Remote Sensing of Environment 129 (2013) 185–196

Contents lists available at SciVerse ScienceDirect

Remote Sensing of Environment

j ourna l homepage: www.e lsev ie r .com/ locate / rse

Detection of harmful algal blooms of Karenia mikimotoi using MODIS measurements:A case study of Seto-Inland Sea, Japan

Eko Siswanto a,⁎, Joji Ishizaka b, Sarat Chandra Tripathy b,1, Kazuyoshi Miyamura c

a Institute of Geospatial Science and Technology, Universiti Teknologi Malaysia, UTM Skudai, Johor 81310, Malaysiab Hydrospheric Atmospheric Research Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 4648601, Japanc Oita Prefectural Agriculture, Forestry and Fisheries Research Center, Kamiura, Saiki, Oita 8792602, Japan

⁎ Corresponding author. Tel.: +60 75530839; fax: +E-mail addresses: ekosiswanto@utm.my (E. Siswanto

jishizaka@hyarc.nagoya-u.ac.jp (J. Ishizaka), sarat@hyarc.miyamura-kazuyoshi@pref.oita.lg.jp (K. Miyamura).

1 Present address: National Centre for Antarctic and OVasco-da-Gama, Goa - 403 804, India.

0034-4257/$ – see front matter © 2012 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.rse.2012.11.003

a b s t r a c t

a r t i c l e i n f o

Article history:Received 30 August 2011Received in revised form 25 September 2012Accepted 6 November 2012Available online 4 December 2012

Keywords:Remote sensingNormalized water leaving radianceOcean colorHarmful algal blooms (HABs)Seto-Inland Sea (Japan)

The end of Sea-viewingWide Field-of-view Sensor (SeaWiFS) mission makes harmful algal bloom (HAB) detec-tion with moderate resolution satellite data now relies on Moderate Resolution Imaging Spectroradiometer(MODIS). Based on MODIS and in situ data collected in the coastal region of the western part of Seto-InlandSea, Japan with HAB-forming algae Karenia mikimotoi, a simpler new satellite remote sensing-based HAB detec-tionmethodwas developed. The strength of thismethod is, although it does not require indigenous atmosphericcorrection scheme, it is expected to be able to classify K. mikimotoi blooms, diatom blooms, TSM-dominated wa-ters, gelbstoff-dominated waters, and mixed waters in the optically complex coastal waters. We anticipate thatour satellite remote sensing-based HAB detection method can operate as a valuable complementary toolassisting in situ HAB monitoring and as an integrated part of HAB early warning systems to mitigate HAB nega-tive impacts not only in the coastal waters of the western part of Seto-Inland Sea, Japan, but also in the othercoastal waterswith differentHAB-forming algae, providing that backscattering signature and pigment packagingof other HAB-forming algae are similar to those of K. mikimotoi.

© 2012 Elsevier Inc. All rights reserved.

1. Introduction

Dinoflagellate Karenia mikimotoi is one of the most common algaecausing harmful algal blooms (HABs; or commonly called as red tide,both used interchangeably in this paper) in the eastern North Atlanticand Japanese waters (e.g., Gentien et al., 2007). In Japan, thesemi-enclosed coastal water of the western part of Seto-Inland Sea(Fig. 1) is one of the most frequent regions suffering from these HABs(e.g.,Miyamura et al., 2005). The latestK.mikimotoi bloom in thewesternpart of Seto-Inland Seawas in summer 2008 and caused serious fisheries'damages and economic losses. Besides causing massive fish mortality,K. mikimotoi can also adversely affect human health (e.g., Anderson etal., 2010; Chen et al., 2011). One of the strategies to minimize theseHAB-related negative effects is by developing near-real time HAB earlywarning, which can be relied on satellite remote sensing observations.

Significant achievements to detect and monitor HABs have beenmade within the past decades. All phytoplankton have photosyntheticpigment of chlorophyll. Therefore, HABs are usually associated withhigh chlorophyll. Steidinger and Haddad (1981) are among the

60 75566163.),nagoya-u.ac.jp (S.C. Tripathy),

cean Research, Headland Sada,

rights reserved.

pioneerswhoused satellite-derived chlorophyll to detect HABs. Recent-ly, Ishizaka et al. (2006) successfully used chlorophyll-a concentration(hereafter Chl-a) product derived by ocean color sensor of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to identify spatial andtemporal variations of red tide in the Ariake Sound, Japan. However, de-lineating red tide by using satellite Chl-a cannot differentiate normalphytoplankton bloom (e.g., seasonal blooms) from red tide and/orHABs (e.g., Tomlinson et al., 2004). To overcome this problem, derivingsatellite Chl-a anomalywas introduced and adopted to be an operationalHABmonitoring (e.g., Stumpf et al., 2003; Tomlinson et al., 2004). Delin-eating spatial extent of HABs using satellite-derived Chl-a or Chl-aanomaly seems to be ineffective for some reasons, i.e.; 1) Chl-a is not aunique indicator for HAB-causing algae, and Chl-a anomaly cannot beeasily detected, if HABs caused by algae with less cellular Chl-a content(Cokacar et al., 2004); 2) high Chl-a is not always necessarily associatedwithHABs, as harmless algae blooms also cause high Chl-a signature, andhence not possible to distinguish HABs from non-HABs; and 3) highsatellite-retrieved Chl-a can also be associated with non-living in-waterconstituents such as total suspended matters (TSM) and gelbstoff,which are normally high in optically complex Case-2 coastal waters, ashigh TSM and gelbstoff lead to reduce blue-to-green band reflectance ra-tioswhich eventually cause incorrect high Chl-a retrievals by NASA stan-dard Chl-a algorithm (O'Reilly et al., 1998; Siswanto et al., 2011).

Because Chl-a alone cannot discriminate HABs from non-HABs, al-ternative approach which relies on low backscattering signature ofHAB-forming algae has recently been proposed and received wide

Beppu Bay

Suo-Nada

SETO-INLAND SEA

(a)

(b)

b

a, b, c

e e e e e

d, e

2010 in situ Chl-a and cell count stations

2008 in situ Chl-a stations

2008 cell count stations

Fig. 1. (a) Geographic map of the western part of Seto-Inland Sea (includes Suo-Nadaand Beppu Bay) with positions of in situ Chl-a and Karenia mikimotoi cell sampling sta-tions superimposed. (b) The same as (a) but with positions of pixels from which nLwspectra (see Fig. 2) were derived. Red, green, and blue squares in (a) denote samplingstations for in situ Chl-a and K. mikimotoi cell counts in summer 2010 (5–8 July 2010),in situ Chl-a in summer 2008 (25 July 2008), and K. mikimotoi cell counts in summer2008 (17–28 July 2008), respectively. Letters a, b, c, d, and e are respectively, pixelsfrom which nLw spectra as in Fig. 2f, g, h, i, and j were derived.

186 E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

acceptance (e.g., Cannizzaro et al., 2008, 2009; Carder & Steward, 1985).However, this method, which is based on harmful algae of Kareniabrevis, seems to be ineffective in the optically complex coastal waterscontaining high TSM and gelbstoff. Although Hu et al. (2005) usingModerate Resolution Imaging Spectroradiometer (MODIS) and Goweret al. (2005) andWynne et al. (2008) usingMediumResolution ImagingSpectrometer (MERIS) have proved that phytoplankton fluorescenceline height (FLH) is potential to discriminate HABs from gelbstoff-richwaters in the coastal waters, FLH itself has high uncertainty as it isinfluenced bymultiple factors such as nutrient availability, light history,phytoplankton physiology, shallow bottomdepth, and recently by com-plex interactionwith TSM (Ahn& Shanmugam, 2007; Babin et al., 1996;Hu et al., 2005). In addition, FLH is also a surrogate for Chl-a that can be

high both duringHABs or non-HABs (e.g., Gower et al., 2005; Tomlinsonet al., 2009).

As an attempt to provide HAB detection method in optically com-plex Case-2 waters, Ahn and Shanmugam (2006), based on extensivein situ bio-optical dataset, proposed a red tide index, an index relatedto absorbing characteristics of HABs. While Ahn and Shanmugam's(2006) method is reliable in detecting HABs in turbid coastal waters,the method requires indigenous atmospheric correction procedure asa prerequisite which is not easy in the coastal waters containing highTSM and gelbstoff.

The limitations and/or complexities of HAB detection methods de-scribed above suggest that a simpler, more practical, and straightforwardmethodwhich is able to detect HABs in optically complex coastal waterswithout any prerequisite procedure is still a challenging task, and highlyrequired as marine aquaculture or fisheries' activities are more concen-trated in coastal regions and more frequently suffer from HABs due toeutrophication.

This work thus offers an alternative simple method to detect HABsusing satellite remote sensing. Different from the aforementionedmethods, this work stems from previous studies conducted in Japa-nese coastal waters which look at satellite normalized water leavingradiance (nLw) spectral shapes (Sasaki et al., 2008; Takahashi et al.,2009). Sasaki et al. (2008) mentioned that nLw spectral peak shiftsto longer green bands during red tide events caused by dinoflagellatesAkashiwo sanguinea and Chattonella spp. However, shifting of nLwspectral peak to longer wavelengths can also be due to high TSM orgelbstoff. More recently, Takahashi et al. (2009) proposed a methodwhich does not only consider nLw spectral peak shifting, but alsoChl-a background and slopes at short and long wavelength domainsof SeaWiFS nLw. Takahashi et al. (2009) worked on red tide causedby multispecies but dominated by diatoms Skeletonema costatumand Thalassiosira sp.. Takahashi et al.'s (2009) method however isnot able to differentiate HABs from non-HABs. The present studythus introduces an alternative HAB (K. mikimotoi) detection methodwhich is expected to be able to work in optically complex coastal wa-ters. The method is also proposed not only to detect HABs, but also todiscriminate HABs from other classes of water types, i.e., diatomblooms, TSM-dominated waters, gelbstoff-dominated waters, mixedwaters, and non-red tide/clear waters. In addition, because manyred tide detection methods above (e.g., Ahn & Shanmugam, 2006;Ishizaka et al., 2006; Stumpf et al., 2003; Takahashi et al., 2009) arededicated to be used with SeaWiFS, whose mission already ended inDecember 2010, alternative methods employing its predecessor, i.e.,MODIS, are necessary to be developed.

2. Materials and methods

2.1. MODIS data processing

Rather than standardMODIS Level-2,MODIS Level-1A data (providedby NASA Ocean Biology Processing Group (OBPG), http://oceancolor.gsfc.nasa.gov) were used, so that we could derive not only standardocean color products, but also all non-standard radiometric andbio-optical products, such as detritus/gelbstoff absorption coefficient(adg) and particulate backscattering (bbp). In this study, we usedMODIS re-processing 2009 data producedwith an improved atmospher-ic correction (http://oceancolor.gsfc.nasa.gov/REPROCESSING/R2009).The periods ofMODIS images used are thosewhenwe had field observa-tions during K. mikimotoi blooms (July–August 2003 and July 2008), dia-tom blooms (July 2010 and October 2010), and non-red tide winterperiod (January 2009). Using SeaWiFS Data Analysis System (SeaDASversion 6.2 under Linux OS), MODIS Level-1A containing raw radiancecounts were processed to MODIS Level-1B and then to MODIS Level-2containing radiometric and bio-optical products of Chl-a (retrieved byMODIS OC3M standard algorithm), nLw, adg, and bbp. The adg and

187E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

bbp used in this study were derived by quasi-analytical algorithm (QAA;Lee et al., 2002).

To understand MODIS nLw spectral library from optically differentwaters, i.e., algal bloom types (diatom or dinoflagellate), non-algalbloom waters, and other water types such as that dominated by TSM,nLw spectral shape was investigated usingMatrix Laboratory (Matlab).Besides considering nLw spectral shape, some empirical relationshipsbetween radiometric and bio-optical variables were also introduced tobe combined with nLw spectral shape for water type classification.

2.2. Field observations

Summer is the periodwhen K. mikimotoi bloom frequently occurs inthe western part of Seto-Inland Sea (Miyamura et al., 2005). Therefore,as part of the red tide monitoring campaign, field observations wereconducted in summer 2008 (17–28 July) and summer 2010 (5–8 July)to identify the dominant algal groups and to count K.mikimotoi cell con-centration in the coastal waters of the western part of Seto-Inland Seacovering Suo-Nada and Beppu Bay areas (Fig. 1a). Using microscope(in summer 2008 and 2010 cruises) and FlowCam (Fluid ImagingTech., in summer 2010 cruise), phytoplankton groups causing discolor-ation of the waters were identified. During the summer 2010 cruise, atthe same stationswhere K.mikimotoi cell sampleswere collected,watersamples in the surface layerwere also collected tomeasure in situ Chl-a.Separately from summer 2008 K. mikimotoi cell count observation

(a)

(b)

(c)

(f) (g)

(k) (l)

(p) (q) (r)

7 Jul 2010 27 Jul 2008 17 Oct 2

Wavelen

nLw

(m

W c

m-2

um

-1 s

r-1)

Fig. 2. MODIS standard Chl-a (first row: a–e) and MODIS nLw spectra (second row: f–j) detypes were confirmed by field observations: a) Diatom-1 bloom, b) Karenia mikimotoi bmixed water, and e) TSM/gelbstoff-dominated waters. The exact areas from which nLw spin Fig. 1b. The third row (k–o) is the same as the second row, except they were separated inat 547 nm, whereas blue lines correspond to the spectra peaked at bands other than 547 npixels) associated with red and blue spectra in the third row. The dates of MODIS data are

stations and dates (17–28 July), water samples for in situ Chl-a mea-surements were also collected on 25 July 2008 (see Fig. 1a).Water sam-ples were directly transferred from surface water bucket intopolyethylene bottles and immediately filtered through a 25 mm glassfiber filter (Whatman GF/F). Following Suzuki and Ishimaru's (1990)method, Chl-a was then measured with a Tuner Design Model 10-AUfluorometer after extraction with N,N-dimethylformamide for day.

We derived grids of 0.1×0.1° (containing satellite pixels) over thestudy region (see Fig. 1b). The grids approximately located at thesame positions of K. mikimotoi cell stations, were selected to deriveMODIS nLw spectral shape. The MODIS nLw spectra and dominantalgal groups observed in those same regions would then be compared.Another massive K. mikimotoi blooms also occurred during summer2003 in Suo-Nada and Beppu Bay coastal waters. This bloom eventwill be used to verify the new HAB detection method described in thispaper.

3. MODIS-based water type spectral library

3.1. Discriminating phytoplankton- from non-phytoplankton dominatedwaters

MODIS Chl-a image retrieved on 7 July 2010 showed a region of highChl-a >10 mg m−3 in the Beppu Bay (Fig. 2a). This high MODIS Chl-awas confirmed by in situ Chl-a which also showed high Chl-a

(d) (e)

(h) (i) (j)

(m) (n) (o)

(s) (t)

010 12 May 2010 28 Jan 2009

gth ( m)

rived from the areas approximately indicated by black arrows where particular waterloom, c) Diatom-2 bloom, and considered based on area and season of the data: d)ectra in (f), (g), (h), (i), and (j) were derived are indicated by letters a, b, c, d, and eto two groups using maximum nLw criteria (i.e., red lines correspond to spectra peakedm). Forth row (p–t) represents MODIS pixels classified into two classes (red and bluementioned in the upper part of MODIS Chl-a figures.

188 E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

>10 mg m−3 (Fig. 3a) indicating phytoplankton bloom. Not only Chl-amagnitude, but also both in situ and MODIS observations showed sim-ilar Chl-a spatial variation. Based on bothmicroscopic and FlowCam ob-servations, this phytoplankton bloom was dominated by diatomsChaetoceros sp., Nitzschia sp., and Skeletonema sp. (hereafter referred toas Diatom-1). K. mikimotoi cell counts in this phytoplankton bloom re-gion were only b5 cell ml−1 (Fig. 3b). Maximum K. mikimotoi cellcount observed north of Beppu Bay was only 15 cell ml−1 (Fig. 3b), in-dicating that summer 2010 phytoplankton bloom in Beppu Baywas notcaused by K. mikimotoi. Diatom bloom also occurred in the coastal re-gion of the Beppu Bay on 17 October 2010, which was also recognizedby MODIS Chl-a image (Fig. 2c), but there was no dominant algaegroup (hereafter referred to as Diatom-2).

Relatively high MODIS Chl-a (but less than summer 2010 MODISChl-a) was observed in the coastal region of Beppu Bay during sum-mer 2008 cruise (27 July 2008, Fig. 2b). Field observations on 25July 2008 indeed confirmed that in situ Chl-a during summer 2008was much lower (b7 mg m−3) than that during summer 2010(Fig. 3c). Field observations confirmed that this high Chl-a was asso-ciated with K. mikimotoi bloom. K. mikimotoi cell counts in theBeppu Bay counted during field observations conducted on 28 July

Longit

Lat

itud

e (o N

)

(a)

(c)

Chl-a (mg m-3)

Chl-a (mg m-3)

Lat

itud

e (o N

)

Fig. 3. Spatial distributions of in situ Chl-a (a) andKareniamikimotoi cell counts (b) collected duin situ Chl-a (c) and K. mikimotoi cell counts (d) collected during 2008 summer cruise (durinK. mikimotoi cell counts in (d) were collected on 17, 20, 22, 23, 26, and 28 July 2008, as indicat

2008 were within the range of 10,000–25,000 cell ml−1 (Fig. 3d), a5–12.5 fold of cell concentration (>2000 cells ml−1, according tolocal fisheries' authorities) causing fish mortality. Observationsmade on 23 and 26 July 2008 even counted K. mikimotoi cell countsas high as 31,150 cell ml−1 and 47,000 cell ml−1, respectively.K. mikimotoi was thus confirmed to be responsible for summer 2008algal bloom, though both in situ and MODIS Chl-a were not as highas those during summer 2010 diatom bloom. The relatively lowChl-a during K. mikimotoi bloom was similar to that during K. brevisbloom reported by Stumpf et al. (2003). They found unproportionalrelationship between Chl-a and K. brevis which might be expectedto be due to changes in the cellular content of Chl-a.

Based on in situ observation, Sasaki et al. (2008) mentioned thatnLw spectra from red tide water dominated by dinoflagellatesA. sanguinea and Chattonella spp. have nLw peak at longer wavelengthin the green band domain. Based on SeaWiFS data, Takahashi et al.(2009) also mentioned highest nLw at 555 nm among the six SeaWiFSvisible bands during red tide events dominated by diatoms S. costatumand Thalassiosira sp.. Accordingly, we expected that MODIS standardnLw data can also be used to discriminate K. mikimotoi bloom (peak atnLw547) from clear, non-red tide waters (peak at bands other than

ude (oE)

(b)

Cell counts (cell ml-1)

1,000 – 2,500

2,500 – 5,000 5,000 – 10,000

10,000 – 25,000

25,000 – 50,000

(cell ml-1) (d)

ring summer 2010 cruise (during diatom bloomperiod of 5–8 July). Spatial distributions ofg K. mikimotoi bloom). In situ Chl-a data in (c) were collected on 25 July 2008, whereased by blue, yellow, black, green, cyan, and red circles, respectively.

189E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

547 nm). But, as will be described below, such a simple red tide classi-fication would be problematic, as turbid, non-red tide waters with highTSM and gelbstoff can also have nLw spectral peak at 547 nm.

The pixels of MODIS images captured during Diatom-1 (7 July and17 October 2010; Fig. 2a, c) and K. mikimotoi (27 July 2008; Fig. 2b)blooms in the Beppu Bay were discriminated from non-algal bloompixels using the criteria mentioned above, meaning that all nLw spectra(Fig. 2f–h) were separated into spectra belonged to non-algal bloom (ifnLw peaks at bandsb547 nm) and algal bloom (if nLw peaks at547 nm) (Fig. 2k–m). Pixels of high MODIS Chl-a (Fig. 2a–c), whichwere considered as algal bloom, spatially matched with the pixelshaving nLw spectra with peak at band 547 nm (red color spectra inFig. 2k–m, p–r). Besides, nLw spectra of high Chl-a pixels also had val-leys at 443–488 domain (Fig. 2k–m). This valley shape was associatedwith the strong phytoplankton absorption in the blue bands (e.g., Ahn& Shanmugam, 2006; Sasaki et al., 2008; Tassan, 1994).

In contrast, duringwinter (28 January 2009), pixels in the coastal re-gions of the western part of Suo-Nada, showed a dome-like spectralshape at the same band domain (443–488 nm) (Fig. 2j and o) andalso showed high MODIS Chl-a (Fig. 2e). Lack of valley at 443–488 nmdomains might indicate low phytoplankton absorption. Senjyu et al.(2001) reported that western part of Suo-Nada is high in turbidity,even during summer with strong water column stratification. Becausethis MODIS image was captured in winter with strong wind-drivenwater column mixing, this high MODIS Chl-a might not be due to realChl-a. In addition, there was no red tide event reported during winterin this region, hence the red color region in Fig. 2t was not red tide,rather, more associated with high non-phytoplankton water constitu-ents such as TSM and gelbstoff. TSM and gelbstoff, respectively, scatterand absorb more light in green and blue bands, respectively (e.g., Jerlov,1976; Kirk, 1994; Wozniak & Stramski, 2004). This in turn would leadto incorrectly high Chl-a retrieval by NASA standard Chl-a algorithms,

Chl-a (mg m-3)

nLw

547–

412

slop

e

Phytoplankton-dom

Non-phytoplankton-domin

Fig. 4. Relationship between nLw547–412 slope and Chl-a drawn based on MODIS data sampledb, and c; mixed water from grid d; and non-phytoplankton-dominated water from grids e). PnLw547 (bloom pixels) and at band other than nLw547 (non-bloom pixels), respectively. Clospeak at nLw547 (bloom pixels) and at band other than nLw547 (non-bloom pixels), respectispectral peak at nLw547 (bloom pixels) and at band other than nLw547 (non-bloom pixels), reat nLw547 and at band other than nLw547, respectively. Left hand-oriented and right hand-opeak at nLw547 and at band other than nLw547, respectively. As an indicative of TSM, colotext) separating phytoplankton-dominated waters from mixed waters.

such as MODIS OC3M that employs blue–green band ratio (e.g., Darecki& Stramski, 2004; Hyde et al., 2007).

The nLw spectra characterized by valley (Fig. 2k–m) and domed(Fig. 2o) shapes at 443–488 nm domain thus could be considered tobe the nLw spectra from the waters optically dominated by phyto-plankton and non-phytoplankton substances, respectively. Practical-ly, if a MODIS pixel has nLw443–nLw412 slope>nLw488–nLw443

slope, and nLw488–nLw443 slope>nLw547–nLw488 slope (hereafterthese band slopes are expressed as nLwλ1–λ2 slope), it would be classi-fied as non-phytoplankton-dominated waters. In contrast, if a pixelhas an opposite feature i.e., nLw443–412 slopebnLw488–443 slope ornLw488–443 slopebnLw547–488 slope, it would be classified asphytoplankton-dominated waters.

The nLw spectra from high Chl-a pixels retrieved in spring (12 May2010; Fig. 2d) also had a peak at nLw547 with relatively high magni-tudes, but accompanied by less pronounced valley at 443–488 domain,and relatively low nLw412 (Fig. 2n). Such a spectral shape was thusprobably associated with the mixture of phytoplankton (due to springbloom), and non-phytoplankton substances such as phytoplanktonbyproduct (gelbstoff) and TSM.

In addition to nLw spectra-based discrimination, looking at the rela-tionship between MODIS Chl-a and nLw547–412 slope, data points fromphytoplankton blooms (phytoplankton-dominatedwaters) could be ob-viously discriminated from non-phytoplankton-dominated waters(Fig. 4). Waters with very high nLw547–412 slope accompanied with highnLw547 seemed to be dominated by TSM. It is also considered that incoastal waters, besides TSM, gelbstoff might also be remarkably high.The reason of using nLw547–412 slope for discriminating phytoplankton-from non-phytoplankton-dominated waters is because waters dominat-ed by TSM or gelbstoff will tend to have high nLw547–412 slope as TSMwould scatter light more in the longer than in the shorter wavelength,whereas gelbstoff would absorb light stronger in the shorter than in

nLw547 (mW cm-2 um-1 sr-1)

inated

ated

Mixed

in the grids as mentioned in Fig. 1b (i.e., phytoplankton-dominated water from grids a,lus and cross are data during Karenia mikimotoi bloom period with nLw spectral peak ated circle and closed diamond are data during Diatom-1 bloom period with nLw spectralvely. Open circle and open diamond are data during Diatom-2 bloom period with nLwspectively. Open square and open star are data for mixed water with nLw spectral peakriented triangles are data for non-phytoplankton dominated water with nLw spectralr bar for nLw547 is also mentioned. Dashed line is the line expressed by Eq. (1) (see

190 E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

the longer wavelength (e.g., Jerlov, 1976; Kirk, 1994; Wozniak &Stramski, 2004).

The nLw spectra of mixed waters mentioned above correspondedto the data points between those of phytoplankton- and non-phytoplankton-dominated waters. Discriminating mixed waters fromother water types by means of nLw spectral shape was not easy,hence for practical purposes, mixed waters were defined as pixels thathad not been classified as non-phytoplankton-dominated waters andhad nLw547–412 slope higher than Eq. (1) expressing nLw547–412 slope asa function of natural logarithmic of Chl-a as below:

nLw547−412slope ¼ −0:0003 ln Chlað Þð Þ2 þ 0:0024 ln Chlað Þ−0:00005: ð1Þ

Chl-a (mg m-3)

Wavelength (nm) Wavelength

Wavelength (nm) Wavelength

nLw

547

(mW

cm

-2m

-1 s

r-1)

bbp547 (m-1)

nLw

(m

W c

m-2

m-1

sr-1

)

nLw

(m

W c

m-2

m-1

sr-1

)

nLw

(m

W c

m-2

m-1

sr-1

)

nLw

(m

W c

m-2

m-1

sr-1

)

(b)(a)

(d)

(f) (g)

7 Jul 2010 27 Jul 2

Fig. 5. The nLw spectra (first row) during Diatom-1 bloom (a), Karenia mikimotoi bloom (b),(b), and (c) correspond to spectra peaked at 547 nm, and blue lines correspond to the spectwhere closed circle and diamond are data during Diatom-1 bloom period with nLw spectraspectively, and plus and cross are data during K. mikimotoi bloom period with nLw spectraspectively. (e), Scatter plot of MODIS Chl-a against nLw488–412 slope and nLw547–488 slope difspectra peak at nLw547 (bloom pixels) and at band other than nLw547 (non-bloom pixels)nLw547=0.8) and (e, i.e., nLw488–412 slope and nLw547–488 slope difference=0.006) are theK. mikimotoi bloom, respectively. The nLw spectra (third row) (f), (g), and (h) are respeDiatom-2 blooms (pink in color) are distinguished from that of K. mikimotoi nLw spectra (rdicating bbp547.

3.2. The possibility of discriminating K. mikimotoi blooms from diatomblooms

It was obvious from Fig. 5a and b, that nLw547 during K. mikimotoibloom that occurred in summer 2008 (27 July 2008) was remarkablylower than that during Diatom-1 bloom that occurred in summer2010 (7 July 2010). Approximately, at the same level of Chl-a, nLw547

during K. mikimotoi bloom (denoted by plus (+)) in Fig. 5d wasb0.8 mW cm−2 μm−1 sr−1 and corresponded to low bbp547. On theother hand, nLw547 during Diatom-1 bloom (colored circles in Fig. 5d)was >0.8 mW cm−2 μm−1 sr−1, and corresponded to high bbp547.This obviously indicated that K. mikimotoi had low Chl-a specific back-scattering signature than that of Diatom-1. The K. mikimotoi bloom

mgm(a-lhC -3)

(nm) Wavelength (nm)

(nm) Wavelength (nm)

nLw

488-

412

slop

e and

nL

w54

7-48

8 sl

ope d

iffe

renc

e

bbp547 (m-1)

nLw

(m

W c

m-2

m-1

sr-1

) nL

w (

mW

cm

-2m

-1 s

r-1)

(c)

(e)

(h)

008 17 Oct 2010

and Diatom-2 bloom (c) same as those in Fig. 2k, l, and m, respectively. Red lines in (a),ra peaked at bands other than 547 nm. (d), Scatter plot of MODIS Chl-a against nLw547,peak at nLw547 (bloom pixels) and at band other than nLw547 (non-bloom pixels), re-peak at nLw547 (bloom pixels) and at band other than nLw547 (non-bloom pixels), re-ference, where open circles and diamonds are data during Diatom-2 bloom with nLw, respectively, and plus and cross are the same as those in (d). Dashed lines in (d, i.e.,lines separating Diatom-1 bloom from K. mikimotoi bloom, and Diatom-2 bloom fromctively the same as (a), (b), and (c), except that nLw spectra lines for Diatom-1 anded in color) applying the dashed lines in (d) and (e). Color scales in (d) and (e) are in-

adg443-412 slope

nLw

547-

412

slop

e

Gelbstoff-dominated waters

TSM-dominated waters

nLw

547 (mW

cm-2

m-1 sr

-1)

Fig. 6. Scatter plot of nLw547–412 slope against adg443–412 slope derived fromMODIS data re-trieved on January 28, 2009, and sampled in MODIS grids e as mentioned in Fig. 1B. As anindicative of TSM, color scale for nLw547 is also mentioned. Dashed line represents a lineexpressed by Eq. (2) (see text) separating TSM- from gelbstoff-dominated waters.

191E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

was thus practically separable fromDiatom-1 bloomby using nLw547 of0.8 mW cm−2 μm−1 sr−1 as a threshold value. Similar results, butbased on in situ data, were also reported by Cannizzaro et al. (2008)and Cannizzaro et al. (2009)when theywere able to differentiate harm-ful algae K. brevis from diatom groups, owing to K. brevis' low Chl-a spe-cific backscattering signature. Low Chl-a specific backscattering mightbe probably a unique and hence an important signature for Kareniaspp. carrying gyroxanthin-diester pigment (e.g., Cannizzaro et al.,2009; Staehr & Cullen, 2003).

Diatom-2 bloom in fall (17 October 2010) however had nLw547 aslow as K. mikimotoi (Fig. 5b–c). So that, a threshold value of0.8 mW cm−2 μm−1 sr−1 could not be used to separate Diatom-2from K. mikimotoi blooms. But, there was still a distinct featuredistinguishing K. mikimotoi bloom from Diatom-2 bloom, i.e., K.mikimotoi bloom was more characterized by flattened spectral shapebetween412 and 547 nmbands, but Diatom-2 bloomwasmore charac-terized by a valley shape at the same band domain. As a consequence,the difference between nLw488–412 slope and nLw547–488 slope duringK.mikimotoi bloomwas somewhat smaller (due tomore flattened spec-tra) than that during Diatom-2 bloom (due to a valley feature of thespectra).

Observing Fig. 5e, most of nLw488–412 slope and nLw547–488 slope dif-ference during K. mikimotoi bloom (27 July 2008; denoted by plus[+]) was approximately b0.006, whereas nLw488–412 slope andnLw547–488 slope difference during Diatom-2 bloom (17 October2010; denoted by open circles) was approximately >0.006. Wetherefore suggested that nLw488–412 slope and nLw547–488 slope differ-ence of 0.006 could be used as a threshold value to separateDiatom-2 bloom from K. mikimotoi bloom. While Diatom-1 andDiatom-2 blooms had the same valley feature between 412 nm and547 nm band domain, they were different in the nLw547 magnitude.Whitmire et al. (2010) reported that different diatom groups havedifferent backscattering signatures. The magnitude of bbp547 duringDiatom-1 bloom (0.01–0.027 m−1) was higher than that duringDiatom-2 bloom (0.004–0.014 m−1), which might be responsiblefor the difference of nLw547 magnitude between Diatom-1 andDiatom-2 bloom events. We however merged both diatom bloomsinto a single class of diatom bloom (Fig. 5f and h).

3.3. Discriminating suspended sediment- from gelbstoff-dominatedwaters

Although it may not be crucial for HAB detection, it is important toseparate non-phytoplankton dominated waters into TSM- andgelbstoff-dominated waters that might be valuable for masking tur-bid waters and delineating river plume. TSM has an optical character-istic to scatter light at all visible bands, but more light scattered atgreen than blue domains. In contrast, gelbstoff absorbs light at all vis-ible bands, but absorbs stronger in the blue than green band domains(e.g., Jerlov, 1976; Kirk, 1994; Wozniak & Stramski, 2004). Therefore,assuming a constant gelbstoff, the water dominated by TSM will havehigher nLw547 and higher nLw547–412 slope. In contrast, water domi-nated by gelbstoff will have lower nLw547 and lower gelbstoff absorp-tion (adg) slope between 412 and 443 nm band domain (Fig. 6). For apractical purpose, TSM-dominated waters will be separated fromgelbstoff-dominated waters by using Eq. (2) as:

nLw547−412slope ¼ −2:857 adg443−412slope

� �2−0:281 adg443−412slope

� �

þ0:0053: ð2Þ

4. Discussion

Our method can be schematically presented as in Fig. 7 that wouldresult to six water classes. The method can be considered simple, but

has several advantages over the previously reported red tide and/orHAB detection methods as will be discussed below.

Red tide events are usually associated with high surface Chl-a andnLw spectral shape which peaks at the green band (e.g., Ishizaka et al.,2006; Sasaki et al., 2008; Takahashi et al., 2009). However, high sur-face Chl-a and nLw spectral peak at the green band observed by sat-ellite can also be due to high TSM and gelbstoff, normally observedin coastal waters. Therefore, by inspecting only these two criteria todetect red tide might lead to a false positive detection in the coastalregion, such as those observed in the western part of Suo-Nada coast-al region in winter (28 February 2010; Fig. 8c) and spring (12 May2010; Fig. 8f, g).

High MODIS Chl-a in the coastal region during winter andspring (Fig. 8a and e) might not be associated with real Chl-a, rathermore associated with high TSM resuspended by strong wind(especially during winter, 28 February 2010) as depicted by highnLw547>1 mW cm−2 μm−1 sr−1 (Fig. 9a and b). High nLw547 ledto incorrect high Chl-a retrieval by NASA standard Chl-a algorithmsemploying blue to green band ratio such as SeaWiFS OC4v4 andMODIS OC3M (e.g., Hyde et al., 2007; Komick et al., 2009; Siswantoet al., 2011). In fact, Tan et al. (2006) and Siswanto et al. (2011)reported that in the coastal waters with SeaWiFS nLw555>1 -mW cm−2 μm−1 sr−1, satellite Chl-a showed high overestimation.Miyamura et al. (2005) reported that K. mikimotoi bloom in the west-ern part of Seto-Inland Sea usually occurs in summer with sea surfacetemperature (SST)>25 °C (Fig. 9f, and see Fig. 8q–t). SSTs were how-ever low in winter (10–14 °C) and spring (15–18 °C) (Fig. 9d and e,see also Yosie et al., 2011), hence unfavorable condition for K.mikimotoi to bloom during winter and spring. In fact, there was nored tide event reported by local fisheries' authorities in this regionduring winter and spring 2010.

Recently, Takahashi et al. (2009) with SeaWiFS data developed ared tide detection method which not only considers the nLw spectralpeak at the green bands (i.e., SeaWiFS nLw 555 nm) and nLw back-ground spectra, but also includes criteria, i.e., if a SeaWiFS pixel hasnLw490–443 slopebnLw555–490 slope, that pixel will be classified as redtide. Because SeaWiFS mission has already ended in December2010, it is necessary to investigate the applicability of Takahashi etal.'s (2009) method for red tide detection using MODIS data. ApplyingTakahashi et al.'s (2009) method with MODIS bands (nearest to theSeaWiFS bands used by Takahashi et al. (2009)), i.e., nLw peak at

NO

nLw peak at 547 ?

YES

nLw443-412 slope > nLw488-443 slope AND

nLw488-443 slope > nLw547-488 slope ??

nLw547-412 slope > -2.857 (adg443-412 slope)2-0.281

(adg443-412 slope) + 0.0053 ?

nLw547-412 slope > -0.0003 [Ln(Chl-a)]2

+ 0.0024 [Ln(Chl-a)] – 0.00005 ?

Gelbstoff-dominated waters TSM-dominated nLw547 > 0.8 Mixed waters

(nLw547-488 slope - nLw547-488 slope) >

0.006 ?

K. mikimotoi bloom Diatom-2 bloom

Diatom-1 bloom

Non-red tide/clear

waters

YES NO NO YES

NO

NO YES

YES

NO YES

Fig. 7. Schematic procedure of HAB detection method to classify water types into six classes i.e., non-red tide/clear waters, TSM-dominated waters, gelbstoff-dominated waters,mixed waters, diatom blooms, and Karenia mikimotoi blooms. Diatom-1 and diatom-2 blooms are merged into a single diatom class.

192 E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

547 nm, and comparing nLw488–443 slope to nLw547–488 slope, Takahashiet al. (2009)-like method could effectively mask false red tide detec-tion resulted by Sasaki et al.'s (2008) method (see Fig. 8b and f, byTakahashi et al., 2009 compared to Fig. 8c and g, by Sasaki et al.,2008). Limited red tide areas were however still returned duringspring by Takahashi et al.'s (2009) approach (Fig. 8f), which seemedlikely to be more associated with normal spring diatom bloom. Apply-ing our method, this false warning was classified as mixed waters anddiatom (Fig. 8h), which seemed to be more reasonable as this regionwas also high in turbidity (Senjyu et al., 2001).

Both Sasaki et al.'s (2008) andTakahashi et al.'s (2009)methodswerenot designed to discriminate type of algal blooms. Therefore, eithercaused by K. mikimotoi (Fig. 8r and s) or diatom blooms (e.g., Fig. 8n, o,

v, andw), bothmethods would only give red tide detection information.One of the advantages of our simplemethod is, it has potential capabilityto discriminate K. mikimotoi from diatom blooms. As can be seen fromFig. 8p and x, our method classified diatom blooms in the coastal regionof Beppu Bay (7 July 2010) and in the wide region from Suo-Nada toBeppu Bay (17 October 2010), but during intense K. mikimotoi bloompe-riod (27 July 2008), our method consistently classified K. mikimotoibloom (Fig. 8t).

There was no red tide event reported on 28 February and 12 May2010, as SST was low during this period, but Sasaki et al.'s (2008)method gave false detections (Fig. 8c and g). While Takahashi etal.'s (2009) method simply masked these false red tide watersreturned by Sasaki et al.'s (2008) method as non-red tide waters

K. mikimotoi bloom

Non-red tide/clear

water

Diatom bloom

TSM-dominated

water

Gelbstoff-dominated

water

Mixed water

Land

Cloud/no data 12 May 2010

MODIS Chl-a Takahashi et al. (2009)-

like method Sasaki et al. (2008)-

like method New practical method

7 Jul 2010

27 Jul 2008

17 Oct 2010

30 May 2010

(m) (n) (o) (p)

(q) (r) (s) (t)

(e) (f) (g) (h)

28 Feb 2010

(a) (b) (c) (d)

(u) (v) (w) (x)

(i) (j) (k) (l)

Fig. 8. MODIS-based red tide mappings derived using Takahashi et al. (2009)-like method (second column), peak shifting (third column) following Sasaki et al. (2008), and thisstudy's methods (fourth column). MODIS standard Chl-a images (first column) corresponding to red tide maps are also shown.

193E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

(Fig. 8b and f), our simple method consistently classified as TSM/gelbstoff-dominated waters and diatom blooms (Fig. 8d and h).

We verified our newpracticalmethod using the independent data ofmassive K. mikimotoi bloom that occurred in summer 2003. The earlierstages of the K. mikimotoi blooms were detected with limited bloom

areas (Fig. 10a and c; 26 July 2003). Our new method also returned alarge extent of K. mikimotoi blooms in 30 July 2003 which was consis-tent with field observations conducted within the period from 29 Julyto 1 August, showing high K. mikimotoi cell counts >1000 cells ml−1

(Fig. 10b, d, and g). The MODIS pixels which were classified as K.

28 Feb 2010 12 May 2010 27 Jul 2008

MODIS

nLw547

MODIS

SST

(d) (f)(e)

(a) (c)(b)

28 Feb 2010 12 May 2010 27 Jul 2008

MODIS

nLw547

MODIS

SST

MODIS

nLw547

MODIS

SST

SST ( oC

) nL

w547 (m

W cm

-2m

-1 sr-1)

Fig. 9. MODIS nLw547 (first row images) and MODIS SST (second row images). The dates when these MODIS data were retrieved are mentioned in the upper part of each image.

194 E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

mikimotoi bloom in 30 July 2003 also showed lownLw547 andmore flat-tened nLw spectral shape at shorter band domain (Fig. 10e) similar tothat observed in summer 2008 (Fig. 5g).

Our method, which looked at nLw spectral shape combined withempirical equations, seemed likely to be robust at the step when sepa-rating phytoplankton blooms from TSM and/or gelbstoff-dominatedwaters in any coastal waters. The universal applicability of our methodto detect all HAB-forming algae still needs to be verified using more in-dependent datasets with different HAB-forming species. We howeveranticipated that our method would seem to be effective to distinguishother HAB-forming algae provided that they have low backscatteringsignature and cellular pigment packaging similar to K. mikimotoi, suchas K. brevis (Cannizzaro et al., 2008, 2009; Stumpf et al., 2003). Constantcoefficients in Eqs. (1) and (2), and threshold values reported heremight need to be changed slightly, as different species would havedifferent absorption/backscattering signatures and pigment packaging.Other harmful algae such as Lingulodinium polyedrum and Cochlodiniumpolykrikoides, which also have reflectance spectra similar toK. mikimotoi's reflectance spectra (lower reflectance at green band andmore flattened nLw spectral shape at phytoplankton absorptionbands; see Maldonado, 2008; Cetinic, 2009), might also be possible tobe classified effectively by this new practical method.

Unlike Ahn and Shanmugam's (2006) red index method which im-practically requires modification of atmospheric correction scheme, sothat their HAB detection method can be effective in optically complexCase-2 waters, our method does not require accurate local atmosphericcorrection scheme, as it uses nLw spectral shapes (spectral slopes, val-ley/domed shape, spectral peak) which are expected to be less sensitiveto atmospheric correction errors. The problemhoweverwill seem likelyto arise in the mixed water containing more gelbstoff than TSM, asgelbstoff's strong absorption at all wavelengths will decrease nLw,hence cause incorrect low backscattering signature, which eventuallylead to false HAB detection. Including FLH productmight help to reducefalse HAB detection due to gelbstoff, as gelbstoff does not have fluores-cence signal. However, commonly used FLH product (Hu et al., 2005;

Letelier & Abbott, 1996) is derived from water leaving radiance, whichhas large uncertainty for the reasons as already discussed above, andsuffers from atmospheric correction problems in the coastal waters.As one of the solutions, it might be an advantage to combine this newmethod with Gower et al.'s (2004, 2005) FLH-like maximum chloro-phyll index, which is FLH derived from top-of-atmosphere radiance,hence less affected by atmospheric correction problems.

5. Conclusions

For the purpose of mitigating fisheries' damage and economic lossdue to summer HABs in the western part of Seto-Inland Sea, Japan, asimple satellite remote sensing-based HAB detection method to beused with standard MODIS data (as the SeaWiFS mission has ended)was developed based on MODIS nLw spectral shape discriminationand empirical relationships. The strength of this new method is, al-though it does not require indigenous atmospheric correction scheme,the method is expected to be able to classify K. mikimotoi blooms, dia-tom blooms, TSM-dominated waters, gelbstoff-dominated waters, andmixed waters in the optically complex coastal waters. Although ournew method could successfully capture massive independent summer2003 K. mikimotoi bloom, the success of this method in delineatingother HAB-forming algae in the other regions seems to depend on thesimilarity of other algae backscattering signatures and pigment packag-ing to K. mikimotoi's low backscattering signature and pigmentpackaging.

Acknowledgments

This work was supported by Coordination Funds for PromotingSpace Utilization from the Japanese Ministry of Education, Culture,Sports, Science and Technology (MEXT) and by Global Change Obser-vation Mission — Climate (GCOM-C) from Japan Aerospace Explora-tion Agency (JAXA). We wish to thank three anonymous reviewersfor their valuable and constructive comments to considerably

26 Jul 2003

26 Jul 2003

30 Jul 2003

30 Jul 2003

25-27 Jul 2003 29 Jul-1 Aug 2003

(f) (g)

(c) (d)

(e)

(a)

nLw

(m

W c

m-2

m-1

sr-1

)

(b)

Wavelength (nm)

K. mikimotoi bloom

Non-red tide/clear

water

Diatom bloom

TSM-dominated

water

Gelbstoff-dominated

water

Mixed water

Land

Cloud/no data

Fig. 10. MODIS standard Chl-a during summer 2003 (a, b) and corresponding red tide maps (c, d) derived with our new HAB detection method to detect Karenia mikimotoi blooms.White square in (c) or (d) is the area delineating K. mikimotoi cell count maps in (f) and (g). Yellow rectangle in (d) or (g) is the area from which the MODIS nLw spectra forK. mikimotoi bloom (30 July 2003) was derived (e).

195E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

improve the first version of this paper. We would also like to thankthe captain and crews of Hoyo Maru for assistance in the field obser-vations and logistical support. We thank the Ocean Biology ProcessingGroup (Code 614.2) at the GSFC, Greenbelt, Maryland, USA, for theproduction and distribution of the ocean color data.

References

Ahn, Y. -H., & Shanmugam, P. (2006). Detecting the red tide algal blooms from satelliteocean color observations in optically complex Northeast-Asia coastal waters. Re-mote Sensing of Environment, 103, 419–437.

Ahn, Y. H., & Shanmugam, P. (2007). Derivation and analysis of the fluorescence algo-rithm to estimate phytoplankton pigment concentration in optically complexcoastal waters. Journal of Optics A: Pure and Applied Optics, 9, 352.

Anderson, D. M., Reguera, B., Pitcher, G. C., & Enevoldsen, H. O. (2010). The IOC interna-tional harmful algal bloom program: History and science impacts. Oceanography,23(3), 72–85.

Babin, M., Morel, A., & Gentili, B. (1996). Remote sensing of sea surface sun-inducedchlorophyll-a fluorescence: Consequences of natural variation in the optical char-acteristics of phytoplankton and the quantum yield of chlorophyll a fluorescence.International Journal of Remote Sensing, 17, 2417–2448.

Cannizzaro, J. P., Carder, K. L., Chen, F. R., Heil, C. A., & Vargo, G. A. (2008). A novel tech-nique for detection of the toxic dinoflagellate, Karenia brevis, in the Gulf of Mexicofrom remotely sensed ocean color data. Continental Shelf Research, 28, 137–158.

Cannizzaro, J. P., Hu, C., English, D. C., Carder, K. L., Heil, C. A., & Muller-Karger, F. E.(2009). Detection of Karenia brevis blooms on the west Florida shelf using in situbackscattering and fluorescence data. Harmful Algae, 8, 898–909.

Carder, K., & Steward, R. G. (1985). A remote-sensing reflectance model of a red-tidedinoflagellate off west Florida. Limnology and Oceanography, 30, 286–298.

Cetinic, I. (2009). Harmful algal blooms in the urbanized coastal ocean: An applicationof remote sensing for understanding, characterization, and prediction, Ph.D. Dis-sertation, University of Southern California, 224 pp.

196 E. Siswanto et al. / Remote Sensing of Environment 129 (2013) 185–196

Chen, Y., Yan, T., Yu, R., & Zhou, M. (2011). Toxic effects of Karenia mikimotoi extractson mammalian cells. Chinese Journal of Oceanology and Limnology, 29(4),860–868, http://dx.doi.org/10.1007/s00343-011-0514-8.

Cokacar, T., Oguz, T., & Kubilay, N. (2004). Satellite-detected early summercoccolithophore blooms and their interannual variability in the Black Sea. DeepSea Research Part I: Oceanographic Research Papers, 51, 1017–1031.

Darecki, M., & Stramski, D. (2004). An evaluation of MODIS and SeaWiFS bio-optical al-gorithms in the Baltic Sea. Remote Sensing of Environment, 89, 326–350.

Gentien, P., Lunven, M., Lazaru, P., Youenou, A., & Crassous, M. P. (2007). Motility andautotoxicity in Karenia mikimotoi (Dinophyceae). Philosophical Transactions of theRoyal Society, 362, 1937–1946.

Gower, J., Brown, L., & Borstad, L. (2004). Observation of chlorophyll fluorescence inwest coast waters of Canada using the MODIS satellite sensor. Canadian Journal ofRemote Sensing, 30(1), 17–25.

Gower, J., King, S., Borstad, G., & Brown, L. (2005). Detection of intense planktonblooms using the 709 nm band of the MERIS imaging spectrometer. InternationalJournal of Remote Sensing, 26(9), 2005–2012.

Hu, C., Muller-Karger, F. E., Taylor, C., Carder, K. L., Kelble, C., Johns, E., et al. (2005). Redtide detection and tracing using MODIS fluorescence data: A regional example inSW Florida coastal waters. Remote Sensing of Environment, 97, 311–321.

Hyde, K. J. W., O'Reilly, J. E., & Oviatt, C. A. (2007). Validation of SeaWiFS chlorophyll ain Massachusetts Bay. Continental Shelf Research, 27, 1677–1691.

Ishizaka, J., Kitaura, Y., Touke, Y., Sasaki, H., Tanaka, A., Murakami, H., et al. (2006). Satellitedetection of red tide in Ariake Sound, 1998–2001. Journal of Oceanography, 62, 37–45.

Jerlov, N. G. (1976). Marine optics. New York: Elsevier (231 pp.).Kirk, J. T. O. (1994). Light and photosynthesis in aquatic ecosystems (2nd ed.). New York:

Cambridge University Press (509 pp.).Komick, N. M., Costa, M. P. F., & Gower, J. (2009). Bio-optical algorithm evaluation for

MODIS for western Canada coastal waters: An exploratory approach using in situreflectance. Remote Sensing of Environment, 113, 794–804.

Lee, Z. P., Carder, K. L., & Arnone, A. (2002). Deriving inherent optical properties fromwater color: A multiband quasi-analytical algorithm for optically deep waters. Ap-plied Optics, 41(27), 5755–5772.

Letelier, R. M., & Abbott, M. R. (1996). An analysis of chlorophyll fluorescence algo-rithms for the moderate resolution imaging spectrometer (MODIS). Remote Sensingof Environment, 58, 215–223.

Maldonado, D. J. C. (2008). Spectral properties and population dynamics of the harmfuldinoflagellate Cochlodinium polykrikoides (Margalef) in southern Puerto Rico, Ph.D.Dissertation. University of Puerto Rico, 166 pp.

Miyamura, K., Takafumi,M., & Kanazawa, K. (2005). Characteristics of a red tide due to theharmful dinoflagellate Karenia mikimotoi occurred in Usuki Bay, Oita Prefecture, in2003. Bulletin of the Japanese Society of Fisheries Oceanography, 68(2), 91–98.

O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., et al.(1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Re-search, 103(C11), 24937–24953.

Sasaki, H., Tanaka, A., Iwataki, M., Touke, Y., Siswanto, E., Tan, C. K., et al. (2008). Opticalproperties of the red tide in Isahaya Bay, Southwestern Japan: Influence of chloro-phyll a concentration. Journal of Oceanography, 64, 511–523.

Senjyu, T., Yasuda, H., Sugihara, S., & Kamizono, M. (2001). Current and turbidity vari-ations in the western part of Suo-Nada, the Seto Inland Sea, Japan: A hypothesis ofthe oxygen-deficient water mass formation. Journal of Oceanography, 57, 15–27.

Siswanto, E., Tang, J., Yamaguchi, H., Ahn, Y. H., Ishizaka, J., Yoo, S., et al. (2011). Empir-ical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, andcolored dissolved organic matter absorption coefficient in the Yellow and EastChina Seas. Journal of Oceanography, 67, 627–650.

Staehr, P. A., & Cullen, J. J. (2003). Detection of Karenia mikimotoi by spectral absorp-tion signatures. Journal of Plankton Research, 25(10), 1237–1249.

Steidinger, K. A., & Haddad, K. D. (1981). Biologic and hydrographic aspects of red tides.Bioscience, 31, 814–819.

Stumpf, R. P., Culver, M. E., Tester, P. A., Tomlinson, M., Kirkpatrick, G. J., Pederson, B. A.,et al. (2003). Monitoring Karenia brevis blooms in the Gulf of Mexico using satelliteocean color imagery and other data. Harmful Algae, 2(2), 147–160.

Suzuki, R., & Ishimaru, T. (1990). An improved method for the determination of phyto-plankton chlorophyll using N,N-dimethylformamide. Journal of Oceanography, 46,190–194.

Takahashi, W., Kawamura, H., Omura, T., & Furuya, K. (2009). Detecting red tides in theEastern Seto Inland Sea with satellite ocean color imagery. Journal of Oceanography,65, 647–656.

Tan, C. K., Ishizaka, J., Matsumura, S., Yusoff, F.Md., &Mohamed,M. I. (2006). Seasonal var-iability of SeaWiFS chlorophyll a in the Malacca Straits in relation to Asian Monsoon.Continental Shelf Research, 26, 168–178, http://dx.doi.org/10.1016/j.csr.2005.09.008.

Tassan, S. (1994). Local algorithms using SeaWiFS data for the retrieval of phytoplank-ton pigments, suspended sediment, and yellow substance in coastal waters. Ap-plied Optics, 33, 2369–2378.

Tomlinson, M. C., Stumpf, R. P., Ransibrahmanakul, V., Truby, E. W., Kirkpatrick, G. J.,Pederson, B. A., et al. (2004). Evaluation of the use of SeaWiFS imagery fordetecting Karenia brevis harmful algal blooms in the eastern Gulf of Mexico. RemoteSensing of Environment, 91(3–4), 293–303.

Tomlinson, M. C., Wynne, T. T., & Stumpf, R. P. (2009). An evaluation of remote sensingtechniques for enhanced detection of the toxic dinoflagellate, Karenia brevis. Re-mote Sensing of Environment, 113, 598–609.

Whitmire, A. L., Pegau, W. S., Boss, L. K., Boss, E., & Cowles, T. J. (2010). Spectral back-scattering properties of marine phytoplankton culture. Optics Express, 18(14).

Wozniak, S. B., & Stramski, D. (2004). Modeling the optical properties of mineral parti-cles suspended in seawater and their influence on ocean reflectance and chloro-phyll estimation from remote sensing algorithms. Applied Optics, 43, 3489–3503,http://dx.doi.org/10.1364/AO.43.003489.

Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J., et al.(2008). Relating spectral shape to cyanobacterial bloom in the Laurentian GreatLakes. International Journal of Remote Sensing, 29(12), 3665–3672.

Yosie, N., Guo, X., Fujii, N., & Komorita, T. (2011). Ecosystem and nutrient dynamics inthe Seto Inland Sea, Japan. In K. Omori, X. Guo, N. Yoshie, N. Fujii, I. C. Handoh, A.Isobe, & S. Tanabe (Eds.), Interdisciplinary studies on environmental chemistry(pp. 39–49).