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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 2 — Jan. 16, 2012
  • pp: 1070–1083
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Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton groups detection in case 1 waters

S. Alvain, H. Loisel, and D. Dessailly  »View Author Affiliations


Optics Express, Vol. 20, Issue 2, pp. 1070-1083 (2012)
http://dx.doi.org/10.1364/OE.20.001070


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Abstract

Past years have seen the development of different approaches to detect phytoplankton groups from space. One of these methods, the PHYSAT one, is empirically based on reflectance anomalies. Despite observations in good agreement with in situ measurements, the underlying theoretical explanation of the method is still missing and needed by the ocean color community as it prevents improvements of the methods and characterization of uncertainties on the inversed products. In this study, radiative transfer simulations are used in addition to in situ measurements to understand the organization of the signals used in PHYSAT. Sensitivity analyses are performed to assess the impact of the variability of the following three parameters on the reflectance anomalies: specific phytoplankton absorption, colored dissolved organic matter absorption, and particles backscattering. While the later parameter explains the largest part of the anomalies variability, results show that each group is generally associated with a specific bio-optical environment which should be considered to improve methods of phytoplankton groups detection.

© 2012 OSA

1. Introduction

For a given chlorophyll a concentration (Chl a), phytoplankton groups scatter and absorb light differently according to their pigments composition, shape and size. However, the first order signal retrieved from ocean color sensors in open oceans, the normalized water leaving radiance (nLw), is due to Chl a [1

1. H. R. Gordon and A. Morel, Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. A Review (Springer-Verlag, New York, USA), 1983).

,2

2. A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water),” J. Geophys. Res. 93(C9), 10,749–10,768 (1988). [CrossRef]

]) and cannot be easily used to extract information about phytoplankton groups present in the oceanic surface layer. To circumvent this difficulty, different approaches have been developed in the past few years. When changes in nLw are significant enough between phytoplankton group, they can be detected from their specific radiances measurements [3

3. S. Sathyendranath, L. Watts, E. Devred, T. Platt, C. Caverhill, and H. Maass, “Discrimination of diatoms from other phytoplankton using ocean-colour data,” Mar. Ecol. Prog. Ser. 272, 59–68 (2004). [CrossRef]

,4

4. A. Ciotti and A. Bricaud, “Retrievals of a size parameter for phytoplankton and spectral light absorption by Colored Detrital Matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil,” Limnol. Oceanogr. Methods 4, 237–253 (2006). [CrossRef]

]. When reflectance changes are not significant enough to separate one group from another one, empirical or semi-empirical methods have to be developed. This last case is particularly relevant when the objective is to detect groups defined from a biogeochemical or size point of view at global scale [5

5. J. Aiken, Y. Pradhan, R. Barlow, S. Lavender, A. Poulton, P. Holligan, and N. J. Hardman-Mountford, ““Phytoplankton pigments and functional types in the Atlantic Ocean: a decadal assessment, 1995-2005,” Deep Sea Res. Part II Top. Stud. Oceanogr. 56(15), 899–917 (2009). [CrossRef]

10

10. R. J. Brewin, S. Sathyendranath, T. Hirata, S. Lavender, R. M. Barciela, and N. J. Hardman-Mountford, “A three-component model of phytoplankton size class for the Atlantic Ocean,” Ecol. Modell. 221(11), 1472–1483 (2010). [CrossRef]

]. Note that ‘phytoplankton groups’ are defined here following the definition based on functional types, as detailed in a previously published article [6

6. S. Alvain, C. Moulin, Y. Dandonneau, and F. M. Breon, “Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery,” Deep Sea Res. Part I Oceanogr. Res. Pap. 52(11), 1989–2004 (2005). [CrossRef]

].

In this study, we will focus on the PHYSAT method (http://log.univ-littoral.fr/Physat) which allows the detection of dominant phytoplankton groups [6

6. S. Alvain, C. Moulin, Y. Dandonneau, and F. M. Breon, “Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery,” Deep Sea Res. Part I Oceanogr. Res. Pap. 52(11), 1989–2004 (2005). [CrossRef]

]. This approach is based on the analysis of the second order variation in nLw measurements after removal of the impact of chlorophyll a variation. Thus, PHYSAT is based on the reflectance anomalies, Ra(λ), computed as follows:
Ra(λ)=nLw(λ)nLwref(λ,Chla)
(1)
where nLwref (λ,Chl a) is calculated for discrete bins of chlorophyll a concentration and from remote sensed nLw measurements [6

6. S. Alvain, C. Moulin, Y. Dandonneau, and F. M. Breon, “Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery,” Deep Sea Res. Part I Oceanogr. Res. Pap. 52(11), 1989–2004 (2005). [CrossRef]

]. Briefly, nLwref is calculated from nLw data and the associated Chl a computed from the OC4v4 algorithm [11

11. 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. 103(C11), 24,937–24,953 (1998). [CrossRef]

] within the following Chl a range: 0.02-3 mg.m−3 with an increment of 0.1 mg.m−3. This reference can then be used to remove the first order effect of chlorophyll a on nLw(λ) measurements. The second order variation is then represented by a new parameter, named Ra(λ) which, by definition, is independent of the Chl a level (being by extension independent of the biomass). Ra(λ) is an adimensional unit parameter.

2. Theoretical and computational considerations

The main objective of the numerical simulations is to test the sensitivity of the PHYSAT method, through the variability of Ra(λ), to the variability of inherent optical properties in case 1 water. We specifically focus on the effect of phytoplankton absorption, aphy, particulate backscattering coefficient, bbp, and absorption by colored dissolved organic matter, acdom (all these IOP are in m−1). For that purpose, different sets of simulations have been performed.

2.1. Mean theoretical reference relationships

A first set of simulations (S1) (Fig. 1
Fig. 1 Schematic view of steps followed to achieve theoretical analysis of ocean color radiances anomalies sensibility.
step 2) is dedicated to the generation of a theoretical nLwref-theo used to generate theoretical radiance anomalies (Ratheo). This nLwref-theo should match the nLwref obtained using the OC4V4 algorithm which is used in PHYSAT. Retrieval of Chl a from band ratio is affected by the natural variability of IOP’s [18

18. H. Loisel, B. Lubac, D. Dessailly, L. Duforet-Gaurier, and V. Vantrepotte, “Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach,” Opt. Express 18(20), 20949–20959 (2010). [CrossRef] [PubMed]

]. Mean IOP vs. Chl relationships have been fixed (Fig. 1 step 1) and numerical simulations have been performed to calculate a set of theoretical radiances corresponding to Chl a values ranging from 0.02 to 3 mg.m−3 (range of the PHYSAT validity).

The radiative transfer equation is solved by the invariant embedding method using the Hydrolight 5.0 code [19

19. C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters (Academic, San Diego, Calif., 1994).

]. All numerical simulations are carried out for a homogeneous and infinitely deep ocean. The air-sea interface is modeled following Cox and Munk [20

20. C. Cox and W. Munk, “Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter,” J. Opt. Soc. Am. 44(11), 838–850 (1954). [CrossRef]

] with a fixed wind speed of 5 m.s−1. A standard clear atmosphere (with a visibility of 15 km) with a sun zenith angle at 30° is adopted. Raman scattering is taken into account in the simulations, whereas other inelastic processes (i.e. Chl a and CDOM fluorescence) are omitted. The molecular scattering phase function is calculated using theoretical consideration [21

21. C. D. Mobley, B. Gentili, H. R. Gordon, Z. Jin, G. W. Kattawar, A. Morel, P. Reinersman, K. Stamnes, and R. H. Stavn, “Comparison of numerical models for computing underwater light fields,” Appl. Opt. 32(36), 7484–7504 (1993). [CrossRef] [PubMed]

]. The particle phase function is derived from the formulation proposed by Mobley et al. [21

21. C. D. Mobley, B. Gentili, H. R. Gordon, Z. Jin, G. W. Kattawar, A. Morel, P. Reinersman, K. Stamnes, and R. H. Stavn, “Comparison of numerical models for computing underwater light fields,” Appl. Opt. 32(36), 7484–7504 (1993). [CrossRef] [PubMed]

], based on measurements by Morel and Maritorena [22

22. A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. 106(C4), 7163–7180 (2001). [CrossRef]

]. Pure sea water absorption and scattering coefficients were taken from Pope and Fry [23

23. R. M. Pope and E. S. Fry, “Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements,” Appl. Opt. 36(33), 8710–8723 (1997). [CrossRef] [PubMed]

] and Smith and Baker [24

24. 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]

], respectively.

For a given wavelength the phytoplankton absorption coefficient, aphy(λ), is modeled as a function of Chl a as follows:

aphy(λ)=A[Chla][E]
(2)

A(λ) and E(λ) are the coefficients calculated by Bricaud et al., 98 [25

25. A. Bricaud, A. Morel, M. Babin, K. Allali, and H. Claustre, “Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio-optical models,” J. Geophys. Res. 103(C13), 31033–31044 (1998). [CrossRef]

] using a large data set of in situ measurements collected in oligotrophic, mesotrophic, and eutrophic oceanic waters. Absorption by colored dissolved organic matter is described according to [26

26. A. Bricaud, A. Morel, and L. Prieur, “Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains,” Limnol. Oceanogr. 26(1), 43–53 (1981). [CrossRef]

, 27

27. A. Morel, “Are the empirical relationships describing the bio-optical properties of case 1 waters consistent and internally compatible?” J. Geophys. Res. 114(C1), C01016 (2009). [CrossRef]

]:

acdom(λ)=0.065[Chla]0.75exp(0.014(λ443))
(3)

Note that absorption by detrital particles is not explicitly taken into account as it is characterized by a spectral shape similar to that of acdom, and it only represents on average 10% of acdom [28

28. D. A. Siegel, S. Maritorena, N. B. Nelson, and D. A. Hansell, “Global distribution and dynamics of colored dissolved and detrital organic materials,” J. Geophys. Res. 107(C12), 3228 (2002). [CrossRef]

].

γ=0.55log[Chla]+1.6
(5)

2.2. Set of nLwtheo for various IOPs values

A second set of simulations (S2) is used to compute nLwtheo values for different IOP conditions (Fig. 1 step 4). In practice, two specific IOP are alternatively fixed while the third one is made variable as follows: for each IOP, its mean specific value (used to compute the S1 set) is multiplied by the following factors: 0.5 / 0.7 / 0.9 / 1.1 / 1.3 / 1.5 / 1.7 and 1.9. These factors allow to cover the specific IOP range of variability generally encountered in natural waters [34

34. R. D. Vaillancourt, C. Brown, R. L. Guillard, and W. M. Balch, “Light backscattering properties of marine phytoplankton: relationships to cell size, chemical composition and taxonomy,” J. Plankton Res. 26(2), 191–212 (2004). [CrossRef]

36

36. H. Loisel and D. Stramski, “Estimation of the inherent optical properties of natural waters from irradiance attenuation coefficient and reflectance in the presence of Raman scattering,” Appl. Opt. 39(18), 3001–3011 (2000). [CrossRef]

]. For each case, a simulation is made to generate the corresponding nLwtheo spectral values. These S2 simulations are performed for three different Chl a concentrations: 0.2, 1 and 2 mg.m−3.

2.3. Phytoplankton groups and their associated bio-optical environment

A second focus of this study is to determine which set of IOP (and their given variability) better explains the organization in term of magnitude of the spectral radiance anomaly (i.e Ra(λ)) empirically determined for PHYSAT specific groups. To answer this question, it is necessary to define mean realistic ranges of IOP variability associated with the PHYSAT groups and their associated bio-optical environment. To remain as close as possible of PHYSAT conditions we will considered not only phytoplankton groups but phytoplankton groups in their realistic environment. Furthermore, considering the most recent validation of PHYSAT versus field observations (see introduction), this study will focus on the following three groups: diatoms, nanoeucayotes and a third one made by assembling Prochlorococcus and Cyanobacteria in a single group denominated Picoplanktonic cyanobacteria. This allows a theoretical analysis of the main groups associated with clearly different Ra *(λ) spectra in the empirically defined method.

Due to insufficient in situ measurements for the considered IOPs when considering each groups in dominance conditions, mean values associated with the three phytoplankton groups and their associated bio-optical environment have to be defined from the mean relationships established before (Eqs. (2)-(5)) and SeaWiFS sensors daily level 3 estimation of Chl a (9 km resolution) as input parameter (Fig. 1 step 6). In situ pigments inventories from NOMAD, GeP&CO, ICOTA 5 - 7 and OISO campaigns have been used for selecting only remote sensed measurements dominated by a specific group (Fig. 1 step 6). Note that the identification of the groups has been established by using the same biomarkers criteria than those established in the PHYSAT method [4

4. A. Ciotti and A. Bricaud, “Retrievals of a size parameter for phytoplankton and spectral light absorption by Colored Detrital Matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil,” Limnol. Oceanogr. Methods 4, 237–253 (2006). [CrossRef]

]. Eventually, 527 inventories for which simultaneous high quality satellite matchup (aerosol optical thickness at 865 nm lower than 0.15 and Chl a values within the PHYSAT validity range) and in situ pigments inventories dominated by a single group have been considered.

3. Results and discussion

3. 1. Evaluation of the reference relationships

The first set of simulation (S1), based on averaged IOPs vs. Chl a relationships, is dedicated to the establishment of theoretical reference spectra close to those used in PHYSAT. Based on relationships detailed before, mean nLwref-theo(λ) have been computed and can be compared to those used in the PHYSAT algorithm (nLwref-PHYSAT(λ)) (Fig. 2
Fig. 2 (a) nLwref-theo(λ) and (b) nLwref-PHYSAT(λ) normalized radiance spectra for different Chl a values.
and Table 4

Table 4. Minimum and maximum values of the nLwref-theo(λ) and nLwref-PHYSAT(λ) normalized radiance spectra at each SeaWiFS wavelength

table-icon
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).

A good agreement is found between nLwref-theo(λ) and nLwref-PHYSAT(λ) in term of magnitude ranges. In addition, the consistency of the general parameterizations considered in this study (Eqs. (2)-(5)) is also tested through the development of a specific OC-MOD relationship. In practice, it is based on 4th order polynomial fit (to be consistent with OC4v4) between Chl a and the “maximum band ratio” of nLwref-theo(λ):
log(Chla)=alog4(R)+blog3(R)+clog2(R)+dlog(R)+e
(6)
with
r=Max(Rrs(443),Rrs(490),Rrs(510))Rrs(555)
with Rrs the remote sensing reflectance (Rrs(λ)=nLw(λ)/Fo(λ), with Fo the extraterrestrial irradiance).

This theoretical OC-MOD parameterization is compared with the OC4v4 empirical algorithm developed to assess Chl a concentration from the SeaWiFS ocean color sensor [11

11. 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. 103(C11), 24,937–24,953 (1998). [CrossRef]

] and used as nLwref in Eq. (1) (Fig. 3
Fig. 3 Chl a as a function of the “maximum band” of the blue to green ratio r (see Eq. (6)) for the OC4v4 SeaWiFS algorithm (gray line, with a = −1.532,b = 0.649,c = 1.93,d = −3.067,e = 0.366 in Eq. (6)), Hydrolight output (black line, with a = 1.351,b = 0.301,c = −0.09,d = −2.37,e = 0.433 in Eq. (6)), superimposed with NOMAD [38] in situ measurements (light gray).
). Over the PHYSAT Chl a range (0.02 - 3 mg.m−3), the Chl a estimated from the OC-MOD algorithm is on average 17% greater than that derived from the OC4v4 formulation. In the context of this study and according to 1) the observed large scatter in the ‘r vs Chl a’ relationship in the NOMAD database [38

38. P. J. Werdell and S. W. Bailey, “An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation,” Remote Sens. Environ. 98(1), 122–140 (2005). [CrossRef]

] and 2) the overall agreement between the OC4v4 and OC-MOD mean relationships, such difference is considered as negligible (see Fig. 3).

The S1 data set can be used to define a theoretical look up table (LUT) of nLwref-theo for a given λ and Chl a (Fig. 1 step 2 and Fig. 2) similar to that used in PHYSAT. Further, the latter comparisons of absolute nLwref values (Fig. 2, and Table 4) and band ratios with a limited bias compared to in situ dispersion (Fig. 3) indicate that the different parameterizations adopted between the IOPs and Chl a (Eqs. (2)-(5)) are adapted to theoretically examine the impact of different scenarios of IOP variability (the S2 simulations) on the radiance anomalies approach.

3.2. Evaluation of IOPs versus chlorophyll a relationships

3.3. PHYSAT signals response to IOPs variability

The aim of this section is to assess the impact of aphy*(λ) = aphy(λ)/Chl a, acdom(λ) and bp(λ) (or bbp) variability on Ratheo(λ).The Ratheo(λ) values are computed by dividing the nLwtheo(λ) outputs from S2 by previously fixed nLwref-theo(λ) in S1 (Fig. 1 step 5). Figures 5(a), (b) and (c)
Fig. 5 Theoretical radiance anomalies as a function of wavelength in response to IOP (aphy, acdom and bp) variability, for three fixed chlorophyll concentration: 0.2 (blue lines), 1(green lines) and 2(red lines) mg.m−3.
show, for three Chl a concentrations, the Ratheo(λ) spectra obtained when acdom(λ), aphy*(λ), and bbp (λ) vary according to the different cases simulated in S2.

For a given chlorophyll concentration, variations in acdom(λ) induce a large variability in Ratheo(λ), especially in the blue part of the spectrum (Fig. 5a). The Ratheo(λ) spectral shape depends strongly on the acdom(λ) values. For low acdom(λ) values, the Ratheo(λ) spectra decrease from the blue to red wavelengths, while they increase with λ for high acdom(λ) values. The slope of the simulated Ratheo(λ) spectra is thus negative when acdom values are high, or positive when acdom values are low. At a given λ, the absolute value of Ratheo(λ) decreases when acdom(λ) increases. This feature is valid for all Chl a values. However, Ratheo(λ) sensitivity to IOP variation is not exactly the same for all of the Chl a. At 412 nm, when the acdom(412) value is twice or half of its mean value, the Ratheo values range between 0.56 and 1.62, respectively.

3.4. Mean theoretical anomaly (Ratheo(λ)) for each phytoplankton group

We have previously shown that, for a given Chl a concentration, variations in aphy*(λ), acdom(λ) and bp(λ) (i.e. bbp(λ)) lead to various changes in Ratheo(λ). Interestingly, ranges of Ratheo(λ) variations obtained from the latter theoretical simulations (Fig. 5) are of the same magnitude than those observed from SeaWiFS measurements. Indeed, values of Ra(λ) used in PHYSAT range between 0.6 to 1.8 at 412 nm, 0.75 to 1.5 at 443 nm and from 0.8 to 1.5 for longer wavelengths [13

13. S. Alvain, C. Moulin, Y. Dandonneau, and H. Loisel, “Seasonal distribution and succession of dominant phytoplankton groups in the global ocean: A satellite view,” Global Biogeochem. Cycles 22(3), GB3001 (2008). [CrossRef]

]. However, one may question whether these Ra(λ) responses are sufficient to explain the distribution of the anomalies amplitudes observed in PHYSAT.

In a second step, new radiative transfer simulations have been performed using both the mean group specific IOP and chlorophyll a concentration (Fig. 1 step 9). This has been done in order to assess the impact of the whole optical environment of each phytoplankton group (as defined in Table 2) on the Ratheo(λ) variability. The specific aim of this simulation was to tentatively explain the organization of the PHYSAT empirically defined spectra which cannot be explained considering each IOP separately.

The absolute values of the theoretical radiance anomalies for each groups slightly differ from the PHYSAT ones in terms of shape or amplitude. Thus, additional studies, based on large database of in situ IOP and radiometric measurements obtained during blooms corresponding to each phytoplankton group (not currently available for all groups), should be performed in the future to specifically address this issue.

3.5 Implications for PHYSAT in case 1 waters

4. Conclusion and perspectives

The main purpose of this study was to theoretically analyze the bio-optical origin of the spectral shapes and amplitudes variability associated with each phytoplankton group detected by PHYSAT in case 1 waters. Indeed, after a necessary first step of development and validation of the PHYSAT method, a theoretical explanation of the empirical anomalies was strongly needed in order to move forward in the domain of phytoplankton groups detection. Thus, sensitivity analyses of the parameters used in PHYSAT Ra(λ), which varies almost independently of the biomass, were performed in function of IOPs. These analyses show that for a given chlorophyll concentration, the particle scattering variability explains the largest part of the remotely sensed Ra(λ) spectral variability, especially when focusing on Ra(λ) magnitude changes (Fig. 4). However, variations in colored dissolved organic matter and phytoplankton absorption coefficients can also have a large impact on Ra(λ) with specific spectral signatures. Following these sensitivity analyses, specific Ratheo(λ) spectra for bio-optical environment where the phytoplankton assemblage is dominated by diatoms, nanoeucaryotes and Picoplanktonic cyanobacteria have been computed. Diatoms are generally associated with high Ratheo(λ) due to high backscattering. Conversely, picoplankton is associated with mean Ratheo(λ) characterized by a high aphy(λ)* compensated by low acdom(λ) and bbp(λ) values. Nanoeucaryotes are associated with low Ratheo(λ) mainly due to high acdom(λ) and moderate bbp(λ). The magnitude of the theoretically defined anomalies for the three groups is in good agreement with specific anomalies empirically highlighted and used in PHYSAT. Complementary studies, based on large in situ database of IOPs measurements, will be necessary in the future to improve our parameterizations in order to obtain a better agreement between the theoretical and PHYSAT spectral anomalies for the different groups. Unfortunately, such database is not yet available. However, this study provides some essential clues to explain the PHYSAT Ratheo(λ) differences between groups. This study represents a first step toward the theoretical understanding of PHYSAT results. It also opens new doors for improving phytoplankton groups detection. Thus, in a near future, the definition of the validity ranges for each group parameters (IOPs) will be integrated to the PHYSAT algorithm already published, in order to avoid misclassifications. Additional tests will also be processed from in situ and remotely sensed measurements in order to improve our knowledge on the optical conditions allowing the best detection for each group. This also opens new potential development by considering phytoplankton groups and their environmental conditions together.

Acknowledgments

The authors would like to thank all participants and voluntary contributors for collecting data that have been assembled in the NOMAD data set. They also thank organizers and observers of Gep&Co, OISO and ICOTA (and especially Dr A. Goffart) campaigns. The authors would like to thank the NASA SeaWiFS project and the NASA/GSFC/DAAC for the production and distribution of SeaWiFS data. We also thank Dr A. Bricaud for providing her parameters. We are also grateful to our funding sources, the CNRS, the CNES-TOSCA/PHYTOCOT project and the INTERREG IV—2 MERS SEAS ZEEN program. Dr. E. Boss and two reviewers are acknowledged for their relevant comments and suggestions on the manuscript.

References and links

1.

H. R. Gordon and A. Morel, Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. A Review (Springer-Verlag, New York, USA), 1983).

2.

A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water),” J. Geophys. Res. 93(C9), 10,749–10,768 (1988). [CrossRef]

3.

S. Sathyendranath, L. Watts, E. Devred, T. Platt, C. Caverhill, and H. Maass, “Discrimination of diatoms from other phytoplankton using ocean-colour data,” Mar. Ecol. Prog. Ser. 272, 59–68 (2004). [CrossRef]

4.

A. Ciotti and A. Bricaud, “Retrievals of a size parameter for phytoplankton and spectral light absorption by Colored Detrital Matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil,” Limnol. Oceanogr. Methods 4, 237–253 (2006). [CrossRef]

5.

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6.

S. Alvain, C. Moulin, Y. Dandonneau, and F. M. Breon, “Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery,” Deep Sea Res. Part I Oceanogr. Res. Pap. 52(11), 1989–2004 (2005). [CrossRef]

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11.

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J.-C. Marty, J. Chiavérini, M.-D. Pizay, and B. Avril, “Seasonal and interannual dynamics of nutrients and phytoplankton pigments in the western Mediterranean Sea at the DYFAMED time-series station (1991–1999),” Deep Sea Res. Part II Top. Stud. Oceanogr. 49(11), 1965–1985 (2002). [CrossRef]

16.

M. D. DuRand, R. J. Olson, and S. W. Chisholm, “Phytoplankton population dynamics at the Bermuda Atlantic time series station in the Sargasso Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr. 48(8-9), 1983–2003 (2001). [CrossRef]

17.

A. Longhurst, Ecological Geography of the Sea, 2nd ed. (Academic, San Diego, Calif. (2007).

18.

H. Loisel, B. Lubac, D. Dessailly, L. Duforet-Gaurier, and V. Vantrepotte, “Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach,” Opt. Express 18(20), 20949–20959 (2010). [CrossRef] [PubMed]

19.

C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters (Academic, San Diego, Calif., 1994).

20.

C. Cox and W. Munk, “Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter,” J. Opt. Soc. Am. 44(11), 838–850 (1954). [CrossRef]

21.

C. D. Mobley, B. Gentili, H. R. Gordon, Z. Jin, G. W. Kattawar, A. Morel, P. Reinersman, K. Stamnes, and R. H. Stavn, “Comparison of numerical models for computing underwater light fields,” Appl. Opt. 32(36), 7484–7504 (1993). [CrossRef] [PubMed]

22.

A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. 106(C4), 7163–7180 (2001). [CrossRef]

23.

R. M. Pope and E. S. Fry, “Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements,” Appl. Opt. 36(33), 8710–8723 (1997). [CrossRef] [PubMed]

24.

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]

25.

A. Bricaud, A. Morel, M. Babin, K. Allali, and H. Claustre, “Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio-optical models,” J. Geophys. Res. 103(C13), 31033–31044 (1998). [CrossRef]

26.

A. Bricaud, A. Morel, and L. Prieur, “Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains,” Limnol. Oceanogr. 26(1), 43–53 (1981). [CrossRef]

27.

A. Morel, “Are the empirical relationships describing the bio-optical properties of case 1 waters consistent and internally compatible?” J. Geophys. Res. 114(C1), C01016 (2009). [CrossRef]

28.

D. A. Siegel, S. Maritorena, N. B. Nelson, and D. A. Hansell, “Global distribution and dynamics of colored dissolved and detrital organic materials,” J. Geophys. Res. 107(C12), 3228 (2002). [CrossRef]

29.

H. R. Gordon and A. Morel, Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. A Review (Springer-Verlag, New York, 1983).

30.

H. Loisel and A. Morel, “Light scattering and chlorophyll concentration in case 1 waters: a re-examination,” Limnol. Oceanogr. 43(5), 847–858 (1998). [CrossRef]

31.

A. Morel, D. Antoine, and B. Gentili, “Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function,” Appl. Opt. 41(30), 6289–6306 (2002). [CrossRef] [PubMed]

32.

H. Loisel, J.-M. Nicolas, A. Sciandra, D. Stramski, and A. Poteau, “Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean,” J. Geophys. Res. 111(C9), C09024 (2006). [CrossRef] [PubMed]

33.

D. Antoine, D. A. Siegel, T. Kostadinov, S. Maritorena, N. B. Nelson, B. Gentili, V. Vellucci, and N. Guillocheau, “Variability in optical particle backscattering in contrasting bio-optical oceanic regimes,” Limnol. Oceanogr. 56(3), 955–973 (2011). [CrossRef]

34.

R. D. Vaillancourt, C. Brown, R. L. Guillard, and W. M. Balch, “Light backscattering properties of marine phytoplankton: relationships to cell size, chemical composition and taxonomy,” J. Plankton Res. 26(2), 191–212 (2004). [CrossRef]

35.

A. Bricaud, H. Claustre, J. Ras, and K. Oubelkheir, “Natural variability of phytoplankton absorption in oceanic waters: influence of the size structure of algal populations,” J. Geophys. Res. 109(C11), C11010 (2004). [CrossRef]

36.

H. Loisel and D. Stramski, “Estimation of the inherent optical properties of natural waters from irradiance attenuation coefficient and reflectance in the presence of Raman scattering,” Appl. Opt. 39(18), 3001–3011 (2000). [CrossRef]

37.

Y. Huot, A. Morel, M. Twardowski, D. Stramski, and R. A. Reynolds, “Particle optical backscattering along a chlorophyll gradient in the upper layer of the eastern south pacific ocean,” Biogeosciences 5(2), 495–507 (2008). [CrossRef]

38.

P. J. Werdell and S. W. Bailey, “An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation,” Remote Sens. Environ. 98(1), 122–140 (2005). [CrossRef]

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(010.1030) Atmospheric and oceanic optics : Absorption
(010.1350) Atmospheric and oceanic optics : Backscattering
(010.1690) Atmospheric and oceanic optics : Color

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: September 6, 2011
Revised Manuscript: November 6, 2011
Manuscript Accepted: November 10, 2011
Published: January 4, 2012

Virtual Issues
Vol. 7, Iss. 3 Virtual Journal for Biomedical Optics

Citation
S. Alvain, H. Loisel, and D. Dessailly, "Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton groups detection in case 1 waters," Opt. Express 20, 1070-1083 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-2-1070


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References

  1. H. R. Gordon and A. Morel, Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. A Review (Springer-Verlag, New York, USA), 1983).
  2. A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water),” J. Geophys. Res.93(C9), 10,749–10,768 (1988). [CrossRef]
  3. S. Sathyendranath, L. Watts, E. Devred, T. Platt, C. Caverhill, and H. Maass, “Discrimination of diatoms from other phytoplankton using ocean-colour data,” Mar. Ecol. Prog. Ser.272, 59–68 (2004). [CrossRef]
  4. A. Ciotti and A. Bricaud, “Retrievals of a size parameter for phytoplankton and spectral light absorption by Colored Detrital Matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil,” Limnol. Oceanogr. Methods4, 237–253 (2006). [CrossRef]
  5. J. Aiken, Y. Pradhan, R. Barlow, S. Lavender, A. Poulton, P. Holligan, and N. J. Hardman-Mountford, ““Phytoplankton pigments and functional types in the Atlantic Ocean: a decadal assessment, 1995-2005,” Deep Sea Res. Part II Top. Stud. Oceanogr.56(15), 899–917 (2009). [CrossRef]
  6. S. Alvain, C. Moulin, Y. Dandonneau, and F. M. Breon, “Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery,” Deep Sea Res. Part I Oceanogr. Res. Pap.52(11), 1989–2004 (2005). [CrossRef]
  7. J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” J. Geophys. Res.111(C8), C08005 (2006). [CrossRef]
  8. D. E. Raitsos, S. J. Lavender, C. D. Maravelias, J. Haralabous, A. J. Richardson, and P. C. Reid, “Identifying four phytoplankton functional types from space: An ecological approach,” Limnol. Oceanogr.53(2), 605–613 (2008). [CrossRef]
  9. T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Retrieval of the particle size distribution from satellite ocean color observations,” J. Geophys. Res.114(C9), C09015 (2009). [CrossRef]
  10. R. J. Brewin, S. Sathyendranath, T. Hirata, S. Lavender, R. M. Barciela, and N. J. Hardman-Mountford, “A three-component model of phytoplankton size class for the Atlantic Ocean,” Ecol. Modell.221(11), 1472–1483 (2010). [CrossRef]
  11. 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.103(C11), 24,937–24,953 (1998). [CrossRef]
  12. Y. Dandonneau, P. Y. Deschamps, J.-M. Nicolas, H. Loisel, J. Blanchot, Y. Montel, F. Thieuleux, and G. Bécu, “Seasonal and interannual variability of ocean color and composition of phytoplankton communities in the North Atlantic, Equatorial Pacific and South Pacific,” Deep Sea Res. Part II Top. Stud. Oceanogr.51(1-3), 303–318 (2004). [CrossRef]
  13. S. Alvain, C. Moulin, Y. Dandonneau, and H. Loisel, “Seasonal distribution and succession of dominant phytoplankton groups in the global ocean: A satellite view,” Global Biogeochem. Cycles22(3), GB3001 (2008). [CrossRef]
  14. M. V. Zubkov, M. A. Sleigh, P. H. Burkill, and R. J. G. Leakey, “Picoplankton community structure on the Atlantic Meridional Transect: a comparison between seasons,” Prog. Oceanogr.45(3-4), 369–386 (2000). [CrossRef]
  15. J.-C. Marty, J. Chiavérini, M.-D. Pizay, and B. Avril, “Seasonal and interannual dynamics of nutrients and phytoplankton pigments in the western Mediterranean Sea at the DYFAMED time-series station (1991–1999),” Deep Sea Res. Part II Top. Stud. Oceanogr.49(11), 1965–1985 (2002). [CrossRef]
  16. M. D. DuRand, R. J. Olson, and S. W. Chisholm, “Phytoplankton population dynamics at the Bermuda Atlantic time series station in the Sargasso Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr.48(8-9), 1983–2003 (2001). [CrossRef]
  17. A. Longhurst, Ecological Geography of the Sea, 2nd ed. (Academic, San Diego, Calif. (2007).
  18. H. Loisel, B. Lubac, D. Dessailly, L. Duforet-Gaurier, and V. Vantrepotte, “Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach,” Opt. Express18(20), 20949–20959 (2010). [CrossRef] [PubMed]
  19. C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters (Academic, San Diego, Calif., 1994).
  20. C. Cox and W. Munk, “Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter,” J. Opt. Soc. Am.44(11), 838–850 (1954). [CrossRef]
  21. C. D. Mobley, B. Gentili, H. R. Gordon, Z. Jin, G. W. Kattawar, A. Morel, P. Reinersman, K. Stamnes, and R. H. Stavn, “Comparison of numerical models for computing underwater light fields,” Appl. Opt.32(36), 7484–7504 (1993). [CrossRef] [PubMed]
  22. A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters: A reappraisal,” J. Geophys. Res.106(C4), 7163–7180 (2001). [CrossRef]
  23. R. M. Pope and E. S. Fry, “Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements,” Appl. Opt.36(33), 8710–8723 (1997). [CrossRef] [PubMed]
  24. 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]
  25. A. Bricaud, A. Morel, M. Babin, K. Allali, and H. Claustre, “Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio-optical models,” J. Geophys. Res.103(C13), 31033–31044 (1998). [CrossRef]
  26. A. Bricaud, A. Morel, and L. Prieur, “Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains,” Limnol. Oceanogr.26(1), 43–53 (1981). [CrossRef]
  27. A. Morel, “Are the empirical relationships describing the bio-optical properties of case 1 waters consistent and internally compatible?” J. Geophys. Res.114(C1), C01016 (2009). [CrossRef]
  28. D. A. Siegel, S. Maritorena, N. B. Nelson, and D. A. Hansell, “Global distribution and dynamics of colored dissolved and detrital organic materials,” J. Geophys. Res.107(C12), 3228 (2002). [CrossRef]
  29. H. R. Gordon and A. Morel, Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. A Review (Springer-Verlag, New York, 1983).
  30. H. Loisel and A. Morel, “Light scattering and chlorophyll concentration in case 1 waters: a re-examination,” Limnol. Oceanogr.43(5), 847–858 (1998). [CrossRef]
  31. A. Morel, D. Antoine, and B. Gentili, “Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function,” Appl. Opt.41(30), 6289–6306 (2002). [CrossRef] [PubMed]
  32. H. Loisel, J.-M. Nicolas, A. Sciandra, D. Stramski, and A. Poteau, “Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean,” J. Geophys. Res.111(C9), C09024 (2006). [CrossRef] [PubMed]
  33. D. Antoine, D. A. Siegel, T. Kostadinov, S. Maritorena, N. B. Nelson, B. Gentili, V. Vellucci, and N. Guillocheau, “Variability in optical particle backscattering in contrasting bio-optical oceanic regimes,” Limnol. Oceanogr.56(3), 955–973 (2011). [CrossRef]
  34. R. D. Vaillancourt, C. Brown, R. L. Guillard, and W. M. Balch, “Light backscattering properties of marine phytoplankton: relationships to cell size, chemical composition and taxonomy,” J. Plankton Res.26(2), 191–212 (2004). [CrossRef]
  35. A. Bricaud, H. Claustre, J. Ras, and K. Oubelkheir, “Natural variability of phytoplankton absorption in oceanic waters: influence of the size structure of algal populations,” J. Geophys. Res.109(C11), C11010 (2004). [CrossRef]
  36. H. Loisel and D. Stramski, “Estimation of the inherent optical properties of natural waters from irradiance attenuation coefficient and reflectance in the presence of Raman scattering,” Appl. Opt.39(18), 3001–3011 (2000). [CrossRef]
  37. Y. Huot, A. Morel, M. Twardowski, D. Stramski, and R. A. Reynolds, “Particle optical backscattering along a chlorophyll gradient in the upper layer of the eastern south pacific ocean,” Biogeosciences5(2), 495–507 (2008). [CrossRef]
  38. P. J. Werdell and S. W. Bailey, “An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation,” Remote Sens. Environ.98(1), 122–140 (2005). [CrossRef]

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