.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

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Morel, A.

A. Morel, “In-water and remote measurement of ocean color,” Boundary-Layer Meterol. 18(2), 177–201 (1980).
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A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
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Moses, W.

A. Gitelson, D. Gurlin, W. 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(045003), 5 (2009).

W. Moses, 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, 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.

D. Gurlin, A. A. Gitelson, and W. J. Moses, “Remote estimation of chl-a concentration in turbid productive waters – return to a simple two-band NIR-red model?” Remote Sens. Environ. 115(12), 3479–3490 (2011).
[CrossRef]

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data - successes and challenges,” Environ. Res. Lett. 4(045005), 8 (2009).

Müller-Karger, F. E.

C. Hu, K. L. Carder, and F. E. Müller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: a practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

F. E. Müller-Karger, J. J. Walsh, R. H. Evans, and M. B. Meyers, “On the seasonal phytoplankton concentration and sea surface temperature cycles of the Gulf of Mexico as determined by satellites,” J. Geophys. Res. 96(C7), 12645–12665 (1991).
[CrossRef]

Obolensky, G.

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

O'Reilly, J. E.

J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
[CrossRef]

Philpot, W.

G. Chang, K. Mahoney, A. Briggs-Whitmire, D. D. R. Kohler, C. D. Mobley, M. Lewis, M. A. Moline, E. Boss, M. Kim, W. Philpot, and T. D. Dickey, “The new age of hyperspectral oceanography,” Oceanography (Wash. D.C.) 17(2), 16–23 (2004).

Povazhnyy, V.

W. J. Moses, A. A. Gitelson, S. Berdnikov, and V. Povazhnyy, “Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data - successes and challenges,” Environ. Res. Lett. 4(045005), 8 (2009).

W. Moses, 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]

Prieur, L.

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[CrossRef]

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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, D. C. Rundquist, B. Leavitt, T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands,” Remote Sens. Environ. 96(2), 176–187 (2005).
[CrossRef]

Salama, M. S.

Siegel, D. A.

J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res.- Oceans 103(C11), 24937–24953 (1998).
[CrossRef]

Smith, R. C.

K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B. G. Mitchell, “Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products,” J. Geophys. Res. 96(C11), 20599–20611 (1991).
[CrossRef]

Snyder, W. A.

Stein, A.

Steward, R. G.

K. L. Carder, S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B. G. Mitchell, “Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products,” J. Geophys. Res. 96(C11), 20599–20611 (1991).
[CrossRef]

Stramski, D.

M. Darecki and D. Stramski, “An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea,” Remote Sens. Environ. 89(3), 326–350 (2004).
[CrossRef]

M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108, 3211 (2003), doi:.
[CrossRef] [PubMed]

Stumpf, R. P.

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Figures (14)

Fig. 1
Fig. 1

Flow chart of the data processing steps (TOA: top of atmosphere)

Fig. 2
Fig. 2

Reflectance spectra simulated using Ecolight.

Fig. 3
Fig. 3

At-sensor SNR curves for the synthetically generated Rrs spectra based on nominal HICO sensor configuration (see section 2.1).

Fig. 4
Fig. 4

Noise-added at-sensor radiance for water with chl-a = 2 mg m−3, TSS = 1 g m−3, and aCDOM(440) = 0.1 m−1. The dashed line represents the contribution from atmospheric path radiance and specularly reflected radiance from the water surface.

Fig. 5
Fig. 5

Atmospherically corrected Rrs spectra for water with chl-a = 2 mg m−3, TSS = 1 g m−3, and aCDOM(440) = 0.1 m−1. The black curve represents the original at-surface Rrs spectrum. The gray curves represent mean plus/minus one standard deviation of the atmospherically corrected Rrs, and the dotted curve represents the coefficient of variation (on the secondary y-axis) of the retrieved at-surface Rrs after atmospheric correction following the addition of noise to the at-sensor radiance.

Fig. 6
Fig. 6

Variations in at-sensor SNR as the sensor configuration is changed, for a case with chl-a = 2.27 mg m−3, aCDOM(440) = 1.35 m−1, and TSS = 1.19 g m−3. The solid black line represents the SNR for nominal sensor setting (the blaze wavelength, λb = 400 nm; diameter of the aperture, D = 0.019 m).

Fig. 7
Fig. 7

Quasi-SNR of the noise-added atmospherically corrected Rrs spectra with the nominal sensor setting. High CDOM concentration causes low quasi-SNR, especially in the blue region, whereas high TSS concentration causes high quasi-SNR throughout the spectrum.

Fig. 8
Fig. 8

Normalized RMSE of estimated chl-a concentrations (by the optimized error minimization approach) for (a) aCDOM(440) = 0.1 m−1 and (b) aCDOM(440) = 2 m−1 at various TSS concentrations.

Fig. 9
Fig. 9

Normalized RMSE of estimated TSS concentrations (by the optimized error minimization approach) for (a) aCDOM(440) = 0.1 m−1 and (b) aCDOM(440) = 2 m−1 at various chl-a concentrations.

Fig. 10
Fig. 10

Normalized RMSE of estimated aCDOM(440) (by the optimized error minimization approach) for (a) TSS = 1 g m−3, (b) TSS = 8 g m−3, and (c) TSS = 20 g m−3 at various chl-a concentrations.

Fig. 11
Fig. 11

Normalized RMSE of estimated chl-a concentrations (by the optimized error minimization approach) for various sensor configurations at low and high concentrations of TSS and CDOM: (a) TSS = 1 g m−3 and (b) TSS = 20 g m−3 with aCDOM(440) = 0.1 m−1; (c) TSS = 1 g m−3and (d) TSS = 20 g m−3 with aCDOM(440) = 2 m−1.

Fig. 12
Fig. 12

Normalized RMSE of chl-a concentrations estimated by the OC4E algorithm.

Fig. 13
Fig. 13

Normalized RMSE of chl-a concentrations estimated by the two-band NIR-red algorithm for (a) low and (b) moderate-to-high chl-a concentrations.

Fig. 14
Fig. 14

Normalized RMSE of chl-a concentrations estimated by the three-band NIR-red algorithm for (a) low and (b) moderate-to-high chl-a concentrations.

Tables (4)

Tables Icon

Table 1 Ranges of concentrations of chl-a and TSS, and absorption of CDOM at 440 nm, for which reflectance spectra were generated using Ecolight.

Tables Icon

Table 2 Descriptive statistics of the constituent concentrations in the in situ measured data set. aCDOM(440) is the absorption coefficient of CDOM at 440 nm. Coefficient of variation is the ratio of the standard deviation to the mean.

Tables Icon

Table 3 Geographic, illumination, and atmospheric parameter values used for the upward propagation of the at-surface Rrs.

Tables Icon

Table 4 The magnitude of SNR-peak (SNRpeak), the location of SNR-peak (SNRpeak wl), in nm and the percent normalized RMSEs of chl-a concentration (%NRChl-a), CDOM absorption (%NRaCDOM(440)), and TSS concentration (%NRTSS) estimated by the optimized error minimization approach at low (aCDOM(440) = 0.1 m−1) and high (aCDOM(440) = 2 m−1) concentrations of CDOM for various sensor configurations. The blaze wavelength was kept at 400 nm for sensor configurations with the nominal setting and D = 0.024 m, and the peak SNR was achieved at 457 nm

Equations (12)

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Signal= λ hc L Δλ π 4 D 2 f 2 p 2 T η sys ,
Thus the photon noise= λ hc L Δλ π 4 D 2 f 2 p 2 T η sys
Noise= ( Shot Noise ) 2 + ( Dark Noise ) 2 + ( Readout Noise ) 2 + ( Digitization Noise ) 2
The signal-to-noise ratio is, SNR= Incoming signal Noise
L t = L p + L sfc t+ ρ w μ o E o t π
Chl-a= 10 0.368 2.814 R E + 1.456 R E 2 + 0.768 R E 3 1.292 R E 4 ,
Chl-a = A×[ R 665 1 × R 708 ]+B,
Chl-a =A×[( R 665 1 R 708 1 )× R 753 ]+B,
Quasi-SNR(λ)= R_orig(λ) 1 1000 i=1 1000 ( R_noise (λ) i R_orig(λ) ) 2 ,
Norm_RMSE_Chl= 1 1000 i=1 1000 ( Chl_ noise i Chl_orig ) 2 Chl_orig
Norm_RMSE_TSS= 1 1000 i=1 1000 ( TSS_ noise i TSS_orig ) 2 TSS_orig
Norm_RMSE_CDOM= 1 1000 i=1 1000 ( a CDOM (440)_ noise i a CDOM (440)_orig ) 2 a CDOM (440)_orig ,

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