RESEARCH ARTICLE10.1002/2014JC009876

A new approach to discriminate dinoflagellate from diatomblooms from space in the East China SeaShaoling Shang1,2, Jingyu Wu2, Bangqin Huang3, Gong Lin1, Zhongping Lee4,James Liu5, and Shaoping Shang1,2

1Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University),Ministry of Education of China, Xiamen, China, 2Research and Development Center for Ocean Observation Technologies,Xiamen University, Xiamen, China, 3Key Laboratory of Coastal and Wetland Ecosystems, Ministry of Education/FujianProvincial Key Laboratory of Coastal Ecology and Environmental Studies, Xiamen University, Xiamen, China, 4School forthe Environment, University of Massachusetts Boston, Boston, Massachusetts, USA, 5Department of Oceanography,National Sun Yat-sen University, Kaohsiung, Taiwan

Abstract Dinoflagellate and diatom blooms often occur in the East China Sea (ECS) during spring andsummer. Some of the dinoflagellate blooms are toxic, resulting in widespread economic damage. In orderto mitigate the negative impacts, remote-sensing methods that can effectively and accurately discriminatebetween bloom types are demanded for early warning and continuous monitoring of bloom events at largescales. An in situ bio-optical data set collected from diatom and dinoflagellate blooming waters indicatesthat the two types of blooms exhibited distinctive differences in the shapes and magnitudes of remote-sensing reflectance (Rrs). The ratio of in situ measured Rrs spectral slopes at two spectral ranges (443–488and 531–555 nm, bands available with the moderate resolution imaging spectrometer (MODIS) sensor),abbreviated as BI (representing bloom index), was found effective in differentiating dinoflagellates from dia-toms. Reflectance model simulations, which were carried out using in situ and algal culture data as input,provided consistent results. A classification approach for separating dinoflagellate from diatom blooms inthe ECS was then developed: When fluorescence line height (FLH) is doubled over the background leveland total absorption coefficient at 443 nm� 0.5 m21, if 0.0 < BI � 0.3, it suggests a dinoflagellate bloom; if0.3 < BI � 1.0, it suggests a diatom bloom. Finally, the approach was applied to MODIS measurements overthe ECS, and a series of diatom and dinoflagellate bloom events during April–June 2005 and 2011 were suc-cessfully identified, suggesting that the proposed approach is generally valid for the ECS.

1. Introduction

Monitoring spatial and temporal variations of the distribution of dominant phytoplankton groups at theglobal scale is important under the context of global change [Karl et al., 2001]. It is also of great interest toregional government agencies and the public to detect specific phytoplankton species at the local scale,particularly in marginal seas where harmful algal blooms (HABs) occur frequently. Approaches for the earlydetection of HABs and their subsequent monitoring that offer high spatial and temporal resolutions arehighly desirable. Many methods have been developed [e.g., Ahn and Shanmugam, 2006; Fisher et al., 2006;Hu et al., 2005, 2010; Stumpf et al., 2003, 2008; Wynne et al., 2005], which include the use of remote sensingfrom satellites and aircrafts. For instance, a chlorophyll anomaly technique that uses the increase in chloro-phyll concentration of 1 mg m23 from the mean of the previous 60 days [Stumpf et al., 2003] was foundvery effective (>80%) for the detection of Karenia brevis blooms along the southwest Florida coast [Tomlin-son et al., 2004]. Also, MODIS fluorescence line height (FLH, mW cm22 lm21 sr21) data were found effectiveto detect and trace a HAB event in southwest Florida coastal waters [Hu et al., 2005]. Besides these effortsthat provide key information on distributions and transport of algal biomass, techniques for species orgroup-level remote detection of HABs have also been developed and applied to satellite ocean color meas-urements with some successes [e.g., Amin et al., 2009; Cannizzaro et al., 2008; Siswanto et al., 2013; Tomlinsonet al., 2009; Wynne et al., 2008]. One classification technique to detect toxic Karenia brevis (dinoflagellate)blooms in the Florida coastal water was developed based on its low particulate backscattering featurescompared to other high chlorophyll waters [Cannizzaro et al., 2008]. An ensemble approach also showed

Special Section:Pacific-Asian Marginal Seas

Key Points:� Dinoflagellate and diatom blooms

were differentiated from ocean colormeasurements� Remote-sensing reflectance at four

bands were used� The approach was successfully

applied to MODIS data

Correspondence to:S. Shang,slshang@gmail.com,slshang@xmu.edu.cn

Citation:Shang, S., J. Wu, B. Huang, G. Lin, Z.Lee, J. Liu, and S. Shang (2014), A newapproach to discriminatedinoflagellate from diatom bloomsfrom space in the East China Sea, J.Geophys. Res. Oceans, 119, doi:10.1002/2014JC009876.

Received 1 FEB 2014

Accepted 1 JUL 2014

Accepted article online 8 JUL 2014

SHANG ET AL. VC 2014. American Geophysical Union. All Rights Reserved. 1

Journal of Geophysical Research: Oceans

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success in separating Karenia brevis from non-Karenia brevis blooms, wherein a combination of the chloro-phyll anomaly [Stumpf et al., 2003; Tomlinson et al., 2004], the backscatter ratio [Cannizzaro et al., 2008] andthe spectral shape of normalized water-leaving radiance (nLw, mW cm22 lm21 sr21) at 490 nm was utilized[Tomlinson et al., 2009]. Recently, a method [Siswanto et al., 2013] using spectral slope difference of nLwshowed strength in identifying various water types, including dinoflagellate and diatom blooms, in theJapan Seto-Inland Sea. It remains unclear, however, if these available techniques are effective in identifyingthese types of phytoplankton in waters beyond their study regions.

The East China Sea (ECS) is a typical marginal sea suffering from HABs. HABs are getting broader in area andmore frequent in occurrence [e.g., Zhou et al., 2008]. In 2004, a dinoflagellate bloom event, mainly Prorocen-trum donghaiense, persisted through the entire month of May, and the coverage was as large as 10,000 km2

[Chen et al., 2006]. Since 2000, the main phytoplankton types blooming in the ECS have shifted from non-toxic diatoms to dinoflagellates (some of them are toxic), and the succession of these two types of phyto-plankton was observed during a field campaign in 2005 [Tang et al., 2006; Zhang et al., 2008]. It is suggestedthat this phenomenon is mostly attributed to increase nitrate load from the Changjiang River, a river rankednumber 3 in terms of water discharge in the world [Wohl, 2007]. Thus, to discriminate diatoms from dinofla-gellates using satellite measurements is not only beneficial to early warning, monitoring, and prediction ofHABs, but also key to establish long-term observations of species succession in the ECS in response to envi-ronmental changes.

We propose a new empirical approach to differentiate dinoflagellate from diatom blooms in the ECS usingremote-sensing reflectance (Rrs, sr21). This scheme is based on in situ measurements of phytoplanktonblooms, algal batch culture data, and modeling of spectral Rrs. The proposed approach has been verifiedwith MODIS measurements over the ECS where two cases of dinoflagellate and diatom blooms wererecorded during field surveys.

2. Data and Measurements

2.1. In Situ ObservationsDiatom and dinoflagellate blooms in two coastal bays, Xiamen Bay (XMB) and Huangqi Bay (HQB), were sur-veyed during 2003–2008 (Figure 1 and Table 1).

Remote-sensing reflectance, Rrs, was derived from measured (1) upwelling radiance (Lu, W m22 nm21 sr21)at an angle �30� from nadir and �90� from the solar plane, (2) downwelling sky radiance (Lsky, W m22

nm21 sr21) at an angle reciprocal to Lu, and (3) radiance from a standard Spectralon reflectance plaque(Lplaque, W m22 nm21 sr21). The instrument used was the GER 1500 spectroradiometer (Spectra Vista Corpo-ration, USA), which covers a spectral range of 350–1050 nm, with a spectral resolution of 3 nm. From thesethree components, Rrs was calculated as [Carder and Steward, 1985]:

Rrs5qðLu2F:LskyÞ=ðp:LplaqueÞ2D; (1)

where q is the reflectance (0.5) of the Spectralon plaque with Lambertian characteristics and F is the surfaceFresnel reflectance (around 0.023 for the viewing geometry). The symbol, D (sr21), accounts for the residualsurface contribution (glint, etc.), which was determined through iterative derivation according to opticalmodels for coastal turbid waters, as described in Lee et al. [2010].

Surface water samples for the determination of the absorption coefficients and chlorophyll-a concentration(Chl, mg m23) were collected. Measurements of Chl and the absorption coefficient of chromophoric dis-solved organic matter (CDOM) (ag, m21) were performed according to the Ocean Optics Protocols, Version2.0 [Mitchell et al., 2000], and were detailed in Hong et al. [2005] and Du et al. [2010]. The particulate absorp-tion coefficient (ap, m21) was measured by the filter-pad technique with a dual-beam PE Lambda 950 spec-trophotometer equipped with an integrating sphere (150 mm in diameter) following the T methodrecommended in the NASA protocol [Mitchell et al., 2000]. Detrital absorption (ad, m21) was obtained byrepeating the T measurements on samples after pigment extraction by methanol. For the calculation of ap

and ad from the measured raw data, the path-length amplification effect caused by multiple scatteringswas corrected following the expression given in Cleveland and Weidemann [1993]. The phytoplanktonabsorption coefficient (aph, m21) was then calculated by subtracting ad from ap. The combination of ad and

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ag yields an estimate for the nonalgal absorption coefficient (adg, m21). Major phytoplankton species wereidentified microscopically.

2.2. Culture ExperimentsBecause in situ data from natural blooms were quite limited (only 46 pairs of Rrs and Chl match-up data and23 pairs of Rrs and aph match-up data), cultures of diatoms and dinoflagellates were grown in the lab toexpand the optical coverage of bloom waters. Absorption and particulate backscattering coefficients (bbp,m21) and Chl were measured for these algal cultures. We chose to cultivate two typical HAB species thatare commonly observed in the ECS, Skeletonema costatum Cleve (diatom) and Prorocentrum donghaiense Lu(dinoflagellate). The strains were provided by the State Key Laboratory of Marine Environmental Sciences,Xiamen University. Cultures were grown by inoculating the strains into F/2 medium in sterilized conicalflasks which were kept at 20�C (62�C) under illuminance of 3000–6000 lux with a light-dark ratio of 14:10.Before measurements, the cultures were diluted to generate eight samples for diatoms and nine samplesfor dinoflagellates, with Chl ranging 2.5–22.9 mg m23 for the diatom and 1.2–27.3 mg m23 for the dinofla-gellate. Measurements of absorption and backscattering properties were performed on healthy cultures inactive growth; the samples were assumed to be free of detritus. The bbp at 420, 442, 470, 510, 590, and700 nm was measured with a Hydroscat sensor (Hobi Labs, Inc.) following Whitmire et al. [2010]. Measure-ments of absorption followed the methodology used for in situ samples (see section 2.1).

2.3. MODIS DataAqua-MODIS daily Level-2 Rrs (Version R2012.0) and FLH data in April–June 2005 and 2011, when HABevents were observed in situ, were downloaded from the NASA Distributed Active Archive Center

China Mainland

CRE

Changjiang River

JiulongRiver

119.75oE 119.85oE 119.95oE26.2oN

26.25oN

26.3oN

26.35oN

26.4oN

118oE 118.05oE 118.1oE 118.15oE

24.45oN

24.5oN

24.55oN

XMB

HQB

Figure 1. Location of sampling stations in 2003 (cross), 2004 and 2005 (triangle), and 2008 (plus); XMB, HQB, CRE, and ECS are the abbrevi-ations of the Xiamen Bay, the Huangqi Bay, the Changjiang River estuary, and the East China Sea, respectively.

Table 1. Algal Bloom Events in Xiamen Bay and Huangqi Bay During 2003–2008

Location Time Chl (mg m23) Bloom Type

Huangqi Bay 2 Jun 2003 5.2–51.2 Dinofllagellate Gymnodinium mikimotoi, Prorocentrum triestinumXiamen Bay 13 May to 4 Jun 2004 2.1–19.5 Diatom Coscinodiscus divisus GrunowXiamen Bay 31 May to 20 Jun 2005 6.1–27.5 Diatom Chaetoceros curvisetusHuangqi Bay 3–27 May 2008 1.2–132.1 Dinoflagellate Prorocentrum dentatum

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(DAAC, http://oceancolor.gsfc.nasa.gov/). The spatial resolution of thesedata is 1 km 3 1 km. Total absorp-tion coefficients were derived byfeeding the Rrs data to the quasi-analytical algorithm (QAA). Detaileddescription and steps of QAA can befound in Lee et al. [2002, 2007,2010]. Computer codes for the deri-vation can be found at the IOCCGwebsite (http://www.ioccg.org/groups/software.html). MODIS FLH isa standard product released byNASA and is derived from threeMODIS bands centered at 665, 678,and 746 nm [Letelier and Abbott,1996].

3. An Empirical Approach to Differentiate Dinoflagellate From Diatom Blooms

3.1. In Situ ObservationsDiscernible differences in Rrs spectra between dinoflagellates and diatoms are demonstrated in two sets ofin situ Rrs data (see Figure 2). Both the values and the shapes in Rrs spectra are different between the dino-flagellate and diatom blooming waters. In particular, (1) Rrs for dinoflagellates is lower than that of diatomsfor a similar level of Chl and (2) the slope of Rrs in the blue region (443–488 nm) for the dinoflagellates isnot as steep as that for the diatoms.

These features suggest possibilities for separating dinoflagellates from diatoms solely based on the Rrs spec-trum, although the observed differences in the Rrs spectrum could also include effects of different levels ofother optically active matters (e.g., CDOM and detritus). We first tested existing approaches, including theSS(490) [Tomlinson et al., 2009] and the spectral slope difference approach (abbreviated as SSD here) [Sis-wanto et al., 2013], and the backscatter ratio approach (abbreviated as BBR here) [Cannizzaro et al., 2008],briefly summarized below:

SS 490ð Þ5nLw 490ð Þ2nLw 443ð Þ2½nLw 555ð Þ2nLw 443ð Þ�3ð4902443Þ=ð5552443Þ; (2)

SSD5 nLw 488ð Þ2nLw 412ð Þ½ �=ð4882412Þ2½nLw 547ð Þ2nLw 488ð Þ�=ð5472488Þ; (3)

where nLw is the normalized water-leaving radiance, which could be easily converted from Rrs (http://oceancolor.gsfc.nasa.gov/DOCS/MSL12/master_prodlist.html/ Rrs).

BBR is defined as the ratio between the measured particle backscattering coefficient and that expectedfrom the Case-1 model [Morel, 1988],

BBR5bbpð550Þ=bbpð550Þ Morel; (4)

with bbp(550) derived from Rrs [Carder et al., 1999]:

bbp 550ð Þ52:0583Rrs 551ð Þ20:00182; (5)

and bbp(550)_Morel was derived from Chl [Morel, 1988], with Chl from sample measurements:

bbpð550Þ Morel50:33Chl0:623½0:00210:23 0:520:253log10ChlÞð �: (6)

Results of applying the above approaches are shown in Figure 3. When Chl� 5 mg m23 (indicative of inten-sive blooms), values of SS(490) for both types of algal blooms are negative (Figure 3a), and SSD values are

Wavelength [nm]

400 500 600 700 800 900

R rs [

sr-1

]

0.000

.003

.006

.009

.012

.015

Chl = 15.2, aph(443) = 0.50 (Dinoflagellate)Chl = 13.1, aph(443) =0.47 (Diatom)

Chl = 23.4, aph(443) = 0.60 (Diatom)

Chl = 24.5, aph(443) = 0.64 (Dinoflagellate)

443 488 531 555

Figure 2. Typical Rrs spectra observed in the Xiamen Bay and Huangqi Bay. Unitsfor Chl and aph(443) are mg m23 and m21, respectively.

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<0.006 (Figure 3b). Note that SS(490) as 0 orSSD as 0.006 were proposed as threshold val-ues to distinguish dinoflagellates from diatoms[Siswanto et al., 2013; Tomlinson et al., 2009].Our data suggested that it is not effective toseparate dinoflagellates from diatoms in theECS based on SS(490) or SSD. On the otherhand, samples associated with BBR< 1.0include both diatoms and dinoflagellates (Fig-ure 3c), thus BBR of 1.0 is not an effective crite-rion to separate dinoflagellates from diatoms inthe ECS, although it worked well to classify Kar-enia brevis in the Florida coastal waters [Canni-zzaro et al., 2008]. Apparently, the threeavailable approaches were not successful inseparating dinoflagellates from diatoms in ourstudy region, which inspired the developmentof a new approach in order to effectively differ-entiate the two types of phytoplankton bloomsin the ECS.

Based on the observed Rrs features from fieldmeasurements (see Figure 2), and through trial-and-error, a bloom index (BI)—ratio of spectralslopes, is developed below:

BI5 Rrs 488ð Þ2Rrs 443ð Þ½ �=ð4882443Þ=f½Rrs 555ð Þ2Rrs 531ð Þ�=ð5552531Þg:

(7)

The Rrs data at the four bands used in the calcu-lation of BI are available from the MODIS meas-urements. The BI provided robust results forseparating dinoflagellate blooms from diatomblooms based on the in situ data set, as high-lighted in the plot of BI versus Chl (Figure 4a). Itappeared that during the two surveys in theXMB (Table 1), BI of the diatom-bloom waters

was never lower than 0.3. In contrast, BI was mostly lower than 0.3 in intensive dinoflagellate-bloom waters(also see Table 1). The strong contrast in BI is also apparent in the plot of BI versus the phytoplanktonabsorption coefficient at 443 nm (aph(443), an optical phytoplankton biomass index), although aph sampleswere missing for one survey in HQB (Figure 4b). Such a contrast was weakened, however, if simply compar-ing the Rrs slope between 443 and 488 nm (Figure 4c), or calculating BI using the MODIS ocean color bandcentered at 547 nm instead of the 555 nm band (Figure 4d).

However, these observations were quite limited in number and dynamic range, thus not necessarily repre-sentative of BI for such bloom waters. To examine the robustness of BI in differentiating dinoflagellateblooms from diatom blooms, we expanded this data coverage with semianalytical simulations based onfield and culture measurements. Details of this simulation are provided below.

3.2. Model of Rrs SpectrumRrs spectrum of diatom and dinoflagellate blooms was modeled based on the relationships derived fromthe radiative transfer theory, using absorption and backscattering properties measured in situ, as well asfrom algal cultures as input.

For optically deep waters, Rrs can be modeled as a function of the total absorption coefficients (a, m21) andtotal backscattering coefficients (bb, m21) of the water column [Gordon et al., 1988; Lee et al., 2002]. There is

1 10 100Chl [mg m-3]

0.1

1

10

BB

R

-0.02

0

0.02

SSD

[mW

cm

-2 μ

m-1

sr-1

nm

-1]

-0.4

0

0.4

SS(4

90) [

mW

cm

-2 µ

m-1

sr-1

] 2003_HQB (Dinoflagellate)2004_XMB (Diatom)2005_XMB (Diatom)2008_HQB (Dinoflagellate)

(a)

(b)

(c)

0.0

0.006

1.0

Figure 3. Scattering plots of Chl versus (a) SS(490) [Tomlinson et al.,2009]; (b) SSD [Siswanto et al., 2013]; and (c) BBR [Cannizzaro et al.,2008]; all were calculated based on in situ measurements in XiamenBay and Huangqi Bay.

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rrs5Rrs=ð0:5211:73RrsÞ; (8)

rrs5½0:0910:1253bb=ða1bbÞ�3bb=ða1bbÞ; (9)

with rrs for the below-surface remote-sensing reflectance.

The absorption and backscattering can be modeled as [Kirk, 1994; Mobley, 1994]

a5aw1adg1aph; (10)

bb5bbw1bbp; (11)

where aw [Pope and Fry, 1997] and bbw [Morel, 1974] are the values of pure seawater, which are consideredas constants. There are various models to calculate spectral aph, adg, and bbp, either based on field measure-ments [e.g., Bricaud et al., 1995; Bukata et al., 1995; Roesler and Peryy, 1995] or from optimization of adatabase [e.g., Maritorena et al., 2002]. The determination of the absorption spectra was based on measure-ments of the blooming waters in XMB and HQB, and bbp based on measurements of the phytoplankton cul-tures, both using 443 nm as a reference wavelength (k0).

3.2.1. aph SpectrumField measured aph spectra of the two types of algal blooms are presented in Figure 5, where the distinctivedifference between diatoms and dinoflagellates appeared at wavelengths of 443–488 nm. The aph of dino-flagellates showed a secondary peak at �463 nm, but the aph of diatoms does not. On the other hand, dia-toms exhibited higher absorption at �540–580 nm. Measurements of phytoplankton cultures showgenerally consistent contrast in aph spectral shapes between dinoflagellates and diatoms (see Figure 5).

Chl [mg m-3]1 10 100

BI

.01

.1

1

102003_HQB (Dinoflagellate)2004_XMB (Diatom)2005_XMB (Diatom)2008_HQB (Dinoflagellate)

(a)

1.0

0.3

aph(443) [m-1].01 .1 1 10

BI

.01

.1

1

10(b)

1.0

0.3

Chl [mg m-3]1 10 100

R rs S

lope

(443

,488

) [sr

-1 n

m-1

]

1e-6

1e-5

1e-4

1e-3(c)

Chl [mg m-3]1 10 100

BI_

547

.01

.1

1

10(d)

1.0

0.3

Figure 4. Scattering plots of (a) BI versus Chl; (b) BI versus aph(443); (c) Rrs slope(443, 488) versus Chl; and (d) BI_547 versus Chl. BI_547 is BIcalculated with Rrs(547) instead of Rrs(555).

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Following the spectral model of Lee et al.[1998], aph(k) of the two types of algae wasmodeled as a function of aph(443) (Figure 6)

aph kð Þ5fa0 kð Þ1a1 kð Þ3ln½aph 443ð Þ�g3aph 443ð Þ;

(12)

with the spectral model coefficients (a0and a1) listed in Table 2.

3.2.2. adg SpectrumIn both diatom and dinoflagellate bloom-ing waters, it was found that adg covariedwith aph(443) to a large degree (R2 5 0.5,see Figure 7). This was not surprising; dur-ing a bloom, most CDOM and detrituswere locally bio-generated products.Assuming that contributions from detritus

and CDOM to the total absorption were similar in the two types of blooms, adg(443) for both bloom waterswas modeled as a function of aph(443):

adg 443ð Þ50:493aph 443ð Þ10:26: (13)

Then adg at other wavelengths was obtainedfrom adg(443) following an exponential function[Bricaud et al., 1981]:

adg kð Þ5adg 443ð Þ3exp 2Sdg3 k2443ð Þ� �

; (14)

with Sdg set as 0.015 nm21 [Lee et al., 2002].

3.2.3. bbp SpectrumSpectral bbp data were collected from algalbatch cultures. We assumed thatbbp(442) 5 bbp(443), and that the bbp modelobtained in cultures was applicable to theblooming waters, based on the fact that theblooming waters were dominated by phyto-plankton particles rather than inorganic miner-als [Stramski et al., 2001].

In general, the bbp for diatoms decreased with awavelength at the range of 420–590 nm (Figure8a). Compared with diatoms, dinoflagellatesshowed lower bbp values for a given Chl and arelatively flatter spectrum. Following conven-tional practices [Gordon and Morel, 1983], bbp

was modeled as a power-law function ofwavelength:

bbp kð Þ5bbp k0ð Þ3ðk0=kÞY ; (15)

with 443 nm as the reference wavelength (k0).The power coefficient (Y) was derived by fitting

Wavelength [nm]

400 500 600 700 800

a ph(

λ)/aph

(443

)

0.0

.2

.4

.6

.8

1.0

1.2Chl = 13.1, aph(443) = 0.47 (Diatom)Chl = 23.4, aph(443) = 0.60 (Diatom)Chl = 15.2, aph(443) = 0.50 (Dinoflagellate)Chl = 24.5, aph(443) = 0.64 (Dinoflagellate)Diatom cultureDinoflagellate culture

Figure 5. Typical aph spectra normalized at 443 nm measured in Xiamen Bayand Huangqi Bay in comparison with those measured in algal cultures. Unitsfor Chl and aph(443) are mg m23 and m21, respectively.

0 0.4 0.8 1.2 1.6 2aph(443) [m-1]

0

0.4

0.8

1.2

a ph(

488)

[m-1

]

0

0.2

0.4

a ph(5

31) [

m-1

]

0

0.1

0.2

0.3

a ph(

555)

[m-1]

Dinoflagellate (2003_HQB)Diatom (2004&2005_XMB)

Figure 6. Scattering plots of in situ measured aph(443) versus aph at488, 531, and 555 nm.

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bbp(k)/bbp(443) versus wavelength (Figure 8b). It was also found that bbp(443) and aph(443) of these cultureswere well correlated (R2 5 0.89–0.98) (Figure 8c). This was evident, considering that the algal culture par-ticles were dominated by phytoplankton cells; in other words, the cultures were free of or had limited detri-tus. It was thus possible to develop an empirical bbp model as

bbp kð Þ5A3aph 443ð Þ3ð443=kÞY : (16)

It was further found that the values of A and Y were 0.021 and 0.77 for dinoflagellates and 0.048 and 1.67 fordiatoms, respectively. We compared this bbp model with that from the Case-1 approach [Morel and Maritorena,2001] (Figure 8d). The relationship between bbp(550) and Chl of diatoms is similar to that of the Case-1 model.The relationship between bbp(550) and Chl of dinoflagellates, however, is lower than that of the Case-1 model,which is consistent with that found in dinoflagellate blooms in natural environments [Cannizzaro et al., 2008],providing an indirect validation of the model (equation (16)) obtained from culture measurements.

After models of the three optically active components (aph, adg, and bbp) are determined, Rrs can then besimulated with a single variable: aph(443). By varying this value from 0.1 to 1.5 m21, Rrs spectrum and corre-sponding BI values were calculated for either dinoflagellate or diatom-dominated waters. The BI differencesbetween dinoflagellate and diatom blooms of this simulated data were, in general, consistent with thoseobserved in situ (Figure 9a). More importantly, even though values of aph(443) higher than 0.8 m21 (themaximum in situ observed value in diatom bloom waters) was used, the two species are easily distinguishedthrough evaluating their BI values. Data from the simulations indicated that it is possible to differentiate dia-toms from dinoflagellates based on BI as long as aph(443) � 0.2 m21 (equivalent Chl as �5 mg m23). How-ever, the ranges of simulated BI (when considering the standard deviations of each optically activecomponent, see Table 2 and Figures 7 and 8 for the standard deviations) do show some overlaps betweenthe lower BI values of diatoms and the higher BI values of dinoflagellates (see Figure 9a). Such overlaps willlimit the power of separation if the BI value is not significantly distant from 0.3.

A relationship between BI and Chl was notconsidered here. This is because Chl is nota direct product of ocean color radiometry[IOCCG, 2006], although conversion fromaph(443) to Chl via chlorophyll-specificabsorption coefficient (a�ph) is straightfor-ward. But this conversion is highlydependent on the accuracy of specifyinga�ph, which is subject to wide variationsboth spatially and temporally [Bricaudet al., 1995, 1998, 2004].

3.3. Differentiation of DinoflagellateFrom Diatom Based on Rrs

After combining in situ observations andmodel results, a criterion is proposed todifferentiate diatom from dinoflagellateblooms via remote-sensing measurements.

Table 2. Spectral Coefficients of the aph Model (Equation (12))

k (mm) a0 a1 R2 N

Diatom 488 0.620 6 0.022 0.009 6 0.017 0.994 24531 0.350 6 0.024 0.038 6 0.019 0.985 24555 0.249 6 0.020 0.047 6 0.015 0.957 24

Dinoflagellate 488 0.685 6 0.004 20.035 6 0.003 0.999 6531 0.305 6 0.007 20.021 6 0.006 0.995 6555 0.156 6 0.003 20.016 6 0.003 0.998 6

aph(443) [m-1]

0.0 .4 .8 1.2 1.6

a dg(4

43) [

m-1

]

0.0

.4

.8

1.2

1.6

Diatom (2004&2005_XMB)Dinoflagellate (2003_HQB)

30=570=

050±260+×080±490=2

NR

xy.

)..()..(

Figure 7. Scattering plot of in situ measured adg(443) versus aph(443).

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When Chl� 5 mg m23 or aph(443)� 0.2 m21,

if 0.0 < BI � 0.3, it suggests a dinoflagellate bloom;

if 0.3 < BI � 1.0, it suggests a diatom bloom.

The threshold value of 0.3 was merely empirical, and there could be high uncertainties if the calculated BIvalue of a water body is around 0.3.

Further, two issues must be addressed to ensure proper application of this scheme to satellite ocean colordata: (1) an accurate detection of blooms; (2) an accurate estimation of Chl or aph(443) for detection of

Wavelength [nm]

400 450 500 550 600

b bp [

m-1

]

0.000

.005

.010

.015

.020

.025

.030

DiatomDinoflagellate(a)

Wavelength [nm]

400 450 500 550 600

b bp(

)/ bbp

(442

)

.4

.6

.8

1.0

1.2

1.4DiatomDinoflagellate

690=

443×050±081=

2

350±770

Rx

y

840=

443×050±990=

2

450±671

Rx

y

(b)

aph(443) [m-1]0.0 .2 .4 .6

b bp(

442)

[m-1

]

0.000

.005

.010

.015

.020

.025

.030

980=×0010±0190=

2Rxy

890=×0070±0490=

2Rxy

(c)

Chl [mg m-3]

5 10 15 20 25

b bp(

550)

[m-1

]

0.000

.004

.008

.012

.016

.020DinoflagellateDiatomMorel and Maritorena [2001]

(d)

Figure 8. The bbp properties measured in algal culture samples with Chl ranging 2.5–22.9 mg m23 for diatoms and 1.2–27.3 mg m23 fordinoflagellates. (a) The bbp spectra; (b) fitting curves for the bbp model (equation (15)); (c) scattering plots of aph(443) versus bbp(442); and(d) bbp(550) versus Chl. Dashed lines in Figures 8b and 8d refer to the best fitting curves.

0 0.5 1 1.5aph(443) [m-1]

0.0

0.5

1.0

1.5

2.0

2.5

BI

0 0.5 1 1.5 2 2.5 3a(443) [m-1]

Dinoflagellate (2003_HQB)Diatom (2004&2005_XMB)Dinoflagellate_ModeledDiatom_Modeled

(a) (b)

Figure 9. Modeled (a) aph(443) dependent BI and (b) a(443) dependent BI in comparison with BI relationships of diatom and dinoflagellateblooms measured in the Xiamen Bay and Huangqi Bay. Solid curves were the BI computed using the best fitting curves of equations (12–16); dashed lines were the bounds of BI computed from the standard deviations of the fitting curves of equations (12–16). Blue (lines andsymbols) represent the diatoms and red represent the dinoflagellates.

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intensive blooms. Harmful dinoflagellate blooms in the ECS were often observed at regions with bottomdepth in a range 30–50 m, and within an area of 27–30�N [Zhou and Zhu, 2006]. It is extremely challengingto detect blooms based on retrievals of Chl in such optically complex ECS coastal waters, where CDOM andnonalgal particles are abundant [Shang et al., 2014]. Accurate derivation of aph(443) would also be difficultbecause the semianalytical algorithms such as QAA [Lee et al., 2002] are sensitive to spectral errors of Rrs atthe 412 nm band induced by imperfect atmospheric corrections in these coastal waters. Bloom detectionand the further discrimination of dinoflagellates from diatoms could be misleading if satellite derived Chl oraph(443) products are erroneous.

The first issue is an accurate detection of blooms in coastal waters; and it was easily solved by simply follow-ing the FLH approach [e.g., Hu et al., 2005]. Pioneering studies have shown a linear correlation betweenchlorophyll concentration and natural phytoplankton fluorescence, stimulating the development of FLH toestimate sun-stimulated chlorophyll fluorescence [Gower, 1980]. FLH is also less sensitive to interferencecaused by nonbiotic optically active materials [Letelier and Abbott, 1996]. Hu et al. [2005] demonstrated thatMODIS FLH provided more reliable information than the ratio-derived Chl product on the relative distribu-tion of the bloom patches in the complex Florida coastal environment, despite some artifacts in the dataand uncertainty attributed to factors such as unknown fluorescence efficiency. This approach was alsofound valid in China’s coastal waters, usually with a doubled value of FLH over the background level as abloom indicator [e.g., Li et al., 2011; Shang et al., 2012; Zhao et al., 2010].

The next issue involves avoiding applying the BI scheme incorrectly due to false information of bloomintensity, i.e., an inaccurate value of Chl or aph(443). It has been shown that the total absorption coefficientat 443 nm (a(443)) derived through QAA is insensitive to the quality of Rrs at the 412 nm band and can beretrieved from ocean color measurements with high accuracy [Lee et al., 2002, 2010]. The BI versus aph(443)system for the identification and classification of bloom waters was thus revisited to derive a relationshipbetween BI and a(443). The results are shown in Figure 9b, which indicate that the identification of the twotypes of algal blooms could also be accomplished with a(443) as an input.

Finally, a criterion based on the combination of FLH, a(443), and BI is proposed below:

When FLH is doubled over the background level and a(443)� 0.5 m21,

if 0.0 < BI � 0.3, it suggests a dinoflagellate bloom;

if 0.3 < BI � 1.0, it suggests a diatom bloom.

We are aware that a(443) includes contributions from CDOM and detritus, so waters with high a(443) arenot necessarily phytoplankton dominated waters. This potential misclassification is avoided with the pro-posed FLH constraint. And most importantly, a(443) could be accurately retrieved even if Rrs(412) is errone-ous, a common issue in ocean color remote sensing over coastal waters [Zibordi et al., 2009]. In short, usinga(443) instead of aph(443) is a feasible alternative to reduce the uncertainty in application of the proposedBI scheme to satellite data.

4. Verification of the Proposed Approach With MODIS Data

The proposed approach was applied to daily MODIS Rrs data in April–June 2005, when bloom events and spe-cies successions in the ECS were reported [Ding et al., 2012; Zhang et al., 2008]. The input data were MODISdaily Rrs, which were downloaded from DAAC (see section 2). Steps for data processing are briefed below:

1. Derive FLH from MODIS Rrs [Letelier and Abott, 1996] or directly download FLH from DAAC, searchingbloom patches by examining if the FLH is doubled over the background levels.

2. For the bloom patches identified in Step 1, derive a(443) from MODIS Rrs (we used QAA [Lee et al., 2002,2010]; one may use other algorithms such as the GSM [Maritorena et al., 2002] through SeaDAS, or directlydownload these products from DAAC), and examine if a(443) is �0.5 m21.

3. For pixels with a(443)� 0.5 m21, calculate BI from MODIS Rrs based on equation (7). If 0.3 < BI � 1.0, itindicates intensive diatom blooms; if 0.0 < BI � 0.3, it indicates intensive dinoflagellate blooms; if BI is neg-ative or> 1.0, it suggests either questionable data or unknown phytoplankton.

In summary, bloom types are classified based on a(443) and BI derived from Rrs, along with MODIS FLH tofacilitate bloom detection in this optically complex water.

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Results of the above scheme over the ECS are shown in Figure 10. The valid coverage from the FLH productwas low, both spatially and temporally. This was due to the intense cloud cover and failure in atmosphericcorrection in the turbid coastal water in the vicinity of the Changjiang River estuary (CRE). Also, high load ofsuspended sediments depress the signal of chlorophyll fluorescence [McKee et al., 2007]. In mid-April,MODIS FLH snapshots indicated blooming patches offshore of the Zhejiang province, with much higherFLH overlaid on a background of 0.001 mW cm22 lm21 sr21. In those patches, pixels with a(443) � 0.5 m21

and 0.3 < BI � 1.0 (shown in blue color) were found, indicating an occurrence of diatom blooms. On 29May, a strong bloom with FLH> 0.08 mW cm22 lm21 sr21 was found off the coast of Zhejiang province.And, in that patch, both 0 < BI � 0.3 (red pixels) and 0.3 < BI � 1.0 (blue pixels) (for a(443) � 0.5 m21) werefound, suggesting a mixed bloom of diatoms and dinoflagellates, or in the middle of transition from diatomto dinoflagellate blooms. Unfortunately, there were no data available to verify this satellite observation. Inearly to mid-June, blooming patches with FLH> 0.08 mW cm22 lm21 sr21 moved to the north, closer tothe CRE. High concentrations of dinoflagellates were found, as indicated by the red pixels in the images of 5and 9 June. In late June, diatom blooms returned and tended to retreat to the south, more prominent inthe waters offshore of Zhejiang province (see blue pixels in the images of 23, 27, and 30 June). These resultswere in general consistent with in situ observations reported by Zhang et al. [2008].

Another case (April–June 2011) was examined. One survey conducted in 14–17 April 2011, captured diatomblooms in the vicinity of the CRE (Chl up to �7 mg m23 at the surface). Later in the period of 31 May to 8June 2011 dinoflagellate blooms were observed in the same region and to the south (Chl up to �17 mgm23 at the surface). Cloud free MODIS data in this period were processed, generating results of MODIS FLHfor bloom detection and images of bloom types classified on the basis of a(443) and BI (Figure 11). Fromwater sample measurements, surface Chl and the percentage of diatoms were 3.4 mg m23 and 83%,respectively, at 123.39�E/30.59�N on 15 April, and were 4.7 mg m23 and 94% respectively, at 122.5�E/31.5�N on 16 April (see cross symbols on the 12 and 18 April images). Nearby these two sampling stations,water with high MODIS FLH (>0.04 mW cm22 lm21 sr21) and high BI (>0.3) were found on 12 and 18 April,indicating an occurrence of diatom blooms in this area. Subsequent dinoflagellate blooms, indicated by insitu measurements, were strikingly consistent with results detected by MODIS (30 May to 3 June). The circlesymbols in the images of 30 May and 3 June showed in situ sampling stations where surface Chl and thepercentage of dinoflagellates ranged 4.7–16.9 mg m23 and 76–100%, respectively. Exactly matching thesein situ observations, very high MODIS FLH (>0.1 mW cm22 lm21 sr21) and low BI (� 0.3) emerged, pointingto strong dinoflagellate blooms in these waters.

5. Discussion

5.1. Why Rrs Shows Different Spectral Characteristics?Figure 2 demonstrates distinct differences in the magnitude and shape of Rrs spectra between dinoflagellateand diatom blooms. The Rrs spectrum is determined by spectral a and bb (equations (8) and (9)). Ignoringcontribution from water molecules, and assuming that contributions from detritus and CDOM are similar inthe two types of blooms, aph and bbp are conceivably the main factors contributing to the difference.

It is noticeable that for a similar level of Chl or absorption due to phytoplankton (e.g., aph(443)�0.5 or 0.6 m21),Rrs for dinoflagellates is lower than that of diatoms (Figure 2). Based on equations (8) and (9), it is clear that (1)the bbp for dinoflagellates is lower than that of diatoms and (2) bbp contributes more to the magnitude differ-ence of Rrs, although absorption will also modify the magnitude. Measurements of cultured samples indicatedthat bbp was indeed higher for diatoms than for dinoflagellates (Figure 8a) with similar Chl levels, likely a resultof the silica shells associated with diatoms. This is consistent with that reported in Cannizzaro et al. [2008]. Theyfound that measured bbp was significantly lower when Karenia brevis concentrations exceeded 104 cells L21

compared to bbp values measured in diatom-dominated high Chl (>1.5 mg m23) waters, and suggested thatlower backscattering accounted for the observed decreased reflectivity for Karenia brevis blooms.

On the other hand, differences in the Rrs spectral shape are most likely attributed to the differences in the aph

spectrum between dinoflagellates and diatoms (Figure 5). The absorption coefficient for dinoflagellates is muchhigher at the bands between 443 and 448 nm than that for diatoms. The major accessory pigments containedin diatom blooms (mainly Skeletonema costatum, Chaetoceros curvisetus, etc.) in the ECS are fucoxanthin, diadi-noxanthin, chlorophyll c1 and c2 [Yao et al., 2011], while they are peridinin, diadinoxanthin, dinoxanthin, and

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Figure 10. MODIS images of (left) FLH and (right) bloom type during April to June 2005; bloom type was identified using a(443) depend-ent criterion; blue and red indicated the diatom and dinoflagellate blooms, respectively.

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chlorophyll c2 for the typical genus of dinoflagellate blooms in the ECS (Prorocentrum and Gymnodinium, etc.)[Zapata et al., 2012]. Among these accessory pigments, fucoxanthin was the dominant one contained in diatoms(fucoxanthin/chlorophyll a� 0.84) and peridinin was the one for dinoflagellates (peridinin/chlorophyll a� 1.35)[Yao et al., 2011; Zapata et al., 2012]. The in vivo absorption spectra of these two pigments are similar and bothpeaking at �490 nm [Bidigare et al., 1990; Falkowski and Raven, 1997; Johnsen et al., 1994]. Diadinoxanthin andchlorophyll c2 are less abundant, but their ratios versus chlorophyll a are still up to 0.64 and 0.33, respectively, indinoflagellates, while these ratios are much lower in diatoms (0.15 and <0.05) [Yao et al., 2011; Zapata et al.,2012]. The in vivo absorption of these two pigments peaks at �460 nm [Johnsen et al., 1994], and the extinctioncoefficients of these two pigments are much higher than that of chlorophyll a [Falkowski and Raven, 1997].Therefore, it is very likely that the differences in the aph shapes observed here are results of different levels ofdiadinoxanthin and chlorophyll c2 contained in dinoflagellates and diatoms, leading to distinctive spectralslopes of Rrs in the 443–488 nm range for these two types of phytoplankton.

A sensitivity analysis was conducted to demonstrate the different impact of bbp and aph on the Rrs spec-trum. When using the diatom aph model coefficients (a0 and a1, see equation (12) and Table 2) to calculatethe absorption spectrum of dinoflagellates, the derived BI for dinoflagellates became almost the same asthat of diatoms (Figure 12a). On the other hand, when the diatom bbp model (with A 5 0.048 and B 5 1.67,equation (16)) was applied to both phytoplankton classes, the derived BI of the two phytoplankton groupsremain nearly the same (Figure 12b). These exercises confirmed that it is mainly the aph spectra, rather thanbbp, that contributed to the depressed BI (<0.3) associated with dinoflagellate blooms.

5.2. Algorithm IssuesAs described in section 3.1, none of the available algorithms which have been successfully used in otherregions, i.e., SS(490), SSD, and BBR, worked well to distinguish dinoflagellates from diatoms in the ECS region.

Figure 11. MODIS images of (left) FLH and (right) bloom type during April to June 2011; bloom type was identified using a(443) depend-ent criterion; blue and red indicated the diatom and dinoflagellate blooms, respectively; cross symbols indicated the diatom bloomsobserved during 14–17 April 2011 and circles indicated the dinoflagellate blooms observed during 31 May to 8 June 2011.

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Both SS(490) and SSD are shape-based algo-rithms. One potential reason for their subduedsuccess in our study region might be due to dif-ferent pigment compositions in different waters.For example, as discussed above, dinoflagellatespecies in the ECS contained a marked accessorypigment peridinin, which is not found in Kareniabrevis. BBR is basically an algorithm based onthe Rrs magnitude difference. Although lowbackscattering of dinoflagellates was retained inour region, BBR failed in distinguishing dinofla-gellates from diatoms. One possibility is that theempirical derivation of bbp through equation (5)[Carder et al., 1999] might not be applicable tothe blooming waters we surveyed (i.e., XMB andHQB). Nevertheless, our results echoed thenecessity of extensive bio-optical investigationsof various phytoplankton blooms. Althoughdominant phytoplankton groups could be thesame, species may be different, and associatedwith different optical properties. Consequently,one approach valid for the detection of onedinoflagellate species, for example, Karenia bre-vis, may not work in detection of another dino-flagellate species, for example, Prorocentrumdonghaiense.

The strength of the BI proposed in this study isits stronger tolerance to imperfect atmospheric correction. First, compared to Rrs(412), Rrs at 443, 488, 531,and 555 nm are more reliable from satellite ocean color measurements over coastal waters [Shang et al.,2014; Zibordi et al., 2009]. Note that Rrs(412) is required to derive SSD. Second, it uses a(443) as an input,which has much higher reliability than that of aph(443) or Chl when derived from ocean color remote sens-ing [IOCCG, 2006; Lee et al., 2010; Shang et al., 2011]. In addition, even though BBR was capable in identifyingphytoplankton blooms, additional uncertainties could be introduced when it is applied to satellite measure-ments because the bbp(550)_Morel requires input of satellite Chl, which has rather low reliability in the opti-cally complex ECS coastal waters [Siswanto et al., 2011]. For the BI scheme proposed here, certainlyextremely high CDOM and detritus contents will affect the BI value for a given phytoplankton absorption.However, this extraordinary condition will reduce FLH [McKee et al., 2007], so a false positive identification ofbloom is likely avoided as our scheme starts with observations of high FLH.

6. Conclusions

Based on in situ observations, measurements of algal cultures, and modeling results, a new approach wasdeveloped to differentiate blooms between dinoflagellate and diatom in the ECS. This scheme, i.e., BI, isbased on the combination of FLH, a(443), and the ratio of Rrs spectral slopes at two different spectralregions, with criteria as below:

When FLH is doubled over the background level and a(443)� 0.5 m21,

if 0.0 < BI � 0.3, it suggests a dinoflagellate bloom;

if 0.3 < BI � 1.0, it suggests a diatom bloom.

Applying this approach to MODIS measurements over the ECS region, a succession of diatom and dinofla-gellate bloom events in 2005 and 2011 (observed via field surveys) was successfully identified.

This approach is simple: it directly uses satellite observed Rrs at a few bands. Specifically, FLH, a(443), and BI,properties required to detect phytoplankton blooms and to classify bloom types, are all derived from Rrs.

0 0.5 1 1.5 2aph(443) [m-1]

0.0

0.5

1.0

1.5

2.0

BI

0.0

0.5

1.0

1.5

2.0

BI

Dinoflagellate (2003_HQB)Diatom (2004&2005_XMB)Dinoflagellate_ModeledDiatom_Modeled

(a)

(b)

Figure 12. Results of BI sensitivity analysis. (a) aph for dinoflagellates iscalculated based on the model developed for diatoms; (b) bbp for dino-flagellates is calculated from the model developed for diatoms.

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The scheme is also relatively tolerant to imperfect atmospheric correction, although subject to some uncer-tainties, an inherent limitation of all satellite approaches. This algorithm was developed and tested withdata obtained in the ECS only; it is thus necessary to be cautious when extending its application to otherregions. Separately, it is difficult to quantify the spatial coverage of blooms because there are many missingdata due to cloud coverage. In fact, the reported areas influenced by dinoflagellate bloom events could beup to 10,000 km2 at maximum [Zhou et al., 2008], while areas identified using MODIS Rrs were about half ofthat value. Further investigations with more field and satellite observations are certainly required before theproposed approach can be implemented routinely, either to monitor HAB events or to generate a timeseries of bloom areas to help understand the ecosystem variability in the ECS.

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AcknowledgmentsThis study was jointly supported bythe Chinese Ministry of Science andTechnology through the High-TechR&D Program (2013BAB04B00) and theNational Basic Research Program(2009CB421201), and NSF-China(41376177 and 40976068). We thankour colleagues in the XiamenUniversity for various help, in particularYahui Gao for identifyingphytoplankton species in fieldsamples, Guomei Wei for helping insitu data, and Haili Wang andGuoqiang Qiu for Hydroscatmeasurements.

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