Abstract

Using a modified geostatistical technique, empirical variograms were constructed from the first derivative of several diverse Remote Sensing Reflectance and Phytoplankton Absorbance spectra to describe how data points are correlated with “distance” across the spectra. The maximum rate of information gain is measured as a function of the kurtosis associated with the Gaussian structure of the output, and is determined for discrete segments of spectra obtained from a variety of water types (turbid river filaments, coastal waters, shelf waters, a dense Microcystis bloom, and oligotrophic waters), as well as individual and mixed phytoplankton functional types (PFTs; diatoms, eustigmatophytes, cyanobacteria, coccolithophores). Results show that a continuous spectrum of 5 to 7 nm spectral resolution is optimal to resolve the variability across mixed reflectance and absorbance spectra. In addition, the impact of uncertainty on subsequent derivative analysis is assessed, showing that a 3% Gaussian noise (SNR ~66) addition compromises data quality without smoothing the spectrum, and a 13% noise (SNR ~15) addition compromises data with smoothing.

© 2017 Optical Society of America

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References

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    [Crossref]

2016 (1)

A. Wolanin, M. A. Soppa, and A. Bracher, “Investigation of spectral band requirements for improving retrievals of phytoplankton functional types,” Remote Sens. 8(10), 871 (2016).
[Crossref]

2015 (7)

A. R. Neeley, S. A. Freeman, and L. A. Harris, “Multi-method approach to quantify uncertainties in the measurements of light absorption by particles,” Opt. Express 23(24), 31043–31058 (2015).
[Crossref] [PubMed]

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[Crossref]

D. R. Thompson, B. C. Gao, R. O. Green, D. A. Roberts, P. E. Dennison, and S. R. Lundeen, “Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign,” Remote Sens. Environ. 167, 64–77 (2015).
[Crossref]

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

D. Stramski, R. A. Reynolds, S. Kaczmarek, J. Uitz, and G. Zheng, “Correction of pathlength amplification in the filter-pad technique for measurements of particulate absorption coefficient in the visible spectral region,” Appl. Opt. 54(22), 6763–6782 (2015).
[Crossref] [PubMed]

H. Xi, M. Hieronymi, R. Rottgers, H. Krasemann, and Z. Qiu, “Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra,” Remote Sens. 7(11), 14781–14805 (2015).
[Crossref]

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

2014 (1)

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

2013 (2)

D. Aurin, A. Mannino, and B. Franz, “Spatially resolving ocean color and sediment dispersion in river plumes, coastal systems, and continental shelf waters,” Remote Sens. Environ. 137, 212–225 (2013).
[Crossref]

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

2012 (2)

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

Z. Lee, C. Hu, R. Arnone, and Z. Liu, “Impact of sub-pixel variations on ocean color remote sensing products,” Opt. Express 20(19), 20844–20854 (2012).
[Crossref] [PubMed]

2011 (1)

E. Torrecilla, D. Stramski, R. A. Reynolds, E. Millán-Núñez, and J. Piera, “Cluster analysis of hyperspectral optical data for discriminating phytoplankton pigment assemblages in the open ocean,” Remote Sens. Environ. 115(10), 2578–2593 (2011).
[Crossref]

2009 (1)

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

2007 (3)

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Z. Lee, K. Carder, R. Arnone, and M. He, “Determination of primary spectral bands for remote sensing of aquatic environments,” Sensors (Basel) 7(12), 3428–3441 (2007).
[Crossref]

C. O. Davis, M. Kavanaugh, R. Letelier, W. P. Bissett, and D. Kohler, “Spatial and spectral resolution considerations for imaging coastal waters,” Proc. SPIE 6680, 66800P (2007).
[Crossref]

2006 (1)

2004 (1)

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

2003 (1)

E. J. Hochberg and M. J. Atkinson, “Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra,” Remote Sens. Environ. 85(2), 174–189 (2003).
[Crossref]

2002 (1)

1999 (2)

D. L. Roelke, C. D. Kennedy, and A. D. Weidemann, “Use of discriminant and fourth-derivative analyses with high resolution absorption spectra for phytoplankton research: limitations at varied signal-to-noise ratio and spectral resolution,” Gulf Mex. Sci. 2, 75–86 (1999).

C. D. Mobley, “Estimation of the remote-sensing reflectance from above-surface measurements,” Appl. Opt. 38(36), 7442–7455 (1999).
[Crossref] [PubMed]

1998 (1)

F. Tsai and W. Philpot, “Derivative analysis of hyperspectral data,” Remote Sens. Environ. 66(1), 41–51 (1998).
[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]

1991 (1)

N. Hoepffner and S. Sathyendranath, “Effect of pigment composition on absorption properties of phytoplankton,” Mar. Ecol. Prog. Ser. 73, 11–23 (1991).
[Crossref]

1985 (1)

M. N. Kishino, N. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).

1963 (1)

G. Matheron, “Principles of geostatistics,” Econ. Geol. 58(8), 1246–1266 (1963).
[Crossref]

Abuhassan, N.

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

Ahmad, Z.

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

Altenburg-Soppa, M.

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

Arnone, R.

Z. Lee, C. Hu, R. Arnone, and Z. Liu, “Impact of sub-pixel variations on ocean color remote sensing products,” Opt. Express 20(19), 20844–20854 (2012).
[Crossref] [PubMed]

Z. Lee, K. Carder, R. Arnone, and M. He, “Determination of primary spectral bands for remote sensing of aquatic environments,” Sensors (Basel) 7(12), 3428–3441 (2007).
[Crossref]

Arnone, R. A.

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Atkinson, M. J.

E. J. Hochberg and M. J. Atkinson, “Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra,” Remote Sens. Environ. 85(2), 174–189 (2003).
[Crossref]

Aurin, D.

D. Aurin, A. Mannino, and B. Franz, “Spatially resolving ocean color and sediment dispersion in river plumes, coastal systems, and continental shelf waters,” Remote Sens. Environ. 137, 212–225 (2013).
[Crossref]

Babin, M.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Bissett, W. P.

C. O. Davis, M. Kavanaugh, R. Letelier, W. P. Bissett, and D. Kohler, “Spatial and spectral resolution considerations for imaging coastal waters,” Proc. SPIE 6680, 66800P (2007).
[Crossref]

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Bracher, A.

A. Wolanin, M. A. Soppa, and A. Bracher, “Investigation of spectral band requirements for improving retrievals of phytoplankton functional types,” Remote Sens. 8(10), 871 (2016).
[Crossref]

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

Brando, V. E.

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

Bresciani, M.

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

Burrows, J. P.

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

Carder, K.

Z. Lee, K. Carder, R. Arnone, and M. He, “Determination of primary spectral bands for remote sensing of aquatic environments,” Sensors (Basel) 7(12), 3428–3441 (2007).
[Crossref]

Carder, K. L.

Cede, A.

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

Craig, S. E.

Davis, C. O.

C. O. Davis, M. Kavanaugh, R. Letelier, W. P. Bissett, and D. Kohler, “Spatial and spectral resolution considerations for imaging coastal waters,” Proc. SPIE 6680, 66800P (2007).
[Crossref]

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Dekker, A. G.

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

Dennison, P. E.

D. R. Thompson, B. C. Gao, R. O. Green, D. A. Roberts, P. E. Dennison, and S. R. Lundeen, “Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign,” Remote Sens. Environ. 167, 64–77 (2015).
[Crossref]

Devred, E.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Dickey, T. D.

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Dinter, T.

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

Dye, D.

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Forget, M.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Franz, B.

D. Aurin, A. Mannino, and B. Franz, “Spatially resolving ocean color and sediment dispersion in river plumes, coastal systems, and continental shelf waters,” Remote Sens. Environ. 137, 212–225 (2013).
[Crossref]

Freeman, S. A.

Gao, B. C.

D. R. Thompson, B. C. Gao, R. O. Green, D. A. Roberts, P. E. Dennison, and S. R. Lundeen, “Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign,” Remote Sens. Environ. 167, 64–77 (2015).
[Crossref]

Giardino, C.

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

Gitelson, A. A.

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]

Gould, R. W.

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Green, R. O.

D. R. Thompson, B. C. Gao, R. O. Green, D. A. Roberts, P. E. Dennison, and S. R. Lundeen, “Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign,” Remote Sens. Environ. 167, 64–77 (2015).
[Crossref]

Harris, L. A.

He, M.

Z. Lee, K. Carder, R. Arnone, and M. He, “Determination of primary spectral bands for remote sensing of aquatic environments,” Sensors (Basel) 7(12), 3428–3441 (2007).
[Crossref]

Herman, J. R.

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

Hestir, E. L.

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

Hieronymi, M.

H. Xi, M. Hieronymi, R. Rottgers, H. Krasemann, and Z. Qiu, “Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra,” Remote Sens. 7(11), 14781–14805 (2015).
[Crossref]

Hirawake, T.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Hochberg, E. J.

E. J. Hochberg and M. J. Atkinson, “Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra,” Remote Sens. Environ. 85(2), 174–189 (2003).
[Crossref]

Hoepffner, N.

N. Hoepffner and S. Sathyendranath, “Effect of pigment composition on absorption properties of phytoplankton,” Mar. Ecol. Prog. Ser. 73, 11–23 (1991).
[Crossref]

Hu, C.

Ichimura, S.

M. N. Kishino, N. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).

Imai, I.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Isada, T.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Jo, Y. H.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Kaczmarek, S.

Kavanaugh, M.

C. O. Davis, M. Kavanaugh, R. Letelier, W. P. Bissett, and D. Kohler, “Spatial and spectral resolution considerations for imaging coastal waters,” Proc. SPIE 6680, 66800P (2007).
[Crossref]

Kennedy, C. D.

D. L. Roelke, C. D. Kennedy, and A. D. Weidemann, “Use of discriminant and fourth-derivative analyses with high resolution absorption spectra for phytoplankton research: limitations at varied signal-to-noise ratio and spectral resolution,” Gulf Mex. Sci. 2, 75–86 (1999).

Kirkpatrick, G. J.

Kishino, M. N.

M. N. Kishino, N. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).

Klemas, V. V.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Kobayashi, T.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Kohler, D.

C. O. Davis, M. Kavanaugh, R. Letelier, W. P. Bissett, and D. Kohler, “Spatial and spectral resolution considerations for imaging coastal waters,” Proc. SPIE 6680, 66800P (2007).
[Crossref]

Kohler, D. D.

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Krasemann, H.

H. Xi, M. Hieronymi, R. Rottgers, H. Krasemann, and Z. Qiu, “Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra,” Remote Sens. 7(11), 14781–14805 (2015).
[Crossref]

Lee, Z.

Letelier, R.

C. O. Davis, M. Kavanaugh, R. Letelier, W. P. Bissett, and D. Kohler, “Spatial and spectral resolution considerations for imaging coastal waters,” Proc. SPIE 6680, 66800P (2007).
[Crossref]

Liu, Y.-G.

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Liu, Z.

Lohrenz, S. E.

Loughner, C. P.

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

Lundeen, S. R.

D. R. Thompson, B. C. Gao, R. O. Green, D. A. Roberts, P. E. Dennison, and S. R. Lundeen, “Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign,” Remote Sens. Environ. 167, 64–77 (2015).
[Crossref]

Mahoney, K. L.

Mannino, A.

D. Aurin, A. Mannino, and B. Franz, “Spatially resolving ocean color and sediment dispersion in river plumes, coastal systems, and continental shelf waters,” Remote Sens. Environ. 137, 212–225 (2013).
[Crossref]

Matheron, G.

G. Matheron, “Principles of geostatistics,” Econ. Geol. 58(8), 1246–1266 (1963).
[Crossref]

Matta, E.

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

Millán-Núñez, E.

E. Torrecilla, D. Stramski, R. A. Reynolds, E. Millán-Núñez, and J. Piera, “Cluster analysis of hyperspectral optical data for discriminating phytoplankton pigment assemblages in the open ocean,” Remote Sens. Environ. 115(10), 2578–2593 (2011).
[Crossref]

Mobley, C. D.

Moisan, T.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Moses, W.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Natsuike, M.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Neeley, A. R.

Noël, S.

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

Nosaka, Y.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Okami, N.

M. N. Kishino, N. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).

Peeken, I.

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

Philpot, W.

F. Tsai and W. Philpot, “Derivative analysis of hyperspectral data,” Remote Sens. Environ. 66(1), 41–51 (1998).
[Crossref]

Piera, J.

E. Torrecilla, D. Stramski, R. A. Reynolds, E. Millán-Núñez, and J. Piera, “Cluster analysis of hyperspectral optical data for discriminating phytoplankton pigment assemblages in the open ocean,” Remote Sens. Environ. 115(10), 2578–2593 (2011).
[Crossref]

Qiu, Z.

H. Xi, M. Hieronymi, R. Rottgers, H. Krasemann, and Z. Qiu, “Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra,” Remote Sens. 7(11), 14781–14805 (2015).
[Crossref]

Reynolds, R. A.

D. Stramski, R. A. Reynolds, S. Kaczmarek, J. Uitz, and G. Zheng, “Correction of pathlength amplification in the filter-pad technique for measurements of particulate absorption coefficient in the visible spectral region,” Appl. Opt. 54(22), 6763–6782 (2015).
[Crossref] [PubMed]

E. Torrecilla, D. Stramski, R. A. Reynolds, E. Millán-Núñez, and J. Piera, “Cluster analysis of hyperspectral optical data for discriminating phytoplankton pigment assemblages in the open ocean,” Remote Sens. Environ. 115(10), 2578–2593 (2011).
[Crossref]

Roberts, D. A.

D. R. Thompson, B. C. Gao, R. O. Green, D. A. Roberts, P. E. Dennison, and S. R. Lundeen, “Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign,” Remote Sens. Environ. 167, 64–77 (2015).
[Crossref]

Roelke, D. L.

D. L. Roelke, C. D. Kennedy, and A. D. Weidemann, “Use of discriminant and fourth-derivative analyses with high resolution absorption spectra for phytoplankton research: limitations at varied signal-to-noise ratio and spectral resolution,” Gulf Mex. Sci. 2, 75–86 (1999).

Rottgers, R.

H. Xi, M. Hieronymi, R. Rottgers, H. Krasemann, and Z. Qiu, “Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra,” Remote Sens. 7(11), 14781–14805 (2015).
[Crossref]

Röttgers, R.

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

Rozanov, V. V.

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

Sadeghi, A.

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

Saitoh, S.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Sathyendranath, S.

N. Hoepffner and S. Sathyendranath, “Effect of pigment composition on absorption properties of phytoplankton,” Mar. Ecol. Prog. Ser. 73, 11–23 (1991).
[Crossref]

Schofield, O. M.

Soppa, M. A.

A. Wolanin, M. A. Soppa, and A. Bracher, “Investigation of spectral band requirements for improving retrievals of phytoplankton functional types,” Remote Sens. 8(10), 871 (2016).
[Crossref]

Steward, R. G.

Stramski, D.

D. Stramski, R. A. Reynolds, S. Kaczmarek, J. Uitz, and G. Zheng, “Correction of pathlength amplification in the filter-pad technique for measurements of particulate absorption coefficient in the visible spectral region,” Appl. Opt. 54(22), 6763–6782 (2015).
[Crossref] [PubMed]

E. Torrecilla, D. Stramski, R. A. Reynolds, E. Millán-Núñez, and J. Piera, “Cluster analysis of hyperspectral optical data for discriminating phytoplankton pigment assemblages in the open ocean,” Remote Sens. Environ. 115(10), 2578–2593 (2011).
[Crossref]

Suzuki, K.

T. Isada, T. Hirawake, T. Kobayashi, Y. Nosaka, M. Natsuike, I. Imai, K. Suzuki, and S. Saitoh, “Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms,” Remote Sens. Environ. 159, 134–151 (2015).
[Crossref]

Takahashi, N.

M. N. Kishino, N. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).

Taylor, B. B.

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

Thompson, D. R.

D. R. Thompson, B. C. Gao, R. O. Green, D. A. Roberts, P. E. Dennison, and S. R. Lundeen, “Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign,” Remote Sens. Environ. 167, 64–77 (2015).
[Crossref]

Toro-Farmer, M. G.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Torrecilla, E.

E. Torrecilla, D. Stramski, R. A. Reynolds, E. Millán-Núñez, and J. Piera, “Cluster analysis of hyperspectral optical data for discriminating phytoplankton pigment assemblages in the open ocean,” Remote Sens. Environ. 115(10), 2578–2593 (2011).
[Crossref]

Tsai, F.

F. Tsai and W. Philpot, “Derivative analysis of hyperspectral data,” Remote Sens. Environ. 66(1), 41–51 (1998).
[Crossref]

Turpie, K. R.

E. Devred, K. R. Turpie, W. Moses, V. V. Klemas, T. Moisan, M. Babin, M. G. Toro-Farmer, M. Forget, and Y. H. Jo, “Future retrievals of water column bio-optical properties using the Hyperspectral Infrared Imager (HyspIRI),” Remote Sens. 5(12), 6812–6837 (2013).
[Crossref]

Tzortziou, M.

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

Uitz, J.

Villa, P.

E. L. Hestir, V. E. Brando, M. Bresciani, C. Giardino, E. Matta, P. Villa, and A. G. Dekker, “Measuring freshwater aquatic ecosystems: the need for a hyperspectral global mapping satellite mission,” Remote Sens. Environ. 167, 181–195 (2015).
[Crossref]

Vountas, M.

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

Wang, L.

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Weidemann, A. D.

D. L. Roelke, C. D. Kennedy, and A. D. Weidemann, “Use of discriminant and fourth-derivative analyses with high resolution absorption spectra for phytoplankton research: limitations at varied signal-to-noise ratio and spectral resolution,” Gulf Mex. Sci. 2, 75–86 (1999).

Wolanin, A.

A. Wolanin, M. A. Soppa, and A. Bracher, “Investigation of spectral band requirements for improving retrievals of phytoplankton functional types,” Remote Sens. 8(10), 871 (2016).
[Crossref]

A. Wolanin, V. V. Rozanov, T. Dinter, S. Noël, M. Vountas, J. P. Burrows, and A. Bracher, “Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results,” Remote Sens. Environ. 166, 243–261 (2015).
[Crossref]

Xi, H.

H. Xi, M. Hieronymi, R. Rottgers, H. Krasemann, and Z. Qiu, “Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra,” Remote Sens. 7(11), 14781–14805 (2015).
[Crossref]

Xing, X.-G.

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Xiu, P.

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Yang, J.-H.

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Zhao, D.-Z.

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Zheng, G.

Appl. Opt. (4)

Biogeosciences (1)

A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009).
[Crossref]

Bull. Mar. Sci. (1)

M. N. Kishino, N. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).

Econ. Geol. (1)

G. Matheron, “Principles of geostatistics,” Econ. Geol. 58(8), 1246–1266 (1963).
[Crossref]

Gulf Mex. Sci. (1)

D. L. Roelke, C. D. Kennedy, and A. D. Weidemann, “Use of discriminant and fourth-derivative analyses with high resolution absorption spectra for phytoplankton research: limitations at varied signal-to-noise ratio and spectral resolution,” Gulf Mex. Sci. 2, 75–86 (1999).

Int. J. Remote Sens. (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]

J. Geophys. Res. Oceans (1)

M. Tzortziou, J. R. Herman, Z. Ahmad, C. P. Loughner, N. Abuhassan, and A. Cede, “Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions,” J. Geophys. Res. Oceans 119(6), 3834–3854 (2014).
[Crossref]

Mar. Ecol. Prog. Ser. (1)

N. Hoepffner and S. Sathyendranath, “Effect of pigment composition on absorption properties of phytoplankton,” Mar. Ecol. Prog. Ser. 73, 11–23 (1991).
[Crossref]

Ocean Sci. (1)

A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher, “Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data,” Ocean Sci. 8(6), 1055–1070 (2012).
[Crossref]

Ocean Sci. J. (1)

X.-G. Xing, D.-Z. Zhao, Y.-G. Liu, J.-H. Yang, P. Xiu, and L. Wang, “An overview of remote sensing of chlorophyll fluorescence,” Ocean Sci. J. 42(1), 49–59 (2007).
[Crossref]

Oceanography (Wash. D.C.) (1)

W. P. Bissett, R. A. Arnone, C. O. Davis, T. D. Dickey, D. Dye, D. D. Kohler, and R. W. Gould, “From meters to kilometers,” Oceanography (Wash. D.C.) 17(2), 32–43 (2004).
[Crossref]

Opt. Express (2)

Proc. SPIE (1)

C. O. Davis, M. Kavanaugh, R. Letelier, W. P. Bissett, and D. Kohler, “Spatial and spectral resolution considerations for imaging coastal waters,” Proc. SPIE 6680, 66800P (2007).
[Crossref]

Remote Sens. (3)

H. Xi, M. Hieronymi, R. Rottgers, H. Krasemann, and Z. Qiu, “Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra,” Remote Sens. 7(11), 14781–14805 (2015).
[Crossref]

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Figures (6)

Fig. 1
Fig. 1 Locations of above-water remote sensing reflectance measurements collected from an ASD radiometer. The corresponding range of HPLC-derived chlorophyll-a concentrations at each station are displayed in the legend, showing a diverse range of water types that were sampled.
Fig. 2
Fig. 2 (A) Empirical variogram performed on the first derivative of spectral phytoplankton absorbance data. Note the relative “flattening” of the tail at the origin of the Gaussian curve. This feature is exploited to gain the maximal rate of information gain, as seen in (B). This distance, or spectral resolution, at which dγ(h)/dh is maximized is interpreted as the optimal spectral sampling frequency.
Fig. 3
Fig. 3 The first derivative of empirical variograms run on different resolutions of data from one phytoplankton absorption sample across the entire integrated spectrum (342–750 nm). The theoretical variogram (red line) helps resolve variability in presence of noise, yielding near-identical results between resolutions.
Fig. 4
Fig. 4 (A) Continuous 1 nm resolution spectra of four species of phytoplankton used in this analysis, Thalassiosira weissflogii (THAL), Synechococcus sp. (SYNEC), Nannochloropsis sp. (NANO), Emiliana huxleyi (EHUX), representing a diverse collection of pigment expressions. (B) The corresponding dγ(h)/dx max in 25 nm increments, indicating the optimal spectral resolution at which the most information is obtained within each increment. Results show most features are resolved between 6 and 15 nm spectral resolution. (C) Phytoplankton absorption from natural assemblages collected from the Korean Strait, highlighting the areas with the highest frequency of absorptions peaks/inflections. Within these highlighted ranges, the optimal spectral frequency is around 6.5-7.0 nm.
Fig. 5
Fig. 5 Continuous 1 nm spectra and corresponding bar graphs (below) indicating the optimal spectral sampling frequency (dγ(h)/dh maximum; bars) as well as the percent resolved variance (dots + line) at given band centers for varying water types. The blue boxes highlight regions that are optimally resolved at 5 nm or less. Water types: (A) One Microcystis bloom spectrum collected from Lake Erie, MI., (B) Four spectra from the turbid river plume waters of the Gulf of Mexico, (C) Four spectra from green coastal waters in the Gulf of Mexico and U.S. East Coast, (D) Four spectra from the shelf waters in the Gulf of Mexico and U.S. East Coast, (E) Four spectra from the open blue waters of the Gulf Stream along the U.S. East Coast, and (F) Four spectra from clear oligotrophic waters in the Bahamas.
Fig. 6
Fig. 6 (A) A single differential normalized reflectance spectrum (green line) taken from shelf waters of the Gulf of Mexico [Fig. 5(d)], shows aberrations after adding 3% Gaussian noise, with and without smoothing (black and red lines, respectively). Below, the ratio of γ(5 nm) to γ(10 nm) at incremental Gaussian noise additions is calculated for all spectra, indicating the threshold at which the variability of first derivative reflectance data at 5 nm spectral resolution meets or exceeds the variability of first derivative reflectance data at 10 nm spectral resolution, e.g. γ(5 nm)/γ(10 nm) = 1. (B) Without any spectral smoothing, the noise threshold is at ~3%. (C) With a Savitsky-Golay spectral smoothing function, this threshold is extended to 13%.

Equations (3)

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R RS = S sfc ( λ )ρ S sky ( λ ) π S g ( λ )/ R g ( λ )
γ( h )= 1 2| N( h ) | N( h ) ( z i z j ) 2
g( h )= c n + σ n 2 ( 1exp( h 2 a 2 ) )

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