Backscattering ratio variation and its implications for studying particle composition: A case study in Yellow and East China seas

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi: /2010jc006098, 2010 Backscattering ratio variation and its implications for studying particle composition: A case study in Yellow and East China seas Minwei Zhang, 1 Junwu Tang, 2,3 Qingjun Song, 4 and Qing Dong 1 Received 6 January 2010; revised 16 July 2010; accepted 25 August 2010; published 4 December [1] Using in situ optical measurements collected during the 2003 spring cruise over the Yellow and East China seas, the particle backscattering ratio is calculated and spectral variability is analyzed by means of geometric mean regression. The analysis shows that the particle backscattering ratio can be regarded as wavelength independent in the range of nm, given the measurement uncertainties associated with the backscattering and scattering data. The backscattering ratio and attenuation measurements are used to calculate the particle refractive index, which is related to the particle composition. The distributions of the particle refractive index and the water component concentration along two transects suggest the feasibility for studying the particle composition. Citation: Zhang, M., J. Tang, Q. Song, and Q. Dong (2010), Backscattering ratio variation and its implications for studying particle composition: A case study in Yellow and East China seas, J. Geophys. Res., 115,, doi: /2010jc Introduction [2] The particle backscattering ratio is the ratio of backscattering to scattering and is used to model the underwater light field and to infer particle composition [Whitmire et al., 2007]. As for the first application, it has been shown that the backscattering ratio can be used to calculate the scattering phase function, which determines all the radiometric quantities in underwater light field when multiple scattering is taken into account [Mobley et al., 2002; Gordon, 1994]. However, recent studies have shown evidence both for and against wavelength dependent backscattering ratio. For example, studies in coastal waters have found the evidence for wavelength dependence [McKee and Cunningham, 2005; Chami et al., 2006b; Snyder et al., 2008], while other studies with more open ocean data showed that backscattering ratio is wavelength independent [Whitmire et al., 2007; Huot et al., 2008]. For the second one, the backscattering ratio is weakly dependent on concentration and can be related to particle size distribution, providing an estimate of bulk refractive index of particles in the ocean [Twardowski et al., 2001]. It allows discrimination between organically dominated particulate assemblages from those dominated by inorganic particles. As a result, it may be useful as a proxy for particle composition [Twardowski et al., 2001; Boss et al., 2004]. [3] The first purpose of the present paper is to analyze the spectral variability of the backscattering ratio. The second 1 Key Laboratory of Digital Earth, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China. 2 National Ocean Technology Center, Tianjin, China. 3 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing, China. 4 National Satellite Ocean Application Service, Beijing, China. Copyright 2010 by the American Geophysical Union /10/2010JC one is to calculate the particle refractive index using in situ optical measurements and to determine the possibilities for studying particle composition. 2. Data [4] The in situ data used in this study were collected during the spring cruise carried out in April 2003 over the Yellow Sea (YS) and East China Sea (ECS). There are 81 observation stations, which are shown in Figure 1. The water component concentration, inherent optical properties, apparent optical properties (see the definitions given by Mobley [1994]) and other environmental parameters were measured during this cruise Study Area [5] The YS and ECS are typical shallow, semienclosed seas with case 2 waters (the definition is detailed by Morel and Prieur [1977]). The YS covers an area about 380,000 km 2, with average and maximum depths of about 44 and 140 m. The values for ECS are 774, 000 km 2, 370 m, and 2719 m, respectively [Feng et al., 1999; He et al., 2004]. Due to the mineral rich soil carried by inland rivers that flow into the YS, the water actually appears yellow [Feng et al., 1999]. The YS is also contaminated by industrial pollution, agricultural runoff and domestic sewage. Abundant nutrients and sediments exported to the ECS from inland rivers [Gao and Song, 2005], together with the accumulative effects of winds, waves, tides, and resuspension of sediments due to vertical mixing lead to low transparency for the water mass Measurements of Colored Dissolved Organic Matter Absorption [6] A GBC UV Visible Cintra20 spectrometer was used to measure the absorption of colored dissolved organic matter (CDOM). Water samples were filtered by a polycarbonate filter with 0.2 mm pore size, which should be presoaked in 1of11

2 Figure 1. The observation stations during the spring cruise carried out in April 2003 over the Yellow and East China seas. 10% HCL for15 min and rinsed with ultrapure water [Mitchell et al., 2002]. The absorption of CDOM (a CDOM ) was calculated from equation (1): a CDOM ðþ ¼2:303½OD g ðþ OD bs ðþ OD null Š=l where l is the wavelength, l is the length of cuvette, OD g is the optical density of the filtrate sample relative to ultrapure water, OD bs is the optical density of a ultrapure water blank treated like a sample relative to ultrapure water, OD null is the apparent residual optical density at long visible or near infrared wavelength where absorption by CDOM is assumed to be zero. OD null was calculated from averaging the optical density in the range of nm at interval of 10 nm during this cruise. Negative values were found for the wavelength longer than 600 nm in Figure 2, which is partly resulted from the low relative accuracy of absorption measurements of low magnitude and partly from the OD null correction [Davies Colley and Vant, 1987]. a CDOM was considered as zero at wavelength longer than 600 nm in this study Scattering Measurements [7] Absorption (a n ) and attenuation (c n ) for materials other than water were measured by a WET Laboratories, Incorporated, AC 9 dual tube spectrophotometer with nine bands at 412, 440, 488, 510, 532, 555, 650, 676, and 715 nm. The AC 9 was calibrated with Milli Q ultrapure water. The ð1þ absorption data were corrected for effects of reflective tube scattering using the method described by WET Laboratories, Incorporated [2008]. The measurements were corrected for temperature and salinity effects using the coefficients given by WET Laboratories, Incorporated [2008] and data from a conductivity, temperature and depth (CTD), which was deployed with the AC 9 and HOBI Laboratories, Incorporated, Hydroscat 6 (HS 6). Particle scattering (b p ) was derived Figure 2. The a CDOM spectra collected during the 2003 spring cruise over the Yellow and East China seas. 2of11

3 the spring cruise are shown in Figure 4. The b p spectra show strong spectral consistency across the range and the b bp spectra show a possible tendency to decrease toward the red near infrared end of the spectrum. The a p spectra show that the particles absorb increasingly strongly in the blue Chlorophyll a Concentration Measurements [10] Chlorophyll a concentration (Chl a) was measured by means of two methods: fluorometric methodology [Trees et al., 2003] and High Performance Liquid Chromatography (HPLC). For the first method, a fluorometer (Turner 10) was used and was calibrated by chlorophyll a standard from SIGMA ( mainland.html). Figure 3. The comparison between b bp before (dashed line) and after (solid line) the sigma correction. The data are from the stations with C TSM about (a) and (b) mg L 1. from b p = c n a n. Particle attenuation (c p ) was derived from c p = c n a CDOM. [8] The measurements with relatively higher turbidity (usually with the total suspended sediment concentration (C TSM ) higher than 30 mg L 1 ) were dropped, for that it is hard to realize the measurements for the AC 9 and HS 6 [Maffione and Dana, 1997; WET Laboratories, Incorporated, 2008]. For each station, simultaneous measurements with the AC 9, HS 6 and CTD were carried out with binned depth intervals of 0.3, 0.4, or 0.5 m, resulting in 3980 measurements for each property Backscattering Measurements [9] The HS 6 was used to measure particle backscattering (b bp ) at 442, 488, 532, 589, 676, and 852 nm. It was calibrated by the manufacturer before the cruise and measures scattering at a single angle in the backward direction about 140 [Maffione and Dana, 1997]. Using the HS 6 measurement and the calibration coefficient, the b bp can be calculated [Oishi, 1990] and the sigma correction was performed for correcting the path length attenuation effects [HOBI Laboratories, Incorporated, 2008]. The comparison between the b bp before and after the sigma correction is shown in Figure 3, from which we can see that: (1) the difference for the turbid waters is larger than that for the relatively clear waters and (2) the difference in the blue wavelength is larger than that in other wavelengths, both resulted from the higher attenuation for the former than the latter. The b p, b bp and a p spectra measured in Figure 4. The b p, b bp and a p spectra for all of the data measured during the cruise; a p is calculated from a p = a n a CDOM. 3of11

4 Figure 5. The comparison between High Performance Liquid Chromatography (HPLC) and fluorometric derived Chl a collected during the cruise. Water samples were collected with a Niskin sampler and sent to laboratory for immediate filtration using 25 mm Whatman GF/F filters in a dark environment. The volume of the filtered water sample was recorded as V FILT. The filters were wrapped in aluminum foil and put into liquid nitrogen, until they were soaked for 24 h in 90% acetone in a refrigerator at 0 C, where the sample volume was recorded as V EXT. The fluorometer was preheated for min. The fluorescence value of the sample was recorded as F b before acidification and as F a after addition of one drop of 10% HCL (by volume). A 90% acetone blank (Blk b ) and an acidified acetone blank (Blk a ) should also be measured. Chl a was calculated from equation (2): HPLC should not be affected by the fluorescence effect from chlorophyll b Suspended Matter Concentration Measurements [12] Total suspended matter samples were filtered under vacuum onto Whatman GF/F filters, 47 mm diameter, mean nominal pore diameter 0.7 mm, which was preweighed. After being rinsed 3 times with 50 ml of distilled waters, the samples were dried in an oven at 100 C for 1 h and reweighed on an electronic analytic scale until the difference of successive calculated C TSM were less than 0.01 mg L 1. The concentration of inorganic suspended matter (C ISM ) was obtained by reweighing the samples after they had been placed in a muffle furnace at 500 C for 1 h, at which time all the organic materials were assumed to be combusted. [13] The C TSM measurements were corrected for the sea salt plus water of hydration retention using the method proposed by Stavn et al. [2009, Figure 1a]. The salinity used in the correction is from the CTD. The filter mass loss is about 1.06 mg when the samples were placed in the muffle furnace at 500 C for 1h, which is an empirical value from North China Sea Branch of the State Oceanic Administration (NCSB, personal communication). The loss of structural water in the clay was calculated using the method given by Barillé Boyer et al. [2003]. The clay fraction, which represents 26% of bottom suspended matter in ECS, consists of 67% illite, 15% Chl a ¼ðF b F a Blk b þ Blk a Þ 1 F r V EXT V FILT ð2þ where t is the fluorometer s sensitivity to pheophytin and F r is the fluorometer s response factor [Trees et al., 2003]. The HPLC method is proposed by Bidigare et al. [2003]. [11] Figure 5 shows the difference of Chl a measured by two methods described above. The fluorometric derived Chl a show a tendency to assume somewhat higher values than that derived from HPLC, which was also found by Stramska et al. [2003] and Zhao et al. [2007]. The RMSE relative error is about 34.8% with the accuracy lower than that presented by Bidigare et al. [2003], primarily due to the complication of the coastal case 2 waters, where there is not only phytoplankton but also abundant organic, inorganic suspended matters, and other pollutants. The calibration of the two methods both used Chl a standard and the correlation coefficient was very high, which became lower when in situ observations were carried out. The fluorometric methodology, based on fluorescence emission at channel 670 nm, is affected by chlorophyll b which is possibly present in the sample of the coastal case 2 waters [Zhao et al., 2007]. On the other hand, the HPLC is based on emission at some wavelengths. The chlorophyll a was discriminated through comparing the sample chromatogram with chlorophyll a standard. Chl a were calculated using the area of chromatographic peak and the response of chlorophyll a. As a result, Figure 6. The comparison between (a) C TSM and (b) C ISM measured by the Institute of Oceanology of Chinese Academy of Sciences (IOCAS) and by the North China Sea Branch of the State Oceanic Administration (NCSB). The solid lines are 1:1 line. 4of11

5 Figure 7. The frequency distributions of the particle backscattering ratio at bands (a) 442, (b) 488, (c) 532, (d) 589, and (e) 676 nm. chlorite, 11% kaolinite, 7% smectite [Youn et al., 2007]. The clay fraction represents 34% of suspended matter in YS, which was calculated from averaging surface sediments of a total of thirteen samples given by Yang and Youn [2007]. The clay minerals are composed of illite, chlorite, kaolinite, smectite, with the percentage values of 66%, 12%, 10%, and 13%, respectively, the averages of sediment samples from YS continental shelf [Park and Khim, 1992]. It was reported that the smectite remain in suspension longer than other clay minerals and thus the data on smectite percentage from bottom sediments is probably an underestimate [Stewart and Patrick, 1990]. The structural water loss can be calculated by thermogravimetric analysis. The mass loss at the temperature of 500 C is 4.5% for illite, 2% for chlorite, 2.5% for kaolinite, 15% for smectite [Mielenz et al., 1954]. The Figure 8. The particle backscattering plotted against the particle scattering at band 442 nm. 5of11

6 Figure 9. The particle backscattering plotted against the particle scattering at bands (a) 442, (b) 488, (c) 532, (d) 589, and (e) 676 nm for the data with b p (442) 8m 1. The solid lines are determined from geometric mean regression. R is the correlation coefficient. amount of clay structural water loss at 500 C represents 1.2% of the inorganic suspended matter in ECS. The value is 1.9% for YS. The clay mineral distribution in the water column above given sediment may be different from that of the bottom sediment, which may introduce error in the calculation of structural water loss. The C ISM were corrected for the loss of filter mass and the structural water in the clay. [14] The suspended matter concentration was measured independently by two groups, Institute of Oceanology of Chinese Academy of Sciences (IOCAS) and NCSB. The comparison is shown in Figure 6, with the RMSE relative error about 28.1% and 43.5%. The consistency of C ISM between the measurements from the two groups is worse that that of C TSM, possibly due to the time and temperature difference when being combusted in the muffle furnace. 3. Results and Discussions 3.1. Spectral Variability of Particle Backscattering Ratio [15] The particle backscattering ratio (b bp /b p ) was calculated by dividing b bp from the HS 6 measurements by b p from the AC 9 measurements. The AC 9 does not have the same wavelengths as the HS 6 and b p (589) was calculated from linear interpolation, the default interpolation method in Hydrolight 5.0 [Mobley and Sundman, 2008]. The reasons that bands 715 and 852 nm were not included in the spectral 6of11

7 Figure 10. The b p, b bp plotted against C TSM. The solid lines are determined using geometric mean regression method. 7of11

8 Figure 11. (a) Wavelength dependence of the point bypoint backscattering ratio for the low end data with b p (442) 8m 1. Maximum and minimum range values are shown with thinner bars, and standard deviations are shown with thicker error bars. (b) Regression determined backscattering ratio have tight confidence intervals for the low end data. variability analysis were detailed as following. The extrapolation for calculating b p (852) may introduce errors since there are some deviations in the model for describing the spectral dependency of b p. The accuracy of sigma correction in calculating the b bp (852) is relatively lower because of the higher attenuation for case 2 waters in YS and ECS in nearinfrared wavelengths [Song and Tang, 2006], resulting in the lower accuracy of b bp (715) which are interpolated using b bp (676) and b bp (852). Both the lower accuracy of b bp (715) and b p (852) introduce some additional errors in the spectral variability analysis. The frequency distributions of the particle backscattering ratio for all of the 3980 measurements are shown in Figure 7. The ranges of b bp /b p are , , , , for the data set at 442, 488, 532, 589, and 676 nm, respectively. These are very broad ranges, even greater than that presented by Whitmire et al. [2007] for the global ocean. If these data were taken at face value, they would suggest marked variability in particle composition, size distribution or both. Of course, it is also necessary to consider other potential sources of apparent variability such as measurement uncertainties [McKee et al., 2009]. [16] McKee et al. [2009] presented two different approaches to analyze the particle backscattering ratio variability. One is a point by point method where the backscattering ratio were calculated by dividing backscattering b bp by the corresponding individual scattering measurement b p and variability was analyzed using descriptive statistics of the particle backscattering ratio distribution. The other one uses regression approach to find the best fit values of b bp /b p for the whole data set. As there are measurement uncertainties in both b bp and b p data, geometric mean regression method, known as the least squares bisector for Model II regression algorithm (E. T. Peltzer, 2009, modify.htm), was used to analyze the spectral variability of the particle backscattering ratio. Nonlinear relationship was found in Figure 8 for the data set with b p (442) > 8 m 1 (usually with C TSM >18mgL 1 ). The nonlinearity may be due to that the material composition varies in some sort of systematic way with increasing turbidity, also possibly due to the performance limitation in the HS 6 sigma correction, or the AC 9 scattering correction or a combination of both. These corrections are a tough test for the data set covering a large range of scattering and backscattering levels. The spectral variability analysis was restricted to the low end data with b p (442) 8m 1 only. The b bp /b p values in Figure 9 are 0.025, , , , with 95% confidence intervals of ± , ± , ± , ± , ± for bands 442, 488, 532, 589, and 676 nm, respectively. The maximum relative error between these five bands is about 14%. [17] The best fit offsets in Figure 9 are unexpected since zero values of b p and b bp should coincide, which may be resulted either from underestimate of b bp or overestimate of b p or a combination of both. The b p and b bp were plotted against C TSM, shown in Figure 10. The positive best fit offsets in the relationship between b p and C TSM reveal overestimate of b p or underestimate of C TSM. The negative offsets between b bp and C TSM reveal underestimate of b bp or overestimate of C TSM.As a result, the nonzero offsets in Figure 9 are possibly resulted from b bp underestimate (" bbp ) and b p overestimate (" bp ). Using the data with b p (442) 8m 1, " bp were calculated from the geometric mean regression lines (Figures 10a, 10c, 10e, 10g, and 10i), which are , , , 0.177, and for bands 442, 488, 532, 589, and 676 nm, respectively. These values of " bbp are , 0.001, , 0.002, and , determined from Figures 10b, 10d, 10f, 10h, and 10j. [18] On one hand, it has been reported that the uncertainty resulted from estimating the backscattering coefficient from a single angle measurement in backward direction can be 10% [Oishi, 1990; Boss and Pegau, 2001; Chami et al., 2006a]. On the other hand, Whitmire et al. [2007] presented that the uncertainty resulted from the acceptance angle (about 0.93 ) for the AC 9 measurement is estimated about 5 25%. Figure 11a shows mean values of backscattering ratio, plotted against wavelength, together with standard deviations and minimum/maximum range values. Figure 11b shows regression determined backscattering ratio as a function of wavelength and associated 95% confidence intervals for the slopes. Given the measurement uncertainties associated with b bp and b p data, the backscattering ratio can be regarded as wavelength independent in the range of nm for waters in YS and ECS. Figure 4c shows that the particles absorb increasingly strongly in the blue. The absorption effect would be to reduce the b bp /b p [McKee et al., 2009]. One 8of11

9 Figure 12. Distribution of (a) n p (589), (b) Chl a (mg m 3 ), (c) C TSM (mg L 1 ), and (d) C ISM (mg L 1 ) along the transect in the Yellow Sea (denoted by transect 1 in Figure 1). possible way in which b bp /b p could remain wavelengthindependent would be if there was a feature in the particle size distribution whose effect countered that of the absorption Implication for Studying Particle Composition [19] It has been presented that oceanic particles span a large range of refractive index which are related to particle composition. The higher water fraction of phytoplankton results in low refractive index, about , relative to water [Carder et al., 1972], while the range for oceanic minerals is about [Aas, 1996]. The analysis described above shows that the offsets in the best fit between the particle backscattering and scattering data are probably resulted from b p overestimate and b bp underestimate. The overestimates " bp and underestimates " bbp were used to recalculate the b p, b bp and hereafter the backscattering ratio. Using the backscattering ratio and c p spectra, particle refractive index (n p ) was calculated by means of the model given by Twardowski et al. [2001]. The profiles of n p and particle concentrations for the two transects (Figure 1) in YS and ECS are shown in Figures 12 and 13. [20] Figure 12 shows that (1) Chl a generally decreases with depth, resulted from the decreased sun irradiance received, (2) C TSM and C ISM increases with depth, resulted from both sedimentation and resuspension, (3) n p increases with depth and has high index near the bottom, consistent with inorganic particles, and (4) n p decreases when Chl a becomes high under the condition that C TSM keeps the same. Figure 13 generally follows the same pattern as Figure Conclusion [21] The main purposes of this study were to analyze the spectral variation of backscattering ratio and to examine the feasibility for studying the particle composition by means of refractive index, calculated from the in situ observations collected during the spring cruise over the Yellow and East China seas in April The measurements of absorption, backscattering, scattering and water component concentration were detailed and the backscattering ratio was analyzed using the geometric mean regression approach. The analysis about relationships between b p, b bp and C TSM showed that 9of11

10 Figure 13. As in Figure 12, but for the transect in the East China Sea (denoted by transect 2 in Figure 1). the unexpected nonzero offsets between backscattering and scattering data are possibly resulted from b p overestimate and b bp underestimate. " bp and " bbp were calculated from the relationships. The backscattering ratio can be regarded as wavelength independent in the range of nm for waters in YS and ECS, given the measurement uncertainties associated with the backscattering and scattering data. The particles absorb increasingly strongly in the blue, then one possible way in which the backscattering ratio could remain wavelength independent would be if there was a feature in the particle size distribution whose effect countered that of the absorption. Using " bp and " bbp, the backscattering ratio was recalculated and hereafter the refractive index. The distributions of the refractive index and water component concentrations along two transects (one in YS and the other in ECS) suggest the feasibility for studying the particle composition. [22] This research just examined the feasibility for studying the particle composition through the refractive index. The quantification of the percentage of each kind of particle has not been realized. The refractive index calculated in this study is the value that reproduces the bulk scattering properties of a particle assemblage. It is equivalent to the average of the individual indices of refraction weighed by the scattering cross sections of all the particles. Using the Mie theory, the refractive index of each kind of particle can be calculated from the particle size distribution [Twardowski et al., 2001], which can be measured by Laser Particle Size Analyzer. Using the estimated refractive index for the whole assemblage, the percentage of each kind of particle can be quantified through weighing their individual refractive index. This study deserves for further research. [23] Acknowledgments. The authors are appreciative to the members of field sample experiment for their hard work during the spring cruise over the Yellow and East China seas. Thanks to two anonymous reviewers for valuable comments on the manuscript. We are grateful to Robert H. Stavn for providing his publications and to David McKee for valuable suggestions about regression method. This research was carried out within the National Basic Research 973 program of China under contract 2009CB References Aas, E. (1996), Refractive index of phytoplankton derived from its metabolite composition, J. Plankton Res., 18, , doi: /plankt/ of 11

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