Light scattering properties and their relation to the biogeochemical composition of turbid...

Light scattering properties and their relation to thebiogeochemical composition of turbid productive

waters: a case study of Lake Taihu

Deyong Sun,1,* Yunmei Li,1 Qiao Wang,1 Jay Gao,2 Heng Lv,1 Chengfeng Le,1

and Changchun Huang1

1Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education,JiangSu Nanjing, China

2School of Geography and Environmental Science, Private Bag 92019, University of Auckland, New Zealand

*Corresponding author: [email protected]

Received 4 November 2008; revised 10 March 2009; accepted 11 March 2009;posted 16 March 2009 (Doc. ID 103590); published 1 April 2009

Light scattering properties in such a highly turbid productive lake as Lake Taihu in China were exam-ined through 118 samples collected during three cruises in November 2006, March 2007, and November2007. The particulate scattering and backscattering coefficients were observed usingWETLabs AC-S andECO-BB9. A power model with a spectral exponent of −0:729 was used to simulate the particulate scat-tering coefficient (bp) spectra. It has a better performance than the linear model. Scattering parametersare more closely related to inorganic suspended matter (ISM) concentration than to other water compo-nents, such as total suspended matter (TSM), organic suspended matter (OSM), and chlorophyll a (Chla).This indicates that ISM dominates the scattering signal in the lake. Three discrepancies with oceanic/coastal waters are observed: (a) the backscattering ratio (~bbp) decreases with an increase in the ISMconcentration because of a highly strong contribution by ISM to bp; (b) the mass-specific scattering coef-ficient (bpm) exhibits a wider range of variability than that reported in previous studies, which can beattributed to considerable variation in the OSM and ISM distributions; (c) the particle size distributionslope (ξ) is mostly larger than 4.0 in Lake Taihu, whereas it is usually within 3.5–4.0 for marine particles.In addition, the bulk refractive index (�np) calculated according to the Twardowski et al. model [J. Geo-phys. Res. 106, 14129 (2001)] indicates that some stations (�np < 1:07) can be regarded as organic-particledominant. Other stations with high ISM concentrations have a very small �np value mostly within1.10–1.17. Overall, the knowledge on the scattering properties gained in this study broadens our under-standing of water optics in highly turbid productive water columns. © 2009 Optical Society of America

OCIS codes: 290.0290, 280.1350.

1. Introduction

Detailed understanding of light scattering propertiesin various types of water columns is of fundamentalimportance for remotely sensing water color and forrelated science development. Light scattering is gen-erally the first-order determinant of remote sensingreflectance (Rrs) variability in water columns. This isattributed mainly to the backscattering coefficient

(bb) of a water column representing the backwardcomponent of the scattering coefficient (b). Becausepure water has a constant backscattering coefficientat a given wavelength, the particulate backscatter-ing coefficient (bbp) controls bb variability. The contri-bution of bb to Rrs can be clearly seen from theapproximation of the radiative transfer model [1,2].The variability of Rrs is also controlled by the parti-culate backscattering ratio (~bbp ¼ bbp=bp), which caneven be the main factor controlling theRrs variabilityas stated by Mobley et al. [3] and Lubac and Loisel[4]. Besides, light scattering is related closely to

0003-6935/09/111979-11$15.00/0© 2009 Optical Society of America

10 April 2009 / Vol. 48, No. 11 / APPLIED OPTICS 1979

the nature of particulates in a water column. Parti-culate scattering and backscattering coefficients aretwo quantities that have been traditionally used toquantify in situ particulate concentration, and ~bbpcan provide useful information on the characteristicsof the particulate material, such as bulk refractiveindex and particle size and composition [5]. Becauselight scattering of suspended particles in water col-umns is of such a great significance, it has attractedattention in the literature.At present, light scattering of suspended particles

has been measured mainly for oceanic and coastalwaters [5–8]. Little research has been carried out ininland turbid productive lakes. Since different envir-onments of water columns may make light scatteringproperties vary widely, light scattering properties inturbid productive lakes differ from those in oceanicand coastal waters.This study focuses on light scattering parameters

in the highly turbid productive Lake Taihu as mea-sured during three cruises. The main purpose is todescribe and to demonstrate the variability of bp(particulate scattering coefficient), bbp, and ~bbp inthis optically complex lake, characterized by a highlevel of suspended matter concentration. In particu-lar, the main aim is to find a suitable model for de-picting the particulate scattering spectra in the lakeand to compare the obtained results with other find-ings. In addition, the relationship between scatteringparameters and water component concentrationswas also analyzed, and the bulk refractive index andparticle size distribution are also presented and dis-cussed with respect to the composition of the parti-culate assemblage.

2. Materials and Method

A. Study Area

Lake Taihu is one of five major freshwater lakes inChina. As a typically shallow inland eutrophic lake,it has an area of 2338km2 and an average depth ofabout 2m [9]. Chlorophyta, Bacillariophyta (dia-toms), and Cyanophyta constitute the major phyto-plankton components. Periphytic algae are mostvaried and abundant in spring and least in winter[10]. Because of its shallow depth and wave effect,sediment resuspension strongly affects particulatecomposition in water columns [11].

A total of 118 groups of bio-optical data were col-lected during three cruises in November 2006 (a),March 2007 (b), and November 2007 (c). The stationsat which surface measurements were carried outwere distributed regularly throughout the lake(Fig. 1). In cruises (a) and (c), 103 groups of data werecollected at 74 stations (some stations were mea-sured twice in both cruises). Cruise (b) was confinedto 15 stations in the Meiliang Bay in the north ofLake Taihu. At each station the optical measurementwas performed on the surface water, and water sam-ples were collected using Niskin bottles. These sam-ples were immediately preserved under a lowtemperature (2 − 4°) and taken to a lab for analysiswithin the same day.

B. Water Sample Analysis

Water component concentrations were measured ac-cording to the investigation criteria for lakes inChina [12]. To determine the concentration level oftotal suspended matter (TSM), 0:45 μm GF/C filterswere precombusted at 550° for four hours to remove

Fig. 1. Location of the study area in China and distribution of sample stations. N represents latitude north, and E represents longitudeeast.

1980 APPLIED OPTICS / Vol. 48, No. 11 / 10 April 2009

organic traces. After cooling, the filters were pre-weighed at about 45° and then stored in numberedPetri dishes before use. After filtration and rinsing,the filters were stored at −25°. They were thenthawed in the laboratory, dried at 105° for four hours,and reweighed at about 45°. The total suspendedmatter weight was the difference between the formerand the latter, and TSM concentration was obtainedby dividing the weight by volume. The filters werethen recombusted at 550° for four hours in order toremove the organic fraction, and weighed again to ob-tain inorganic suspended matter. So the concentra-tion of inorganic suspended matter (ISM) wasobtained. The concentration of organic suspendedmatter (OSM) was the difference between TSM andISM. Chla was extracted with ethanol (90%) at 80°and analyzed spectrophotometrically at 750nm and665nm, with correction for phaeopigments [13].

C. Scattering and Backscattering Measurements

The particulate scattering coefficient was measuredwith a WETLabs AC-S [14]. It has 85 spectral chan-nels in the visible light range at a spectral resolutionof about 4nm and a measurement accuracy of�0:01m−1. Prior to the field experiments, the AC-Swas calibrated with pure water and air to confirmthat its performance was within factory specifica-tions (�0:005m−1). To obtain accurate absorptionand attenuation, temperature correction was per-formed following Pegau et al. [15] and Sullivanet al. [16] (salinity correction was not needed forthe freshwater). Particulate absorption was cor-rected for scattering errors using the spectral scatter-ing and the measured value at the last wavelength ofAC-S (third method of Zeneveld et al. [17]). Becausedissolved matter in water has a negligible effect onscattering, the particulate scattering can be com-puted from the difference between attenuation andabsorption:

bp ¼ cpþg − apþg; ð1Þ

where cpþg is the attenuation coefficient of particu-lates and dissolved matter; apþg is the absorptioncoefficient of particulates and dissolved matter. Theyare both directly obtainable by AC-S.The particulate backscattering coefficient was ob-

tained by using a WETLabs ECO-BB9 [18]. TheECO-BB9 measures scattering at a single angle inthe backward direction (117°) at nine wavelengths:412nm, 440nm, 488nm, 510nm, 532nm, 595nm,660nm, 676nm and 715nm. It had a measurementaccuracy of �0:005m−1. The collected data were pro-cessed in the following way.

1. Since the calibration of the ECO-BB9 uses mi-crospherical scatters, the component of attenuationattributable to scattering was incorporated intothe scaling factor, i.e., the calibration itself:

βmeasð117°Þ ¼ sf · ðoutput − dcÞ; ð2Þ

where sf and dc are, respectively, calibration scalefactor and signal obtained by covering the detectorwith black tape and submerging the sensor in water(both are supported by the factory); output is themeasured signal output of the scattering meter;βmeasð117°Þ is the calculated raw total volume scat-tering coefficient (m−1 sr−1).

2. Absorption of the incident beam was includedin the correction as below:

βcorrð117°Þ ¼ βmeasð117°Þ expð0:039aÞ; ð3Þ

where βcorrð117°Þ is the corrected total volume scat-tering coefficient (m−1 sr−1); a is the correspondingtotal absorption coefficient that can be obtained bythe AC-S measurement plus pure water absorp-tion (m−1).

3. The volume scattering of water was subtractedfrom the total volume scattering coefficient to obtainthe volume scattering coefficient of particles:

βpð117°Þ ¼ βcorrð117°Þ − βwð117°Þ; ð4Þ

where βpð117°Þ is the particulate volume scatteringcoefficient (m−1 sr−1); βwð117°Þ is the volume scatter-ing coefficient of pure water (m−1 sr−1) that was ob-tained from the relationship proposed by Morel [19].

4. The particulate backscattering coefficient bbpwith a unit of m−1 was determined through estima-tion from the single measurement of βpð117Þ° usingan X factor:

bbp ¼ 2πXβpð117°Þ: ð5Þ

From measurements of the volume scatteringfunction with a high angular resolution in diversewater types, Boss and Pegau [20] determined X tobe 1.1. This factor estimates bbp with an uncertaintyof 4 percent.

D. Calculation of Bulk Refractive Index and Particle SizeDiscrimination Slope

The particulate attenuation coefficient (cpðλÞ) was ob-tained by the AC-S and the dissolved absorptionmeasurements performed according to the filteringmethod by Bricaud et al. [21], Green and Blough [22],and Keith et al. [23]. The AC-S can measure cpþg. Socpþg minus the dissolved absorption (agðλÞ) approxi-mately equals cpðλÞ given the weak scattering of thedissolved matter. Although particulate absorptionspectra apðλÞ generally have strong spectral depen-dencies (i.e., peak and dip) in aquatic systems,particulate attenuation spectra cpðλÞ are characteris-tically smooth functions composed of apðλÞ and bpðλÞ.This is because shape structure in an absorptionspectrum is compensated by the inverse of such astructure in the scattering spectrum. The cpðλÞ spec-tra can be well described by a hyperbolic model[24–26]:


cpðλÞ ¼ Aλ−γ; ð6Þ

where γ is the hyperbolic slope of the spectrum, and Ais the corresponding amplitude. γ is related closely tothe hyperbolic shape of the particle size distribution(PSD) ξ. Their relationship is often described as ξ ¼γ þ 3. However, for more general cases of PSDs withfinite limits, the following relationship is more accu-rate [27,28]:

ξ ¼ γ þ 3 − 0:5 expð−6γÞ: ð7ÞIt can provide a significant correction only whenlarge particles are abundant, as when sediment re-suspension occurs. So this relationship was used toestimate parameter ξ.The bulk refractive index �np was calculated accord-

ing to the analytical model developed by Twardowskiet al. [24] as the variations in ξ and ~bbp meet the con-ditions of 2:5 ≤ ξ ≤ 4:5 and 0 ≤ ~bbp ≤ 0:03:

�np ¼ 1þ ~bbp0:5377þ0:4867ðξ−3Þ2 ½1:4676þ 2:2950ðξ − 3Þ2

þ 2:3113ðξ − 3Þ4�: ð8Þ

3. Results and Discussion

A. Variations of Bio-Optical Parameters

There is a great variability in the concentrationof water components (TSM, OSM, ISM, and Chla)(Table 1), which indicates the presence of complexwater column conditions. Lake Taihu waters arehighly turbid judging from the total suspended mat-ter (TSM) variation that ranges from 6.47 to143:47mg 1−1 with a mean of 33:92mg 1−1 (SD ¼24:18mg 1−1). Mean TSM concentration is muchhigher than that of other turbid water bodies, suchas Chesapeake Bay [29], sandpit lakes and reservoirsin eastern Nebraska (USA) [30], and some other USlakes [31]. The Chla concentration varies between0.98 and 408:69mgm−3, the most variable amongall water components (CV ¼ 178%). These large var-iations of water component concentrations strongly

affect water scattering parameters. Particulate scat-tering coefficient bpð532Þ ranges from 3.32 to48:09m−1, with a mean of 20:17m−1 (SD ¼9:35m−1). The backscattering ratio ~bbpð532Þ variesbetween 0.005 and 0.027 (mean value, 0:013�0:005 in Lake Taihu waters.

B. Spectral Dependence of bp, cp, bbp, and ~bbp

At present two models are usually used to simulatethe particulate scattering spectra and linear andpower functions [6,8,32]. Both models were triedfor the highly turbid and productive Lake Taihuwaters. The dataset was divided into two parts,one (78 samples) for model development and another(40 samples) for model testing. While the model de-velopment dataset has a bpð532Þ variability of3:32∼ 48:09m−1 ðmean ¼ 19:85m−1Þ, the bpð532Þranges from 4:84m−1 to 45:00m−1 with a mean valueof 20:83m−1 in the model test dataset. The bpð555Þwas selected as the reference because it is locatednear the middle of the wavelength range of AC-Sand canminimize errors from a too broad wavelengthinterval in the regression analysis. First, bpð555Þwasused to model the scattering coefficients at all otherwavelengths, i.e., by Eq. (9). The goodness of fit israther satisfactory, with R2 ≥ 0:998 at all 84 wave-lengths. Thus, the parameter MðλÞ in Eq. (9) couldbe obtained:

bpðλÞ ¼ MðλÞbpð555Þ: ð9Þ

Then the linear and power functions were both usedto fit the relationship between MðλÞ and wavelengthλ. The linear and power models achieved R2 of 0.989and 0.996, respectively. As a result, two modelsEqs. (10) and (11) were obtained for simulating theparticulate scattering spectra:

bpðλÞ ¼ ð−0:0012λþ 1:7037Þbpð555Þ; ð10Þ

bpðλÞ ¼ bpð555Þ� λ555

�−0:729

: ð11Þ

In order to compare the two scattering models, thetest dataset was used to carry out error analysis.The mean absolute percentage errors (MAPE) be-tween the modeled and measured scattering valueswere calculated using Eq. (12),

MAPE ¼ 1nΣn

i−1

��bpðλÞi − bpðλÞ0ibpðλÞi

��; ð12Þ

where bpðλÞ0i and bpðλÞi are the modeled and mea-sured scattering coefficient of sample i, respectively.As shown in Fig. 2, the twomodels are almost equallyaccurate in the spectrum range of about 400–475 nmexcept that the linear model has a minor error peakat about 410nm. Nevertheless, in the relatively

Table 1. Descriptive Statistics of the Optically Active Substances andScattering Parametersa

Statistics Minimum Maximum Mean SD CV (%)

TSM ðmg 1−1Þ 6.47 143.47 33.92 24.18 71OSM ðmg 1−1Þ 3.20 118.67 15.46 17.81 115ISM ðmg 1−1Þ 2.47 70.40 18.46 12.26 66Chla ðmgm−3Þ 0.98 408.69 40.59 72.11 178bpð532Þ ðm−1Þ 3.32 48.09 20.17 9.35 46bbpð532Þ ðm−1Þ 0.09 0.25 0.22 0.03 14~bbpð532Þ 0.005 0.027 0.013 0.005 40aIncluding Chla (chlorophyll-a), TSM (total suspended matters),

OSM (organic suspendedmatters), ISM (inorganic suspendedmat-ters), bpð532Þ, bbpð532Þ, and~bbpð532Þ (particulate scattering coef-ficient, backscattering coefficient, and backscattering ratio at532nm); SD, standard deviation; CVð¼SD=Mean of parameter),coefficient of variation in %.


broad spectrum range of 475–750 nm the power mod-el has a smaller error than the linearmodel. Thus thepower model performs better than the linear model.The MAPE of the power model ranges from 0.1% to4.2% at all wavelengths, with a mean value of 2.1%(standard deviation, �1:2%).The wavelength dependency slopes of both models

are similar to the results obtained in other studies.For example, after studying the spectral shape ofbpðλÞ in case 2 waters (yellow-substance-dominatedand mesotrophic), Morel et al. [33] found that thenormalized scattering spectra correspond approxi-mately to λ−0:6 dependency, which is very similar tothe slope of the power model (−0:729) in this study.The empirical parameters (−0:0012, 1.7037) of thelinear model [see Eq. (10)] are quite similar to those(−0:00113, 1.62517) reported by Gould et al. [32] forclear, open-ocean case 1 waters and turbid, case 2coastal waters. To some extent, this similarity be-tween different study areas supports the generaliza-tion that the spectral dependency of bpðλÞ generallychanges little in some so-called case 2 waters, andsometimes even in case 1 waters. However, the excep-tional case of Snyder et al. [8] showed a considerabledegree of variation existing in the particulate scat-tering spectra in US coastal waters. Their particu-late scattering power-law spectral exponents variedbetween −1:0 and 1.0, with an average value of −0:48.This demonstrates that more measurements in di-verse water columns are needed to further under-stand the variability of the particulate scatteringspectra.The established cpðλÞ spectral model Eq. (13) has a

better performance with high simulation accuracy(R2≧0:998) than other models. The spectral slopeof −1:066 indicates that the cpðλÞ spectra are steeperthan the bpðλÞ spectra. It is worthy to note that thedeveloped cpðλÞ spectral model is different fromthe Eq. (6) model. Although the Eq. (6) model can re-late cpðλÞ directly to λ, it has more parameters to

determine and is not applicable for modeling thecpðλÞ spectra:

cpðλÞ ¼ cpð555Þ� λ555

�−1:066

: ð13Þ

To model the bbpðλÞ spectra, the particulate backscat-tering spectral data were fitted with a similar power-law function. The 532nm was selected as the refer-ence wavelength, and the obtained spectral slope is−3:064 [Eq. (14)]. The bbpðλÞ spectral model also has ahigh simulation accuracy (R2≧0:848). Figure 3 showsthe measured and simulated bp, cp and bbp spectra ata station. They are representative of the spectral da-taset. Comparatively, the bbpðλÞ spectra are steeperthan the bp and cp spectra in Lake Taihu waters.All of them can be well modeled by the power-lawfunction, as reported by Snyder et al. [8]. With thedeveloped backscattering and scattering spectralmodels, it is possible to derive the particulate back-scattering ratio model by dividing bbp by bp [Eq. (15)],resulting in a wavelength-dependent slope ofparticulate backscattering ratio (−2:335) in LakeTaihu. Several studies have reported wavelength-dependency of particulate backscattering ratios.For instance, Chami et al. [34] presented a spectralvariability of ~bbp in the Crimea coastal waters, andthe largest average difference of 0.08 occurs between490nm and 555nm, which is larger than that of LakeTaihu (∼0:03). McKee and Cunningham [35] andChami et al. [36] also reported the existence of the~bbp spectral dependency. However, Whitmire et al.[37] found no statistical evidence to support a wave-length-dependent backscattering ratio using a data-set collected in Crater Lake, New York Bight,southern California/Mexico, and Gulf of Californiacoastal waters.

bbpðλÞ ¼ bbpð532Þ� λ532

�−3:064

; ð14Þ

Fig. 2. MAPE of linear and power models describing the particu-late scattering spectra.

Fig. 3. Measured and simulated bp, cp, and bbp spectra at astation.


~bbpðλÞ ¼ ~bbpð532Þ� λ532

�−2:335

: ð15Þ

C. Relationship between Scattering Parameters andWater Components

Table 2 shows the correlations between scatteringparameters and water component concentrations.The relationship between ISM and bpð532Þ, bbpð532Þ,and ~bbpð532Þ is closer than that between ISM andother water components, including TSM, OSM,and Chla. This indicates that ISM dominates mostlywater scattering characteristics in the lake. The cor-relation coefficients of the above pairs are 0.888,0.549, and −0:784, respectively (Table 2). Threepower functions with different coefficients were usedto model the relationships [Fig. 4; Eqs. (16)–(18)].The models of bpð532Þ versus ISM and ~bbpð532Þ ver-sus ISM performed well with high determinationcoefficients, whereas the model of bbpð532Þ versusISM had a relatively poor performance (R2 ¼ 0:432):

bpð532Þ ¼ 3:355½ISM�0:626 ðR2 ¼ 0:797Þ ; ð16Þ

bbpð532Þ ¼ 0:140½ISM�0:161 ðR2 ¼ 0:432Þ ; ð17Þ

~bbpð532Þ ¼ 0:042½ISM�−0:465 ðR2 ¼ 0:757Þ : ð18ÞIt is noteworthy that the correlation coefficients be-tween ~bbpð532Þ and water component concentrationsare all negative, which also present no statisticallysignificant correlations [except ISM; see Eq. (18)].This is similar to that reported by Snyder et al. [8],who found negative correlations between ~bbpð550Þand the permutations of OSM, ISM, and TSM inUS coastal waters. As to the relationship betweenthe backscattering ratio and chlorophyll concentra-tion, all previous results point to weak empirical re-gression relationships [7,37–40], which has beencorroborated by this study (Fig. 5). The obtained re-lationship [see Eq. (19)] has a very low determinationcoefficient:

~bbpð532Þ ¼ 0:016½Chla�−0:097 ðR2 ¼ 0:103Þ : ð19Þ

There is a weak relationship between the backscat-tering ratio and Chla concentration in the lakewaters. This finding is consistent with those reportedin previous studies, even though they cover different

Table 2. Correlations between Scattering Parameters and WaterComponent Concentrationsa

TSM OSM ISM Chla bpð532Þ bbpð532Þ ~bbpð532ÞTSM 1OSM 0.873 1ISM 0.705 0.269 1Chla 0.619 0.675 0.240 1bpð532Þ 0.693 0.329 0.888 0.285 1bbpð532Þ 0.483 0.278 0.549 0.243 0.664 1~bbpð532Þ −0:636 −0:325 −0:784 −0:274 −0:905 −0:651 1

aThe former includes bpð532Þ, bbpð532Þ, and~bbpð532Þ, and thelatter includes TSM, OSM, ISM, and Chla concentrations.

Fig. 4. Scatterplots of ISM and scattering parameters: (a) bpð532Þversus ISM, (b) bbpð532Þ versus ISM, (c) ~bbpð532Þ versus ISM.


ranges of chlorophyll concentration. For example, thechlorophyll concentration in Whitmire et al. [37]ranges from undetectable to over 12mgm−3. Theauthors established a power-law model with a deter-mination coefficient of R2 ¼ 0:36 using all data. TheSullivan et al. [40] dataset encompasses a large num-ber of high chlorophyll concentrations up to100mgm−3. Their model of ~bbp ¼ 0:013½Chl�−0:216has an R2 value of 0.33. The Chla concentration inthis study ranges from 0:97mgm−3 to 408:69mgm−3.The consistent finding (i.e., a weak relationship be-tween ~bbp and chlorophyll concentration) suggeststhat chlorophyll-containing particles have little ef-fect on ~bbp in a water column. This is also confirmedby the loose relationship between ~bbp and Chla : cpratio in our study (not shown). The ratio can oftenbe considered as an indicator of the nature of thebulk particulate matter [41]. The established rela-tionship between ~bbp and the Chla : cp ratio is alsosimilar to that in the previous studies [5,7].Current knowledge suggests that low bbp : bp

values are expected for a particulate populationdominated by low refractive index materials suchas phytoplankton, whereas high bbp : bp values aregenerally observed in waters of a relatively highconcentration of inorganic particles [7]. However,no researchers have reported on the empirical rela-tionships between the bbp : bp ratios and inorganicparticulate concentrations. Snyder et al. [8] foundno discernible evidences to suggest that larger back-scattering ratios are on average associated with bothhigher mineral fractions and higher OSM/Chla ra-tios. They speculated that other factors (e.g., particlesize and/or shape) must also influence the backscat-tering ratios in their study. In this study a power-lawrelationship was fitted between ~bbpð532Þ and ISM. Adecreasing trend is found with increasing ISM[Fig. 4(c)]. This is not surprising given the highlystrong contribution of ISM to particle scattering.With an increase in the quantity of ISM in watercolumns, both scattering and backscattering coeffi-cients increase, but the scattering coefficient in-

creases much more than the backscatteringcoefficient [see Eqs. (16) and (17)], resulting inthe decrease in bbp=bp (i.e., ~bbp). The negativecorrelations between ~bbp and suspended matter con-centrations may be characteristic of highly turbidproductive waters, at least for the investigated watercolumn. TSM among all the stations ranges from6:5mg 1−1 to 143:5mg 1−1 with a mean value of33:9� 24:2mg 1−1, which is higher than that re-ported in previous studies [7,8]. More than half(64%) of the stations have an ISM larger than OSM,an important reason for the fact that the particlescattering characteristics are mostly dominatedby ISM.

Another key factor could be the presence of multi-ple scattering events in the lake. To identify whetherthis is true, the contribution of multiple scattering toradiance reflectance is calculated according to the re-lationship between 1 − L1=Ltot and bb=a developed byChami et al. [42], where L1 corresponds to the radi-ance resulting from single-scattering events, Ltotcorresponds to the total radiance, a is the total ab-sorption coefficient, and bb is the total backscatteringcoefficient. Results show that the radiance contribu-tion by multiple scattering is much higher than 60%at most stations, and the mean contribution standsat 81% (Fig. 6). This indicates that multiple scatter-ing events strongly affect the underwater light field.In the investigated turbid waters, the higher contri-bution of ISM to particulate scattering but not to par-ticulate backscattering can be explained mostly bymultiple scattering events. This is also consistentwith the finding that scattering in forward directionsis the dominant contributor to reflectance signals inturbid waters in light of multiple scattering events[42]. Additionally, multiple scattering in the lake af-fects the particulate backscattering measurement.

Fig. 5. Scatterplots of Chla and backscattering ratio ~bbpð532Þ.

Fig. 6. Distribution of the radiance contribution by multiple scat-tering at all stations.


From the observed bbp data it is found that the max-imal bbp only reaches 0:25m−1 at 532nm, eventhough particulate scattering coefficients are rela-tively high. Since multiple scattering is influencedby many important factors, more experiments areneeded to further understand multiple scatteringevents in highly turbid lake waters.There is a strong correlation between the particu-

late scattering coefficient and TSM (Table 2), whichcan be described by a simple regression model. Themass-specific scattering coefficient (bpmðλÞ ¼ bpðλÞ=TSM, m2 g−1) is usually considered as an importantparameter reflecting the nature of particles in awater column. In general, the mass-specific scatter-ing coefficient is lower for inorganic particles thanthat of organic particulate assemblages [6]. Pre-viously observed bpm values range from 0:1m2 g−1to 0:8m2 g−1 in coastal waters, lakes, and within hy-drothermal plumes [[43–45]]. As reported by Babinet al. [6], bpm values in several regions average 1.0and 0:5m2 g−1 in case 1 and case 2 waters, respec-tively. Song and Tang [46] reported bpm to vary from0.25 to 0:46m2 g−1 in the Huanghai Sea and EastChina Sea. In this study the observed bpm value(the reference wavelength is set to 555nm forconvenience of comparison) ranges from 0.14 to1:57m2 g−1, with a mean of 0:67� 0:22m2 g−1. Thisvariation was much larger than the above observedresults. Therefore, some stations are highly abun-dant in organic particulate assemblages, and othersin inorganic particles. This assumption is also veri-fied by the measured OSM and ISM distributions.Figure 7 shows large differences between ISM andOSM at some stations. The ratios of OSM : ISM orISM : OSM are all higher than 3, and even up to 6at separate stations. Overall, the various particu-lates, including phytoplankton, detritus, nonlivingorganic particles, and minerals (usually summarizedinto organic and inorganic particles), have an unevendistribution, which causes a large variation in themass-specific scattering coefficient.

D. Backscattering Ratio, Bulk Refractive Index, andParticle Size Distribution

The measured particulate backscattering ratio (~bbp)is plotted against PSD slope (ξ), with the bulk refrac-tive index values calculated from the Twardowskiet al. (2001) model as a background (Fig. 8). ~bbp variesbetween 0.005 and 0.027, similar to that in the pre-vious studies. For example, Boss et al. [5] reported a~bbp range of 0.005–0.035 on the New Jersey, USAcoast. Similar measurements in different waterbodies show a wide range of ~bbp values (Table 3).Lake Taihu waters have a smaller range of ~bbp varia-bility, which is strongly related to the refractive in-dex of particles and particle size distribution,which is discussed below.The bulk refractive index �np estimated in this

study spans a range of 1.02–1.17 (Fig. 8), whichshows no very large variation in comparison withthe previous studies [5,7,40]. This suggests a relative

assemblage of the light refraction capability of parti-cles in the lake. Many bulk refractive indices are low-er than 1.07. Live phytoplankton, organic detritusparticles, viruses, heterotrophic bacteria, and othercomponents (e.g., different strains and species) typi-cally have a low refractive index value relative towater (approximately 1.02–1.07) because of theirhigh water content [[47–50]]. Thus, the stations witha value below 1.07 can be regarded as organic-parti-cle dominant. This result is also consistent with thepresence of high organic suspended matter concen-trations at these stations. The stations with highISM/TSM ratios have a distinct �np variability witha higher value than that of high OSM/TSM ratio sta-tions. While the maximal �np is 1.17 at stations withhigh ISM/TSM ratios, the stations with high OSM/TSM ratios have the highest �np value of 1.11. Corre-spondingly, their mean values are 1.07 and 1.04,respectively. Although some other stations also have

Fig. 7. OSM and ISM distribution at some stations. The differ-ences between ISM and OSM at these stations are very big (here,OSM : ISM or ISM : OSM ratios ≥3). The serial numbers on the xaxis are not meaningful and just temporary numbers for the sta-tions of big difference between ISM and OSM.

Table 3. Variations of Particulate Backscattering Ratio in SeveralTypical Studies

Study AreasVariationRanges b References

New Jersey coast 0.005–0.035 Boss et al. [5]Irish Sea 0.005–0.050 McKee and

Cunningham [56]eastern EnglishChannel and southernNorth Sea

0.0024–0.0417 Loisel et al. [7]

Global dataset a 0.005–0.060 Whitmire et al. [37]US coastal waters 0.005–0.060 Snyder et al. [8]Lake Taihu waters 0.005–0.027 This studyaGlobal dataset includes the data collected in Gulf of California,

Mid-Atlantic Bight, and Crater Lake.bParticulate backscattering ratio is usually considered to have

no spectral dependence [37]. Thus we believed that the ~bbp resultsbased on different reference wavelengths in different referencescould be compared together.


relatively high inorganic suspended matter concen-trations, their corresponding bulk refractive indexvalues are not very high, mostly in the range of1.10–1.17. This indicates that there are less hardmaterials in particles, such as calcite, mica, and am-phibole, whose refractive index is typically high(approximately >1:20) [51]. Inorganic suspendedmatter in the lake may be composed mainly of clayand feldspar (∼1:14) by sediment resuspension andamorphous silica (∼1:08), used for diatom (importantalgae in Lake Taihu waters) exostructures [51].Particle size in a water column is a property af-

fected by many processes, for example, biologicalgrowth, flocculation, aggregation, disaggregation,and sedimentation. These different processes domi-nate at different size scales [52]. If particle size is ap-proximately less than 1 μm, the coagulation processis dominated by Brownian motion and the PSD slopehas a theoretical value of 2.5; in the range of1 − 100 μm, shear coagulation is regarded as thedominant process and the resulting PSD slope isin the vicinity of 4; for particles with a larger size,differential sedimentation and gravitational settlingprocesses mainly affect the particle size distribution,which has a theoretical slope >4:5 [52,53]. The mea-sured ξ varies from 3.7 to 4.5 with a mean value of 4.1(SD, �0:15) (Fig. 8). This indicates that the particlesize of Lake Taihu waters is usually in the range of1 − 100 μm, and there are fewer particles of a muchsmaller or larger size. For marine particle popula-tions, ξ typically varies between 2.5 and 5 with mostvalues falling within 3.5–4.0 [54]. This range withmost values can also be seen clearly from figure 4in Boss et al. [5] and figure 9 in Loisel et al. [7], eventhough there is a relatively high frequency of occur-

rence of ξ > 4:0 during the BIOSOPE cruise [55].However, in Lake Taihu waters most ξ values (76%)are larger than 4.0, an important difference in parti-cle size distribution between Lake Taihu waters andmarine waters.

4. Conclusion

This study compares two methods for simulating theparticulate scattering spectra of highly turbid andproductive Lake Taihu waters. Results show thatthe power model outperforms the linear model. Thepower-law spectral exponent of Lake Taihu wasfound to be −0:729, comparable to corresponding re-sults in some previous studies. Particulate attenua-tion, backscattering, and backscattering ratiospectra models were also similarly established. Therelationship between ISM and bpð532Þ, bbpð532Þ, and~bbpð532Þ is tighter than that between ISM and otherwater components (TSM, OSM, and Chla), which in-dicates that ISM dominates mostly water scatteringin Lake Taihu. However, a negative correlation existsbetween ~bbpð532Þ and ISM, which is different fromcommon cases in oceanic and coastal waters consid-ering that high ~bbp values are more commonly asso-ciated with high inorganic particle concentrationareas. This can be attributed to a highly strong con-tribution of ISM to bp and a relatively weak contri-bution to bbp in Lake Taihu waters.

The observed bpm values varied between 0.14 and1:57m2 g−1. This range of variability is wider thanthat in other water columns because of considerablevariation in the observed OSM and ISM distribu-tions. Judging from the bulk refractive index andparticle size distribution, particle composition, andsize of Lake Taihu have a small variability. �np varieswithin a relatively narrow range of 1.02–1.17 in com-parison with the previous findings and the calculatedξ ranges from 3.7 to 4.5 with a mean value of 4.1 (SD,�0:15). The larger than 4.0 value for most ξ in LakeTaihu is different from the mostly within 3:5 − 4:0values for marine particle populations. In summary,this study reveals that there exist some discrepan-cies in light scattering properties between LakeTaihu and oceanic/coastal waters owing to the varia-bility of particulate composition, size, and concentra-tion in different water columns.

This research was supported by a National Natur-al Science Foundation (NNSF) grant (40571110) anda National Science and Technology Support Project(2008BAC34B05) of China. Additional financial sup-port was received from the Foundation of GraduateInnovation Training plan of Jiangsu Province(CX08B_015Z) and an excellent thesis cultivationfund of Nanjing Normal University (181200000220).Lan Wu, Xin Wang, Kun Shi, Rui Xia, Yu Yang, XinJin, and Yanfei Wang are acknowledged for their par-ticipation in the field experiment. We are grateful totwo anonymous reviewers for their useful commentsand suggestions.

Fig. 8. Particulate backscattering ratio (~bbp) versus PSD slope (ξ)in Lake Taihu waters (the reference wavelength is 532nm). Thesolid curves overlaid on this plot represent the refractive indexcontours as calculated by the model of Twardowski et al. (2001).The refractive index contours shown range from 1.02 (bottom)to 1.20 (top) with a gradation of 0.02. The open triangles and blackrhombuses represent the sets of data with high ratios ofOSM=TSM > 0:5 and ISM=TSM > 0:5, respectively.


References

1. H. R. Gordon, O. B. Brown, and M. M. Jacobs, “Computed re-lationship between the inherent and apparent optical proper-ties of a flat, homogeneous ocean,” Appl. Opt. 14, 417–427(1975).

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

3. C. D. Mobley, L. K. Sundman, and E. Boss, “Phase functioneffects on oceanic light fields,” Appl. Opt. 41, 1035–1050(2002).

4. B. Lubac and H. Loisel, “Variability and classification ofremote sensing reflectance spectra in the Eastern EnglishChannel and southern North Sea,” Remote Sens. Environ.110, 45–58 (2007).

5. E. Boss, W. S. Pegau, M. Lee, M. Twardowski, E. Shybanov,G. Korotaev, and F. Baratange, “Particulate backscattering ra-tio at LEO 15 and its use to study particle composition anddistribution,” J. Geophys. Res. 109, C01014 (2004).

6. M. Babin, A. Morel, V. Fournier-Sicre, F. Fell, and D. Stramski,“Light scattering properties of marine particles in coastal andopen ocean waters as related to the particle mass concentra-tion,” Limnol. Oceanogr. 48, 843–859 (2003).

7. H. Loisel, X. Mériaux, J.-F. Berthon, and A. Poteau, “Inves-tigation of the optical backscattering to scattering ratio ofmarine particles in relation to their biogeochemical composi-tion in the eastern English Channel and southern North Sea,”Limnol. Oceanogr. 52, 739–752 (2007).

8. W. A. Snyder, R. A. Arnone, C. O. Davis, W. Goode, R.W. Gould,S. Ladner, G. Lamela, W. J. Rhea, R. Stavn, M. Sydor, andA. Weidemann, “Optical scattering and backscattering by or-ganic and inorganic particulates in U. S. coastal waters,” Appl.Opt. 47, 666–677 (2008).

9. B. Q. Qin, W. P. Hu, and W. M. Chen, The Process and Mecha-nism of Water Environment Evolvement in Taihu Lake(Science Press, 2004).

10. X. F. Yuan, H. H. Shi, and X. R. Wang, “Temporal and spatialdistributions of periphytic algae in Lake Taihu,” J. Agro-Environ. Sci. 25, 1035–1040 (2006).

11. Y. L. Zhang, B. Q. Qin, W. M. Chen, and L. C. Luo, “A study ontotal suspended matter in Lake Taihu,” Resource Environ.Yangtze Basin 13(3), 266–271 (2004).

12. X. Huang, Eco-Investigation, Observation and Analysis ofLakes (Standard Press of China, 1999), pp. 77–99.

13. Y. W. Chen, K. N. Chen, and Y. H. Hu, “Discussion on possibleerror for phytoplankton chlorophyll-a concentration analysisusing hot-ethanol extraction method,” J. Lake Sci. 18,550–552 (2006).

14. C. Moore, A. Barnard, and D. Hankins, “Spectral Absorptionand Attenuation Meter (ac-s) User’s Guide, Revision A (WETLabs, 2004), pp. 5–20.

15. W. S. Pegau, D. Gray, and J. R. V. Zaneveld, “Absorption of visi-ble and near-infrared light in water: the dependence on tem-perature and salinity,” Appl. Opt. 36, 6035–6046 (1997).

16. J. M. Sullivan, M. S. Twardowski, J. R. V. Zaneveld,C. M. Moore, A. H. Barnard, P. L. Donaghay, and B. Rhoades,“Hyperspectral temperature and salt dependencies of absorp-tion by water and heavy water in the 400–750nm spectralrange,” Appl. Opt. 45, 5294–5309 (2006).

17. J. R. V. Zaneveld, J. C. Kitchen, and C. M. Moore, “The scatter-ing error correction of reflecting-tube absorption meters,”Proc. SPIE 2258, 44–55 (1994).

18. C. Moore, A. Barnard, and D. Hankins, ScatteringMeter (BB9)User’s Guide, Revision A (WET Labs, 2005), pp. 2–13.

19. A. Morel, “Optical properties of pure water and pure sea-water,” in Optical Aspects of Oceanography, N. G. Jerlov andE. Steemann Nielson, eds. (Academic, 1974), pp. 1–24.

20. E. Boss and W. S. Pegau, “The relationship of light scatteringat an angle in the backward direction to the backscatteringcoefficient,” Appl. Opt. 40, 5503–5507 (2001).

21. A. Bricaud, A. Morel, and L. Prieur, “Absorption by dissolvedorganic matter in the sea (yellow substance) in the UV andvisible domain,” Limnol. Oceanogr. 26, 43–53 (1981).

22. S. Green and N. Blough, “Optical absorption and fluorescenceproperties of chromophoric dissolved organic matter in natu-ral waters,” Limnol. Oceanogr. 39, 1903–1916 (1994).

23. D. J. Keith, J. A. Yoder, and S. A. Freeman, “Spatial and tem-poral distribution of colored dissolved organic matter (CDOM)in Narragansett Bay, Rhode Island: implications for phyto-plankton in coastal waters,” Estuar. Coast. Shelf Sci. 55,705–717 (2002).

24. M.S.Twardowski, E. Boss, J.B.MacDonald, W. S. Pegau,A. H. Barnard, J. R. V. Zaneveld, “A model for estimating bulkrefractive index from the optical backscattering ratio and theimplications for understanding particle composition in case Iand case II waters,” J. Geophys. Res. 106, 1412914142 (2001).

25. F. Volz, “Die Optik und Meterologie der atmospharischen Tru-bung,” in Ber. des Detsch. Wetterdienstes 13 (Bad Kissingen,1954), Vol. 2, pp. 3–47.

26. E. Boss, W. S. Pegau, W. D. Gardner, J. R. V. Zaneveld,A. H. Barnard, M. S. Twardowski, G. C. Chang, andT. D. Dickey, “The spectral particulate attenuation and parti-cle size distribution in the bottom boundary layer of a conti-nental shelf,” J. Geophys. Res. 106, 9509–9516 (2001).

27. A. Morel, “Diffusion de la lumiere par les eaux de mer: Resul-tats experimentaux et approche theorique,” in Optics of theSea, AGARD Lecture Series (North Atlantic Treaty Organiza-tion, 1973), Vol. 61, pp. 3.1.1–3.1.76.

28. E. Boss, M. S. Twardowski, and S. Herring, “Shape of the par-ticulate beam attenuation spectrum and its relation to the sizedistribution of oceanic particles,” Appl. Opt. 40, 4885–4893(2001).

29. A. A. Gitelson, J. F. Schalles, and C. M. Hladik, “Remote chlor-ophyll-a retrieval in turbid, productive estuaries: CheapeakeBay case study,” Remote Sens. Environ. 109, 464–472 (2007).

30. G. Dall’Olmo, A. A. Gitelson, D. C. Rundquist, B. Leavitt,T. Barrow, and J. C. Holz, “Assessing the potential of SeaWiFSandMODIS for estimating chlorophyll concentration in turbidproductive waters using red and near-infrared bands,”RemoteSens. Environ. 96, 176–187 (2005).

31. A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist,T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simplesemi-analytical model for remote estimation of chlorophyll-a in turbid waters: validation,” Remote Sens. Environ. 112,3582–3593 (2008).

32. R. W. Gould Jr., R. A. Arnone, and P. M. Martinolich, “Spectraldependence of the scattering coefficient in case 1 and case 2waters,” Appl. Opt. 38, 2377–2383 (1999).

33. A. Morel, B. Gentili, M. Chami, and J. Ras, “Bio-optical proper-ties of high chlorophyll Case 1 waters and of yellow-substance-dominated Case 2 waters,” Deep-Sea Res. I 53, 1439–1459(2006).

34. M. Chami, E. B. Shybanov, T. Y. Churilova, G. A. Khomenko,M. E.-G. Lee, O. V. Martynov, G. A. Berseneva, andG. K. Korotaev, “Optical properties of the particles in theCrimea coastal waters (Black Sea),” J. Geophys. Res.110, (2005).

35. D. McKee and A. Cunningham, “Evidence for wavelength de-pendence of the scattering phase function and its implicationfor modeling radiance transfer in shelf seas,” Appl. Opt. 44,126–135 (2005).

36. M. Chami, E. B. Shybanov, G. A. Khomenko, M. E.-G. Lee,O. V. Martynov, and G. K. Korotaev, “Spectral variation ofthe volume scattering function measured over the full range


of scattering angles in a coastal environment,” Appl. Opt. 45,3605–3619 (2006).

37. A. L. Whitmire, E. Boss, T. J. Cowles, and W. S. Pegau, “Spec-tral variability of the particulate backscattering ratio,” Opt.Express 15, 7019–7031 (2007).

38. O. Ulloa, S. Sathyendranath, and T. Platt, “Effect of the par-ticle size-distribution on the backscattering ratio in seawater,”Appl. Opt. 33, 7070–7077 (1994).

39. A. Morel and S. Maritorena, “Bio-optical properties of oceanicwaters: a reappraisal,” J. Geophys. Res. 106, 7763–7780(2001).

40. J. M. Sullivan, M. S. Twardowski, P. L. Donaghay, andS. A. Freeman, “Use of optical scattering to discriminate par-ticle types in coastal waters,”Appl. Opt. 44, 1667–1680 (2005).

41. H. Loisel and A. Morel, “Light scattering and chlorophyllconcentration in case 1 waters: a reexamination,” Limnol.Oceanogr. 43, 847–858 (1998).

42. M. Chami, D. Mckee, E. Leymarie, and G. Khomenko, “Influ-ence of the angular shape of the volume-scattering functionand multiple scattering on remote sensing reflectance,” Appl.Opt. 45, 9210–9220 (2006).

43. E. T. Baker and J. W. Lavelle, “The effect of particles size onthe light attenuation coefficient of natural suspensions,” J.Geophys. Res. 89, 8197–8203 (1984).

44. A. Hofmann and J. Dominik, “Turbidity and mass concentra-tion of suspended matter in lake water: a comparison of twocalibration methods,” Aquat. Sci. 57, 54–69 (1995).

45. E. T. Baker, D. A. Tennant, R. A. Feely, G. T. Lebon, andS. L. Walker, “Field and laboratory studies on the effect of par-ticle size and composition on optical backscattering measure-ments in hydrothermal plumes,” Deep-Sea Res. I 48, 593–604(2001).

46. Q. J. Song and J. W. Tang, “The study on the scattering proper-ties in the Huanghai Sea and East China Sea,” Acta Oceanol.Sin. 28, 56–63 (2006).

47. K. L. Carder, R. D. Tomlinson, and G. F. Beardsley Jr., “Atechnique for the estimation of indices of refraction ofmarine phytoplankters,” Limnol. Oceanogr. 17, 833–839(1972).

48. S. G. Ackleson and R. W. Spinrad, “Size and refractive index ofindividual marine particulates: a flow cytometric approach,”Appl. Opt. 27, 1270–1277 (1988).

49. E. Aas, “Refractive index of phytoplankton derived from itsmetabolite composition,” J. Plankton Res. 18, 2223–2249(1996).

50. D. Stramski, A. Bricaud, and A. Morel, “Modelling the inher-ent optical properties of the ocean based on the detailedcomposition of the planktonic community,” Appl. Opt. 40,2929–2945 (2001).

51. D. R. Lide, ed., “Physical and optical properties of minerals,” inCRC Handbook of Chemistry and Physics, 77th ed. (CRCPress, 1997), pp. 4130–4136.

52. J. R. Hunt, “Prediction of oceanic particle size distributionsfrom coagulation and sedimentation mechanisms, in particu-lates in water,” Advances in Chemistry Series No. 189,M. C. Kavanaugh and J. O. Leckie, eds. (American ChemicalSociety, 1980), pp. 243–257.

53. A. B. Burd and G. A. Jackson, “Modeling steady-state particlesize spectra,” Environmental Sci. Technol. 36, 323–327(2002).

54. H. Bader, “The hyperbolic distribution of particle sizes,” J.Geophys. Res. 75, 2822–2830 (1970).

55. H. Loisel, J.-M. Nicolas, A. Sciandra, D. Stramski, andA. Poteau, “Spectral dependency of optical backscatteringby marine particles from satellite remote sensing of the globalocean,” J. Geophys. Res. 111, C09024 (2006).

56. D. McKee and A. Cunningham, “Identification and character-ization of two optical water types in the Irish Sea from in situinherent optical properties and seawater constituents,” Es-tuar. Coast. Shelf Sci. 68, 305–316 (2006).


Light scattering properties and their relation to the biogeochemical composition of turbid...

Documents

Transcript of Light scattering properties and their relation to the biogeochemical composition of turbid...