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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 48, Iss. 7 — Mar. 1, 2009
  • pp: 1249–1261
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Parameterization of light absorption by components of seawater in optically complex coastal waters of the Crimea Peninsula (Black Sea)

Egor V. Dmitriev, Georges Khomenko, Malik Chami, Anton A. Sokolov, Tatyana Y. Churilova, and Gennady K. Korotaev  »View Author Affiliations


Applied Optics, Vol. 48, Issue 7, pp. 1249-1261 (2009)
http://dx.doi.org/10.1364/AO.48.001249


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Abstract

The absorption of sunlight by oceanic constituents significantly contributes to the spectral distribution of the water-leaving radiance. Here it is shown that current parameterizations of absorption coefficients do not apply to the optically complex waters of the Crimea Peninsula. Based on in situ measurements, parameterizations of phytoplankton, nonalgal, and total particulate absorption coefficients are proposed. Their performance is evaluated using a log–log regression combined with a low-pass filter and the nonlinear least-square method. Statistical significance of the estimated parameters is verified using the bootstrap method. The parameterizations are relevant for chlorophyll a concentrations ranging from 0.45 up to 2 mg / m 3 .

© 2009 Optical Society of America

1. Introduction

The absorption of sunlight by particulate and dissolved matters in the ocean significantly contributes to the spectral distribution of the water-leaving radiance. The inverse dependence of the remote sensing reflectance on the total spectral absorption coefficient together with the distinctive characteristics of the oceanic constituents provides the physical basis for ocean color algorithms for determining their concentration from satellite measurements. The inversion of the remote sensing reflectance to retrieve concentrations of materials of interest (i.e., suspended sediments, chlorophyll, organic matter, and suspended organic detritus) requires the development of bio-optical models consisting of establishing relationships between the constituent concentrations and the optical properties of the particles. The absorption coefficient of the hydrosols is partitioned into absorptions due to phytoplankton pigments, col ored dissolved organic matter (CDOM), and nonalgal particles (NAPs).

During the past two decades, several field experiments devoted to the study of the spectral signature of phytoplankton absorption coefficients in various aquatic environments have been carried out [1

1. B. G. Mitchell and D. A. Kiefer, “Chlorophyll a specific absorption and fluorescence excitation spectra for light limited phytoplankton,” Deep-Sea Res. 35, 639–663 (1988). [CrossRef]

, 2

2. M. Babin, J. C. Therriault, L. Legendre, and A. Condal, “Variations in the specific absorption coefficient for natural phytoplankton assemblages: impact on estimates of primary production,” Limnol. Oceanogr. 38, 154–177 (1993). [CrossRef]

, 3

3. M. Babin, A. Morel, P. G. Falkowski, H. Claustre, A. Bricaud, and Z. Kobler, “Nitrogen- and irradiance-dependent variations of the maximum quantum yield of carbon fixation in eutrophic, mesotrophic and oligotrophic systems,” Deep-Sea Res. 43, 1241–1272 (1996). [CrossRef]

, 4

4. D. Stramski, A. Bricaud, and A. Morel, “Modeling the inherent optical properties of the ocean based on the detailed composition of planktonic community,” Appl. Opt. 40, 2929–2945 (2001). [CrossRef]

]. In open ocean waters, hereafter referred to as Case I waters [5

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

], the spectral variation of the phyto plankton absorption coefficient as a function of the chlorophyll a concentration is thoroughly studied in [6

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

, 7

7. 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, 31033–31044 (1998). [CrossRef]

]. In coastal waters, hereafter referred to as Case II waters [5

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

], the bio-optical models were poorly documented over a long period of time. An extensive analysis of light absorption coefficients for a large data set collected in various European coastal waters is provided in [8

8. 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:10.1029/2001JC000882. [CrossRef]

]. In particular, Babin et al. [8

8. 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:10.1029/2001JC000882. [CrossRef]

] proposed parameterizations of the NAP and CDOM absorption properties for coastal zones. However, the parameterization of phytoplankton absorption coefficient with respect to chlorophyll concentration has still not been extensively studied in optically complex waters. The goal of the current paper is to study the variability in the spectral absorption coefficients of the particulate and dissolved components in coastal waters whose optical properties significantly depart from those measured by Babin et al. [8

8. 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:10.1029/2001JC000882. [CrossRef]

]. We especially focus on the relationship between the absorption properties of the phytoplankton and the pigment concentration. The analysis is conducted based on field measurements collected in the Black Sea coastal zone off the Crimea Peninsula, for which the variations of the optical properties of particles are presented and discussed in detail in [9

9. M. Chami, E. B. Shybanov, T. Y. Churilova, G. A. Khomenko, M. E.-G. Lee, O. V. Martynov, G. A. Berseneva, and G. K. Korotaev, “Optical properties of the particles in the Crimea coastal waters (Black Sea),” J. Geophys. Res. 110, C11020 (2005), doi:10.1029/2005JC003008. [CrossRef]

, 10

10. M. Chami, E. Marken, J. J. Stamnes, G. Khomenko, and G. Korotaev, “Variability of the relationship between the particulate backscattering coefficient and the volume scattering function measured at fixed angles,” J. Geophys. Res. 111, C05013 (2006), doi:10.1029/2005JC003230. [CrossRef]

, 11

11. M. Chami, D. McKee, E. Leymarie, and G. Khomenko, “Influence of the angular shape of the volume scattering function and multiple scattering on remote sensing reflectance,” Appl. Opt. 45, 9210–9220 (2006). [CrossRef] [PubMed]

, 12

12. M. Chami, E. B. Shybanov, G. Khomenko, M. Lee, O. V. Martynov, and G. Korotaev, “Spectral variation of the volume scattering function measured over the full range of scattering angles in a coastal environment,” Appl. Opt. 45, 3605–3619 (2006). [CrossRef] [PubMed]

].

First, a short description of the field experiment and the methods used for measuring the optical properties of the particles is presented. Second, bio- optical models for deriving the absorption coefficients of phytoplankton, NAPs, and CDOM are investigated. It is shown that the current parameterizations of spectral absorption coefficients [7

7. 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, 31033–31044 (1998). [CrossRef]

, 8

8. 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:10.1029/2001JC000882. [CrossRef]

] do not fit our data set. Therefore we propose new parameterizations of the absorption coefficients of phytoplankton, NAPs, and CDOM, which could be applicable for coastal waters showing similar optical features as those observed in Black Sea coastal zones. The parameterizations are obtained by applying different statistical methods. The performance of these methods is discussed and a rigorous analysis of the errors is performed. The stability and reliability of the retrieved parameters are verified.

2. Material and Methods of Measurements

The particulate absorption was measured using the methods described in [1

1. B. G. Mitchell and D. A. Kiefer, “Chlorophyll a specific absorption and fluorescence excitation spectra for light limited phytoplankton,” Deep-Sea Res. 35, 639–663 (1988). [CrossRef]

, 20

20. C. S. Yentsch, “Measurement of visible light absorption by particulate matter in the ocean,” Limnol. Oceanogr. (Engl. Transl.) 7, 207–217 (1962). [CrossRef]

]. The separation of the particulate absorption into phytoplankton and other components is based on the methanol extraction method [21

21. M. 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).

]. The measurement made before the methanol extraction provides the total particulate absorption coefficient ap(λ), while the depigmented particle absorption coefficient aNAP(λ) is obtained after the extraction. The parameter aNAP(λ) is consistent with the absorption by the detrital particulate matter and is thus referred to as the NAP absorption coefficient. The phytoplankton absorption coefficient aph(λ), which represents the absorption by the living particles, is obtained by the difference between ap(λ) and aNAP(λ). The spectral absorption of CDOM was measured with the dual beam spectrophotometer (SPECORD-M40, Carl Zeiss) using a 0.1m long cuvette and deionized water as the reference. The water samples were filtered throughout 0.2μm-pore-size membrane filters, which were first prerinsed with 50ml of deionized water. The absorption was corrected for the instrument baseline, and the CDOM absorption coefficient aCDOM(λ) was calculated according to
aCDOM(λ)=(ODs(λ)ODbs(λ))·ln10,
(1)
where ODs(λ) is the optical density of the filtrate sample, and ODbs(λ) is the optical density of a purified water blank. Note that OD(λ)=D(λ)/l, where D(λ) is the spectral absorbance, and l is the cuvette length (0.1m here).

3. Results and Discussion

3A. Local Absorption Budget

In the open ocean, the phytoplankton makes the major contribution to the absorption at 443nm. In coastal waters, the absorption balance is much more variable. The relative contribution of phytoplankton, NAPs, and CDOM to the total absorption coefficient for the Black Sea coastal water was studied as a function of wavelength for 4 days, covering the entire experiment (Fig. 1). The contribution from the CDOM absorption coefficient to the total absorption coefficient is higher than 50%. Thus the CDOM is the main light-absorbing component at 443nm. Note that similar results were obtained for the New England [22

22. H. M. Sosik and R. E. Green, “Temporal and vertical variability in optical properties of New England shelf waters during late summer and spring,” J. Geophys. Res. 106, 9455–9472 (2001). [CrossRef]

] and South Atlantic Bight shelf waters [23

23. J. R. Nelson and S. Guarda, “Particulate and dissolved spectral absorption on the continental shelf of the southeastern United States,” J. Geophys. Res. 100, 8715–8732 (1995). [CrossRef]

]. The phytoplankton and NAP contribution is around 24% and 22%, respectively, with rather stable values for phytoplankton.

3B. Phytoplankton Pigment Absorption as a Function of Chlorophyll A Concentration

3B1. Shape of the Relationship between aph and Chla

The analysis of a great number of measurements provided by [6

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

] demonstrated that a nonlinear relationship between the particulate absorption coefficient and the chlorophyll a concentration can be approximated using a power-law function. Further, Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

] proposed a more relevant parameterization for the phytoplankton absorption coefficient when dealing with Case I water:
aph(λ)=Aph(λ)ChlEph(λ).
(2)
The coefficients Aph(λ) and Eph(λ) can be obtained through the linear fitting between logarithms of the absorption lnaph(λ) and the concentration lnChl of pigments for each wavelength λ:
Aph(λ)=exp(lnaph(λ)¯Eph(λ)lnChl¯),Eph(λ)=(lnaph(λ)lnaph(λ)¯)(lnChllnChl¯)¯(lnChllnChl¯)2¯,
(3)
where the notation of parameters with an overbar means ensemble averaging.

The same approach as in the Bricaud et al. study [7

7. 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, 31033–31044 (1998). [CrossRef]

] was applied to the Black Sea data. The spectral distribution of the absorption coefficient as a function of Tchla was then determined. The data set used in our study was obtained for a relatively short period of time, and the values of Tchla range from 0.45 up to 2.04mg/m3. This is much less than in [7

7. 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, 31033–31044 (1998). [CrossRef]

], where the chlorophyll concentration varied by a factor of 500. The scatterplot of the phytoplankton absorption coefficients at 440nm (Fig. 2) shows that the nonlinear effect observed in the data of Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

] is not pronounced here. However, we have used the power-law fitting, as it is additional “a priori” information obtained and validated earlier by Bricaud et al. on the basis of the large number of measurements. Another reason is that it describes well the behavior of the absorption coefficient for the values of chlorophyll concentration outside the range of our measurements.

3B2. Statistical Distribution of the Measurements and Estimation of Confidence Intervals

The results of fitting the Black Sea absorption data set together with the similar fitting presented in [7

7. 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, 31033–31044 (1998). [CrossRef]

] are shown in Fig. 3. The numerical values of the fitting parameters are tabulated in Table 1 with a spectral resolution of 10nm. The complete values can be obtained from the authors upon request. It is observed that the simple least-squares fit with a power-law function induces a spectral distribution of the estimated parameters, which is rather noisy [Figs. 3a, 3b]. It is necessary to identify the variations showing a statistical significance. The coefficients Aph(λ) and Eph(λ), which are derived from the statistical fitting, are considered as random values. To check whether the difference between our fitting and the parameterization proposed by Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

] is not a result of stochastic fluctuations, the confidence intervals for the coefficients Aph(λ) and Eph(λ) need to be estimated. Because most of the frequently used efficient estimates and statistical tests are based on the assumptions of Gaussian distribution of the data, it was necessary to check first whether the statistical distribution of lnaph(λ) and lnTchla is consistent with a normal distribution. Two tests were used for that purpose. The first one is the Jarque–Bera test [28

28. A. K. Bera and C. M. Jarque, “Efficient tests for normality, homoscedasticity and serial independence of regression re siduals,” Econ. Lett. 6, 255–259 (1980). [CrossRef]

]. This test is based on the hypothesis that samples from the normal distribution have an expected skewness value of 0 and an expected kurtosis value of 3. The second test, the so-called Lilliefors test [29

29. H. Lilliefors, “On the Kolmogorov–Smirnov test for normality with mean and variance unknown,” J. Am. Stat. Assoc. 62, 399–402 (1967). [CrossRef]

], uses as the statistics the maximum discrepancy between the empirical distribution function and the cumulative distribution function of the normal distribution with the estimated expectation and variance. So the Lilliefors test is just an adaptation of the Kolmogorov–Smirnov test [30

30. A. N. Kolmogorov, “Confidence limits for an unknown distribution function,” Ann. Math. Stat. 12(4), 461–463 (1941). [CrossRef]

] to the case where the parameters of normal distribution are not specified. Based on these tests, the hypothesis that the variables lnTchla and lnaph(λ) follow a normal distribution (Fig. 4) can be rejected with a probability of 0.95 for most of the wavelengths. Thus one cannot successfully apply the standard methods for constructing confidence intervals that are commonly based on student’s distribution or F distribution (see [31

31. D. S. Wilks, Statistical Methods in the Atmospheric Sciences, 2nd ed. (Elsevier Academic, 2006).

] for a complete description of the standard statistical methods). Therefore the so-called “bootstrap method” [32

32. A. C. Davison and V. D. Hinkley, Bootstrap Methods and Their Application (Cambridge U. Press, 1997).

] was used to estimate the confidence intervals for coefficients Aph(λ) and Eph(λ).

Parametric techniques calculate a distribution function of the estimated parameter from the distribution function of the population, which is supposed to be known. In contrast, the bootstrap method allows us to estimate confidence intervals without this latter hypothesis. The bootstrap method is based on the generation of several ensembles of samples (so-called bootstrap samples) using the original measurements. Each bootstrap sample is created using a random sampling of the measured data. Every sample is returned to the original data set after selection. Therefore, when constructing a bootstrap sample, a particular data point from the original data set could appear multiple times. Using the various generated bootstrap samples, the procedure allows us to resample the coefficients Aph(λ) and Eph(λ) as many times as we need for the construction of an empirical cumulative distribution function that enables us to establish the uncertainty of those quantities. The confidence intervals constructed in such a way using 200 bootstrap samples are also shown in Figs. 3a, 3b. The results point out that the difference between the Aph(λ) and the Eph(λ) coefficients derived from the Black Sea measurements and those derived from [7

7. 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, 31033–31044 (1998). [CrossRef]

] cannot be explained by a poor statistic, and thus the observed difference is statistically meaningful.

Figures 3a, 3b also show that all high spectral perturbations occurring within a resolution of 20nm remain inside the confidence intervals, and thus these perturbations are statistically not significant. Therefore a low-pass filter was applied to remove the spurious harmonics. This filter is based on the local regression smoothing procedure proposed by Cleveland and Devlin [33

33. W. S. Cleveland and S. J. Devlin, “Locally-weighted regression: an approach to regression analysis by local fitting,” J. Am. Stat. Assoc. 83, 596–610 (1988). [CrossRef]

]. This procedure employs the weighted second degree polynomial fitting in a moving interval. The weights are evaluated to adapt the moving interval to the irregular wavelength step and to make the solution robust to possible outliers. The Cleveland and Devlin [33

33. W. S. Cleveland and S. J. Devlin, “Locally-weighted regression: an approach to regression analysis by local fitting,” J. Am. Stat. Assoc. 83, 596–610 (1988). [CrossRef]

] procedure was modified here to better account for the spectral variations of the parameters. The modification that was carried out in the current study consists of setting the spectral variability of the span (the key parameter of the local regression smoothing) by employing the confidence intervals smoothed with a running mean. The proposed method preserves well the low-frequency variability of the extracted signal. The final spectral distribution of the coefficients Aph(λ) and Eph(λ) are shown in Figs. 3c, 3d together with the confidence intervals. It is observed that the overall spectral shapes of the parameters calculated for the Black Sea waters and for the Case I waters are nearly similar, especially for Aph. The correlation coefficients between our data and the Bricaud et al. study [7

7. 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, 31033–31044 (1998). [CrossRef]

] are 0.99 and 0.76 for Aph(λ) and Eph(λ), respectively. Therefore, in the particular case where the values of Tchla are close to 1mgm3, the calculation of phytoplankton absorption coefficient based on Eq. (2) is fairly independent of Eph(λ), and thus the spectral shape of absorption by phytoplankton in the Crimea coastal zone is almost identical to the shape found in deepwater regions by Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

].

3B3. Comparison of Parameterizations

In contrast with Aph(λ), the Eph(λ) values derived from our fitting are smaller than those derived from the Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

] study in nearly the entire visible region [Fig. 3d]. All the exponents of the power- law function derived from our measurements are less than unity, which means a decrease of the chlorophyll-a-specific absorption coefficients with increasing Tchla. Such a type of function agrees with results obtained in open ocean waters [6

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

, 7

7. 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, 31033–31044 (1998). [CrossRef]

, 24

24. J. S. Cleveland, “Regional models for phytoplankton absorption as a function of chlorophyll a concentration,” J. Geophys. Res. 100, 13333–13344 (1995). [CrossRef]

, 25

25. H. M. Sosik and B. G. Mitchell, “Light absorption by phytoplankton, photosynthetic pigments, and detritus in the California current system,” Deep-Sea Res. 42, 1717–1748 (1995). [CrossRef]

, 34

34. S. Sathyendranath, T. Platt, V. Stuart, B. Irwin, M. J. W. Veldhuis, G. W. Kraay, and W. G. Harrison, “Some bio-optical characteristics of phytoplankton in the NW Indian Ocean,” Mar. Ecol. Prog. Ser. 132, 299–311 (1996). [CrossRef]

, 35

35. S. Sathyendranath, V. Stuart, B. Irwin, H. Maass, G. Savidge, L. Gilpin, and T. Platt, “Seasonal variations in bio-optical properties of phytoplankton in the Arabian Sea,” Deep-Sea Res. II 46, 633–653 (1999). [CrossRef]

] and more recently in the deep water region of the Black Sea [36

36. T. Churilova, G. Berseneva, and L. Georgieva, “Variability in bio-optical characteristics of phytoplankton in the Black Sea,” Oceanology 44, 192–204 (2004).

]. In contrast with the results reported in [7

7. 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, 31033–31044 (1998). [CrossRef]

], our fitting of Eph(λ) reveals a strong minimum around the 580nm wavelength. Based on the confidence intervals, the minimum observed at 580nm is statistically significant. The existence of this minimum means that the spectral shape of the phytoplankton absorption in the Crimea coastal zone is significantly different from the shape found in the open ocean waters. Within the range of Tchla measured during the Black Sea experiment, the influence of the coefficient Eph(λ) on the parameterization of aph(λ) is moderate. On the basis of Eq. (2), the major impact of the differences observed in Eph(λ) values between Case II and Case I waters would be observed at extremely high (typically when Tchla is more than 10mg/m3) or low (typically when Tchla is less than 0.1mg/m3) chlorophyll concentrations. In effect, the coefficient Eph(λ) varies from 0.4 to 0.8 within the spectral range around the minimum. The term ChlEph(λ), which is used in the parameterization [see Eq. (2)], varies by a factor of 2.5 when Tchla ranges from 0.1 to 10mg/m3 and only by a factor of 1.3 when Tchla varies within the interval from 0.45 to 2.04mg/m3 corresponding to our data set.

3B4. Analysis of Errors

Together with the confidence intervals, the error of the parameterization was estimated using the leave-one-out cross validation method [31

31. D. S. Wilks, Statistical Methods in the Atmospheric Sciences, 2nd ed. (Elsevier Academic, 2006).

]. This method simulates the use of the parameterization with unknown data by (i) repeating the fitting procedure on the data subsets and then (ii) estimating the error on the data portions left out of each of these subsets. The mean value of this error can be interpreted as the systematic bias of the absorption parameterization, while the standard deviation characterizes the random error. The spectral variations of these errors (i.e., mean and random errors) are presented in Fig. 5. At all wavelengths, the random error is significantly higher than the mean error. In the green–red spectral region, the variations of random error are in agreement with the spectral variations of the absorption coefficient. With decreasing wavelength, the situation changes, and both errors increase. High values of the relative error of the absorption coefficient are observed in the UV spectral region, which is especially important at low values of chlorophyll concentrations. At the same time, the broadening of confidence intervals suggests a possible instability in the shortwave area. So it is likely that the constructed parameterization is questionable at wavelengths shorter than 400nm.

3C. Absorption Properties of Nonalgal Particles, Total Particulate, and Colored Dissolved Organic Matter

3C1. Influence of the Fitting Method on aNAP(λ) Parameterization

In this paper, two different techniques of exponential fitting were applied: one is based on the data linearization method (DLM) [38

38. J. H. Mathews and K. K. Fink, Numerical Methods Using MATLAB (Prentice Hall, 2004).

], and the other is based on the numerical minimization of the functional error (the nonlinear least-square method [NLSM]) [38

38. J. H. Mathews and K. K. Fink, Numerical Methods Using MATLAB (Prentice Hall, 2004).

]. The mean values of the RMSE are quite different for these methods, namely, 0.0074 and 0.0023m1 for DLM and NLSM, respectively. An example showing one of the cases where RMSE has a typical value is presented in Fig. 7a. Both methods systematically induce an underestimate of the absorption measurements in the spectral range of 450550nm and an overestimate of the data in the range of 680750nm. However, in the UV spectral region, the behavior of DLM and NLSM approximations does not show any systematic underestimate or overestimate of the data. This is probably because the measurements showed the highest variability in the UV part of the spectrum. The comparison of the performance of DLM and NLSM is now carried out based on a representative case for which the fitting error of the absorption spectra retrieval is the highest [Fig. 7b]. For such a case, the mean values of the relative RMSE are 12% and 7% for DLM and NLSM, respectively. Therefore the NLSM appears to be a more accurate technique than the DLM. This can be explained by the fact that the sensitivity of the fitting carried out using the NLSM to the spectral variations of the optical parameters does not depend on the wavelength, while the DLM takes into account the spectral dependency of these parameters due to the linearization approach. As a result, the NLSM is more robust and appropriate for parameterizing absorption coefficients of marine particles.

3C2. Estimation of the Exponential Slope Parameter

The significance of the difference between the slope parameters of the exponential fit (SNAP) obtained by the fitting methods considered in this paper was also studied. We constructed the histograms of SNAP and fitted the probability density functions of the normal distribution for each of these two methods [Figs. 9a, 9b]. The slope parameter of the exponential fit SNAP varies from 0.0099 to 0.0157nm1 for the DLM and from 0.0086 to 0.0159nm1 for the NLSM, which is in agreement with the results obtained by Babin et al. [8

8. 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:10.1029/2001JC000882. [CrossRef]

]. We were not able to find any significant correlation between the slope parameter and the absorption coefficient at the reference wavelength 443nm, as well as some apparent nonlinear relationship (Fig. 10). Thus the uncertainty in SNAP cannot be reduced using aNAP(443). Therefore the mean SNAP value was used in the parameterization of NAP absorption. In the latter case, the uncertainty in the parameterization can be defined as two or three times the value of the standard deviation of SNAP, which is equal to 0.001m1 for the DLM and 0.0012m1 for the NLSM. The difference of mean values of SNAP obtained by the DLM and the NLSM is significant and exceeds the value of the standard deviation. Note also that the difference between mean values of SNAP for the DLM and the NLSM is greater than the range of the average slopes obtained for different waters represented in [8

8. 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:10.1029/2001JC000882. [CrossRef]

]. The problem of the choice of the exponential fitting method is really important for constructing the parameterization of the NAP absorption coefficient, and thus it is necessary to decide which of the tested methods is most suitable. Based on the results discussed in Subsection 3C1 (see Fig. 8), the use of the NLSM is clearly more appropriate. The NAP absorption coefficient could be parameterized as
aNAP(λ)=aNAP(443)e(0.0104±0.0024)(λ443).
(5)

3C3. Parameterization of the Total Particulate Spectral Absorption Coefficient

The relationship between the particulate absorption coefficient ap and the total phytoplankton pigment concentration for the Crimea coastal zone was also investigated using the method proposed in [7

7. 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, 31033–31044 (1998). [CrossRef]

] (see Subsection 3B1). The spectral distribution of coefficients Ap(λ) and Ep(λ) was calculated using the log–log regression technique [Figs. 11a, 11b]. The numerical values of these coefficients are tabulated Table 1. The confidence intervals (95%) were constructed using the bootstrap method.

The comparison with the fitting of Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

] clearly shows that the spectral distributions of coefficients Ap(λ) and Ep(λ) are not so similar in magnitude (much greater values in our case) and in shape, as it was highlighted for the parameterization of absorption by phytoplankton. The highest difference can be observed in the UV green spectral region, while the parameterizations in the red spectral region are fairly consistent. Obviously the discrepancy between our parameterization and that of Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

] cannot be explained by instability owing to the small number of samples. The discrepancy is probably due to the high content of NAPs, which is much more important here (around 50%) than in Case I waters. Such a high content of NAPs is expected in coastal areas, owing to rivers inputs. The decrease of the discrepancy with parameterization of Bricaud et al. [7

7. 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, 31033–31044 (1998). [CrossRef]

] around 678nm can be explained by a minor contribution of NAPs to ap in this spectral region.

3C4. Variability in the Colored Dissolved Organic Matter Absorption Coefficient

The water samples dedicated to the CDOM measurements were collected at the sea surface within the entire period of data acquisition on 2, 3, 10, and 14 August 2002. The magnitude of the CDOM absorption coefficient at 443nm aCDOM(443) ranges from 0.081m1 (on 3 August 2002) to 0.197m1 (on 14 August 2002) and changes by more than a factor of 2 during the field experiment, with a significant increase after the wind-gusting event that occurred a few days after the beginning of the sampling [14

14. T. Y. Churilova and G. P. Berseneva, “Absorption of light by phytoplankton, detritus, and dissolved organic substances in the coastal region of the black sea (July—August 2002),” Phys. Oceanogr. 14, 221–233 (2004), in Russian.

]. The mean value of aCDOM(443) (0.13m1) is close to values measured in the North Sea and remains within the interval of the whole variability of this parameter, namely, 0.010.6m1 found in various coastal waters around Europe [8

8. 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:10.1029/2001JC000882. [CrossRef]

]. The aCDOM(443) values obtained in the Black Sea are consistent with the cross-shelf measurements in the South Atlantic Bight (0.020.4m1) [23

23. J. R. Nelson and S. Guarda, “Particulate and dissolved spectral absorption on the continental shelf of the southeastern United States,” J. Geophys. Res. 100, 8715–8732 (1995). [CrossRef]

] but remain lower than the data obtained in the New England shelf waters (0.0600.081m1) [22

22. H. M. Sosik and R. E. Green, “Temporal and vertical variability in optical properties of New England shelf waters during late summer and spring,” J. Geophys. Res. 106, 9455–9472 (2001). [CrossRef]

].

4. Conclusions

We have studied the absorption properties of phytoplankton and nonalgal and CDOMs for coastal seawaters. Our analysis is based on a data set of bio-optical measurements obtained during a field experiment carried out in the Crimea Peninsula (Black Sea, Ukraine). The absorption budget showed that Crimea coastal waters clearly fall into the Case II water type, with a yellow-substance-dominated regime. It was demonstrated that the use of current parameterization methods to estimate the spectral absorption coefficients of marine particles is not relevant under these circumstances. Therefore relevant parameterizations for modeling the particulate and phytoplankton absorption properties were proposed. The parameterizations of the particulate and phytoplankton absorption coefficients obtained using the simple power-law fitting suffered from the spurious noise. We proposed a combination of the simple power-law fitting with the modified local regression method based on a low-pass filter and bootstrapping. The bootstrap method allowed us to estimate confidence intervals for the parameterization coefficients in the case of non-Gaussian distributed samples of the particulate and phytoplankton absorption coefficients. The derived confidence intervals were then used to adjust the local regression filter for efficiently removing the spurious noise with the minimum underestimation of low-frequency variability. The parameterizations are relevant to coastal waters near the Crimea Peninsula and for chlorophyll a concentrations ranging from 0.45 up to 2.04mg/m3.

The measurements of NAP and CDOM absorption coefficients were fitted using an exponential function. Two different techniques were tested and compared for estimating the slope parameter: the DLM and the NLSM. The sensitivity of the DLM to the choice of spectral intervals used for its calibration was significant. Thus the use of the DLM led to the instability of the parameterizations and to greater errors. In particular, it was shown that the NLSM is weakly sensitive to the influence of any residual pigment absorption, and thus the NLSM is more appropriate than the standard exponential law fits currently employed in coastal areas [8

8. 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:10.1029/2001JC000882. [CrossRef]

] to parameterize NAP absorption coefficients. Thus, to obtain SNAP and SCDOM, we chose the NLSM as the fitting method that appeared to be much more accurate and stable to estimate the slope parameters. Our estimate of the mean value of SNAP is 0.0104±0.0024nm1, which is significantly lower than the slopes derived by Babin et al. [8

8. 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:10.1029/2001JC000882. [CrossRef]

]. On the other hand, the mean value of SCDOM=0.0179nm1 is in good agreement with [8

8. 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:10.1029/2001JC000882. [CrossRef]

].

The results presented in this study clearly show that the Crimea coastal waters have their own optical particularities. We demonstrated that parameterizations established by other investigators do not fit our data set, and this is not explained by variations due to a small number of measurements. The proposed methods allowed us to increase the accuracy and stability of the parameterizations of the spectral absorption coefficients for Case II water. The results discussed in this paper have important implications for ocean color research. The proposed methods and parameterizations will contribute to significantly improve the modeling of the inherent optical properties and thus the analysis of water-leaving radiances in optically complex waters, which are of great interest for satellite remote sensing applications.

This work was funded by the Centre National d’Etudes Spatiales (CNES), France, and by the Russian Foundation for Basic Research (projects 07-05-00328-а and 06-05-64916-a). The authors thank A. Kuznetsov, the director of the platform in Katsiveli, Ukraine. We are grateful to all the par ticipants of the experiment: M. E.-G. Lee, G. A. Berseneva, E. B. Martynov, and M. Shybanov. The authors also thank A. Bricaud for helpful dis cussions. We dedicate this paper to the memory of G. Khomenko.

Table 1. Numerical Values of the Coefficients Used for the Parameterizations of Phytoplankton and Particulate Absorption Coefficientsa

table-icon
View This Table
Fig. 1 Relative contribution of phytoplankton, NAPs, and CDOM absorption to the total absorption for (a) 2 August, (b) 3 August, (c) 10 August, and (d) 14 August 2002. The area between the x axis and the first lower curve represents the contribution of phytoplankton to the total absorption. The area between the lower and the upper curves represents the contribution of NAPs. The area above the upper curve represents the contribution of CDOM.
Fig. 2 Log–log scatterplot of the phytoplankton absorption coefficients at 440nm versus Tchla. The linear regression line is presented as a dashed curve, and the log–log regression is plotted using the solid line.
Fig. 3 Spectral values of the numerical coefficients (a) Aph(λ) and (b) Eph(λ) defining the parameterization of light absorption by phytoplankton as a function of Tchla. (c), (d) Similar to (a) and (b), except the modified local regression smoothing was used to remove the spurious high-frequency variability. Thick black lines are used for our fitting, thick gray lines represent the results obtained by Bricaud et al. [7], and dashed black lines represent 95% confidence intervals.
Fig. 4 Tests of normal distribution for lnaph(λ). The upper bar represents the results of the Jarque–Bera test, the middle bar represents the Lilliefors test, and the lower bar corresponds to the hybrid test. The significance level is 0.05.
Fig. 5 Spectral distribution of the mean and random errors (the random error is defined as the standard deviation of the error) of the absorption parameterization of aph(λ).
Fig. 6 Variations of the spectral absorption coefficient of phytoplankton for a fixed value of Tchla, namely, 0.65mgm3. In (a), the thick gray curve represents the parameterization of phytoplankton absorption coefficient proposed by Bricaud et al. [7], and the thick black curve is the parameterization obtained based on our data set. The thin gray curves (five in total) are spectral absorption coefficients measured for different waters at the fixed chlorophyll concentration value of 0.65mg/m3. The correlation coefficients between these five curves are represented in (b).
Fig. 7 Examples of the exponential fit of the NAP spectral absorption coefficient using different methods for (a) a case representing the fit for which the error is close to its typical value (sample collected on 28 July 2002, depth 8m) and (b) a case corresponding to the greatest error made by the NLSM fitting (sample collected on 14 August 2002, depth 16m).
Fig. 8 RMSE of DLM (solid gray curve) and NLSM (solid black curve) for all the measurements (a) when the fitting is carried out in the spectral region from 380 to 730nm, excluding the intervals 400–480 and 620710nm, and (b) when the fitting is performed using the entire spectral region from 350 to 700nm.
Fig. 9 Histograms of the exponential slope parameter SNAP for the parameterization of the NAP absorption coefficient. The fitting is performed in the spectral region from 380 to 730nm, excluding the intervals 400–480 and 620710nm using two different methods: (a) DLM and (b) NLSM. Black curves show the probability density functions of the normal distribution fitted to the corresponding data and scaled to the histograms.
Fig. 10 Scatterplots of the slope parameter SNAP estimated by the DLM (squares) and by the NLSM (stars), with respect to the NAP absorption coefficient at the reference wavelength 443nm. Black solid and dashed lines show the corresponding mean values of SNAP.
Fig. 11 Spectral values of the numerical coefficients (a) Ap(λ) and (b) Ep(λ) used in the parameterization of the total particulate absorption coefficient as a function of Tchla. Thick black lines represent the results of the current study, thick gray lines represent results obtained in the Bricaud et al. study [7], and thin gray lines represent the confidence intervals at 95%.
Fig. 12 Exponential fitting of the CDOM absorption coefficient using the NLSM. The gray curves represent the measurements of the CDOM absorption coefficient obtained on 3 August 2002 (surface), the dashed curves show the proposed fitting calculated over the spectral region from 350500nm, the thick and thin black solid curves show the aCDOM parameterization proposed by Babin et al. [8] with uncertainty intervals.
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M. Babin, A. Morel, P. G. Falkowski, H. Claustre, A. Bricaud, and Z. Kobler, “Nitrogen- and irradiance-dependent variations of the maximum quantum yield of carbon fixation in eutrophic, mesotrophic and oligotrophic systems,” Deep-Sea Res. 43, 1241–1272 (1996). [CrossRef]

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7.

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, 31033–31044 (1998). [CrossRef]

8.

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:10.1029/2001JC000882. [CrossRef]

9.

M. Chami, E. B. Shybanov, T. Y. Churilova, G. A. Khomenko, M. E.-G. Lee, O. V. Martynov, G. A. Berseneva, and G. K. Korotaev, “Optical properties of the particles in the Crimea coastal waters (Black Sea),” J. Geophys. Res. 110, C11020 (2005), doi:10.1029/2005JC003008. [CrossRef]

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M. Chami, E. Marken, J. J. Stamnes, G. Khomenko, and G. Korotaev, “Variability of the relationship between the particulate backscattering coefficient and the volume scattering function measured at fixed angles,” J. Geophys. Res. 111, C05013 (2006), doi:10.1029/2005JC003230. [CrossRef]

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M. Chami, D. McKee, E. Leymarie, and G. Khomenko, “Influence of the angular shape of the volume scattering function and multiple scattering on remote sensing reflectance,” Appl. Opt. 45, 9210–9220 (2006). [CrossRef] [PubMed]

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M. Chami, E. B. Shybanov, G. Khomenko, M. Lee, O. V. Martynov, and G. Korotaev, “Spectral variation of the volume scattering function measured over the full range of scattering angles in a coastal environment,” Appl. Opt. 45, 3605–3619 (2006). [CrossRef] [PubMed]

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G. K. Korotaev, G. A. Khomenko, M. Chami, G. A. Bersevena, O. V. Martynov, M. E-B. Lee, E. B. Shybanov, T. V. Churilova, A. S. Kuznetsov, and A. K. Kuklin, “International subsatellite experiment on the oceanographic platform,” Phys. Oceanogr. 14, 150–160 (2004). [CrossRef]

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T. Churilova, G. Berseneva, and L. Georgieva, “Variability in bio-optical characteristics of phytoplankton in the Black Sea,” Oceanology 44, 192–204 (2004).

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OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(010.1030) Atmospheric and oceanic optics : Absorption

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: August 13, 2008
Revised Manuscript: January 14, 2009
Manuscript Accepted: January 14, 2009
Published: February 23, 2009

Citation
Egor V. Dmitriev, Georges Khomenko, Malik Chami, Anton A. Sokolov, Tatyana Y. Churilova, and Gennady K. Korotaev, "Parameterization of light absorption by components of seawater in optically complex coastal waters of the Crimea Peninsula (Black Sea)," Appl. Opt. 48, 1249-1261 (2009)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-48-7-1249


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References

  1. B. G. Mitchell and D. A. Kiefer, “Chlorophyll a specific absorption and fluorescence excitation spectra for light limited phytoplankton,” Deep-Sea Res. 35, 639-663 (1988). [CrossRef]
  2. M. Babin, J. C. Therriault, L. Legendre, and A. Condal, “Variations in the specific absorption coefficient for natural phytoplankton assemblages: impact on estimates of primary production,” Limnol. Oceanogr. 38, 154-177 (1993). [CrossRef]
  3. M. Babin, A. Morel, P. G. Falkowski, H. Claustre, A. Bricaud, and Z. Kobler, “Nitrogen- and irradiance-dependent variations of the maximum quantum yield of carbon fixation in eutrophic, mesotrophic and oligotrophic systems,” Deep-Sea Res. 43, 1241-1272 (1996). [CrossRef]
  4. D. Stramski, A. Bricaud, and A. Morel, “Modeling the inherent optical properties of the ocean based on the detailed composition of planktonic community,” Appl. Opt. 40, 2929-2945(2001). [CrossRef]
  5. A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22, 709-722 (1977). [CrossRef]
  6. A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: analysis and parameterization,” J. Geophys. Res. 100, 13321-13332 (1995). [CrossRef]
  7. 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, 31033-31044 (1998). [CrossRef]
  8. 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:10.1029/2001JC000882. [CrossRef]
  9. M. Chami, E. B. Shybanov, T. Y. Churilova, G. A. Khomenko, M. E.-G. Lee, O. V. Martynov, G. A. Berseneva, and G. K. Korotaev, “Optical properties of the particles in the Crimea coastal waters (Black Sea),” J. Geophys. Res. 110, C11020 (2005), doi:10.1029/2005JC003008. [CrossRef]
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