Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI

Remote Sensing of Environment 140 (2014) 562–572

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Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI Xiulin Lou a,b,⁎, Chuanmin Hu b a b

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA

a r t i c l e

i n f o

Article history: Received 16 April 2013 Received in revised form 8 September 2013 Accepted 23 September 2013 Available online 19 October 2013 Keywords: Harmful algal blooms Red tides East China Sea GOCI Remote sensing Diurnal change Prorocentrum donghaiense

a b s t r a c t Harmful algal blooms (HABs) in the East China Sea (ECS) have been reported every year in the last decade, and satellite remote sensing has often been used to study the bloom size and duration. Yet satellite remote sensing suffered from lack of reliable algorithms to detect HABs in optically complex coastal waters and from frequent cloud cover. Thus, it has not been possible to document short-term changes of HABs in synoptic scales. Here, using measurements from the Geo-stationary Ocean Color Imager (GOCI), we studied diurnal changes of a HAB of the dinoflagellate Prorocentrum donghaiense in the ECS in May 2011. The standard remote sensing reflectance (Rrs) products from the GDPS processing algorithms showed promise in delineating HABs in turbid coastal waters, yet the cloud-masking is often too tight to reveal valid cloud-free data. An alternative approach was developed to circumvent this difficulty by using the Rayleigh-corrected reflectance (Rrc) and a normalization technique. A modified red tide index (RI) was developed from the normalized Rrc data at 443, 490 and 555 nm, and proven effective in delineating the P. donghaiense bloom in sediment-rich waters. The hourly RI images on 29 and 30 May 2011 (8 images per day from 8:30 to 15:30) showed consistent bloom evolution through the course of a day, with physical locations driven by tides while its surface expression increased significantly from early morning to early afternoon (maximum around 14:30 local time, a 124% and 163% increase from 9:30, respectively). The maximum coverage of the HAB at 14:30 on 29 May 2011, when no cloud was observed, reached 6620 km2. While the short-term changes in the surface expression could be a result of the horizontal dilution due to tides, vertical migration of the dinoflagellate from early morning to afternoon, as reported elsewhere, may be a dominant reason. The case study here demonstrates the unique value of a geostationary satellite ocean color sensor in revealing short-term dynamics of HABs. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Harmful algal blooms (HABs; or commonly known as red tides by the public) have been reported in the East China Sea (ECS) every year in the last decades (SOA, 2001–2011). In particular, coastal waters off Zhejiang often suffered the most from HABs (Fig. 1). Of the N40 HAB species identified in the ECS, the dinoflagellate Prorocentrum donghaiense is the most common specie found in Zhejiang coastal waters (Tang, Di, et al., 2006; Wang & Wu, 2009). Although P. donghaiense is now considered to be non-toxic (Lu, 2001; Lu et al., 2005), blooms of P. donghaiense affects the coastal aquatic environment through high biomass accumulations, often causing water discoloration and water quality deterioration. P. donghaiense may also form blooms together with Karenia mikimotoi, one of the principal toxin-producing species in the ECS (Lu, 2001; Wang & Wu, 2009). Thus, it is important to have accurate ⁎ Corresponding author at: State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China. Tel.:+86 13777415872. E-mail address: [email protected] (X. Lou). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.09.031

and timely information on the occurrence of P. donghaiense blooms, including their initiation, evolution, and dissipation. Considerable effort has been spent on monitoring and studying HABs in the ECS in the past decades. The first HAB event in Zhejiang coastal waters was documented in 1933, yet only limited HAB records were available before 1990 for all coastal waters of China (Fei, 1952; Xia, Lu, Zhu, & Du, 2007; Zhou, Zhu, & Zhang, 2001). In the 1990s, a number of studies have been reported to focus on the description of HAB events (causative species, location and size) in the ECS (Lu & Zhang, 1996; Xu, Hong, Wang, & Shen, 1994; Zhou et al., 2001). Since 2000, an extensive HAB monitoring network, coordinated by government agencies responsible for marine environmental monitoring, has been established, and annual reports on HAB occurrence have been issued by the State Oceanic Administration of China (Wang & Wu, 2009). More intensive studies of HABs in the ECS have also been reported (Tang, Di, et al., 2006; Wang & Huang, 2003; Wang & Wu, 2009; Zhou, Yan, & Zhou, 2003). During 2001–2006, a national interdisciplinary project, known as the Ecology and Oceanography of Harmful Algal Blooms in China (CEOHAB), has been conducted to study the ecology and dynamics of HABs in the ECS (Zhou & Zhu, 2006).

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Fig. 1. Map of the Zhejiang coast in the East China Sea. Overlaid are bathymetric isobaths from 30 to 100 m, the study area outlined in red, and a tide prediction station (red star) near Wenzhou. Also overlaid are the locations (green dots) where high concentrations of the HAB species P. donghaiense were reported in 2011 by the State Oceanic Administration of China and Zhejiang Provincial Ocean and Fisheries Bureau (SOA, 2001–2011; ZPOFB, 2011).

Among these recent efforts are those dedicated to develop methods to forecast HAB occurrence (Lin & Liang, 2002; Wang, Ge, & Li, 2006). A critical component in establishing a forecast system is HAB observation, which provides initial conditions and validation data for the system. Timely and accurate HAB observations also help coordinate field campaigns and help understand bloom dynamics. While traditional field sampling techniques provide accurate information on HABs, they are usually scarce in both time and space (Ahn & Shanmugam, 2006; Stumpf & Michellec, 2005). Satellite remote sensing, on the other hand, provides more synoptic and repeated measurements of the surface ocean, thus complementing the limited field observations for HAB detection and understanding as well as for impact assessment (Cullen, Ciotti, Davis, & Lewis, 1997; Schofield et al., 1999; Tester & Stumpf, 1998). Several studies have used satellite remote sensing to study HABs in the ECS (Ahn & Shanmugam, 2006; Huang & Lou, 2003; Lei et al., 2011; Lou et al., 2006; Xu, Pan, Mao, Tao, & Liu, 2012), with the emphasis on the development of local algorithms to detect algal blooms in optically complex waters. However, in general satellite ocean color remote sensing suffers from two difficulties for turbid coastal waters: lack of reliable algorithms for HAB detection and quantification, and lack of temporal coverage due to clouds. These problems are particularly prominent for coastal water in the ECS because the water often contains high concentrations of resuspended sediments, and persistent cloud cover often prevails over most of the ECS.

The recent launch of a geo-stationary ocean color sensor by the Korea Ocean Satellite Center (KOSC), namely the Geo-stationary Ocean Color Imager (GOCI), makes it possible to overcome the cloud cover problem to increase temporal coverage (Ryu, Han, Cho, Park, & Ahn, 2012). With a 500-m nadir resolution, GOCI was launched on 27 June 2010 in a geostationary orbit, providing eight measurements a day over the Northeast Asian region from 9:30 to 16:30 Korea time. In comparison, polar-orbiting ocean color satellites (e.g., SeaWiFS, MODIS, and MERIS) measure the subtropical and tropical oceans once every 2–3 days. The unprecedented high-frequency measurements of GOCI not only increased cloud-free observations, but also provided data critical to understand short-term dynamics in highly dynamic environments. Preliminary studies showed the potentials of using GOCI data to study diurnal changes in suspended sediments, chlorophyll-a concentrations (Chl), and floating macroalgae patches (Choi et al., 2012; Lee et al., 2012; Son, Min, & Ryu, 2012). However, application of GOCI data to study diurnal changes of HABs has not been reported. A P. donghaiense bloom off Zhejiang coast from mid-May to early June 2011 provided an excellent opportunity to test GOCI's potentials in studying the diurnal evolution of HABs in a turbid-water environment. The 23-day bloom near Wenzhou and Taizhou caused fish mortality and adverse impact on local economy as well as on the marine environment (SOA, 2001–2011; ZPOFB, 2011). Given the availability of hourly GOCI measurements concurrent to the reported P. donghaiense

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bloom event, the study here had the following objectives: 1) To develop a practical method using GOCI data for HAB detection and tracking in optically complex coastal waters of the ECS; and 2) To study diurnal changes of the P. donghaiense bloom in the ECS in 2011. 2. Data and methods 2.1. GOCI data processing GOCI Level-1B data for April and May 2011 were obtained from the Korea Ocean Satellite Center (KOSC) (http://kosc.kordi.re.kr/). After examining data coverage, 29 and 30 May 2011 were found to contain minimal cloud cover, which were also coincident with the reported P. donghaiense bloom in the study area, and therefore were selected for this study. Eight hourly measurements were available for each day from 8:30 to 15:30 local time. The Level-1B data were first processed to Level-2 data using the GOCI data processing software (GDPS) with default parameters and standard atmospheric correction (Ryu & Ishizaka, 2012), resulting in the spectral remote sensing reflectance (Rrs) products. Ahn and Shanmugam (2006) proposed a red tide index (RI) to identify potential algal blooms in optically complex Northeast-Asia coastal waters from SeaWiFS imagery. The RI is based on the satellitederived water-leaving radiance in the blue and green bands. Application of the RI method to GOCI data showed that although it could delineate blooms in some cases, it was difficult to distinguish blooms in sediment-rich turbid waters. Hence, in this study a modified RI was calculated from Rrs at 443, 490 and 555 nm as follows (see rationale in Section 2.2):

RI ¼

Rrs ð555Þ−Rrs ð443Þ : Rrs ð490Þ−Rrs ð443Þ

ð1Þ

The design of the modified RI is the same as the principle to use blue/ green band ratio to derive chlorophyll-a concentrations (Chl in mgm−3),

11:30 29 May2011 GOCI true color image

but with a subtraction of Rrs(443) to suppress the effect of resuspended sediments. Fig. 2 shows two examples of the GOCI RGB true color images, RI images, and delineated HAB images under two different conditions: clear and hazy atmospheres. Although the method appears effective in delineating dark water patches from sediment-rich coastal waters under a clear atmosphere (Fig. 2a–c), the cloud-masking of GDPS is too tight to reveal valid cloud-free data (Fig. 2d–f), possibly because that water turbidity exceeded the range implemented in GDPS (Lee et al., 2012). Clearly, before a new cloud-masking algorithm is implemented, an alternative approach must be developed to circumvent this difficulty. For such a purpose, an approach using the Rayleigh-corrected reflectance (Rrc) and a normalization technique was developed. First, GOCI Level-1B data were corrected by removing the effects due to ozone absorption and molecular (Rayleigh) scattering, yielding Rayleigh-corrected reflectance Rrc(λ) (Hu, 2009; Hu, Cannizzaro, Carder, Muller-Karger, & Hardy, 2010):

Rrc ¼

πLt −Rr F 0 cos θ0

ð2Þ

where L∗t is the calibrated sensor radiance after adjustment for ozone absorption, F0 is the extraterrestrial solar irradiance at data acquisition time, θ0 is the solar zenith angle, and Rr is the Rayleigh reflectance. The correction of Rayleigh scattering would remove most of the atmospheric “color”, because aerosol reflectance is more spectrally flat (Hu et al., 2010). However, because aerosol contributions may vary over the course of a day, Rrc data were further normalized to relatively clear water in the image in order to obtain temporally consistent data. Assuming that the offshore clear water is stable with minimal diurnal changes in reflectance, any observed Rrc changes over clear water must be due to changes in the residual atmospheric effects (ΔRrc), as shown in Fig. 3a. Fig. 3a shows that Rrc over clear water between 11:30 and 14:30 is stable, whose average can be used as a reference. The difference between Rrc over clear water from each measurement

11:30 29 May2011 GOCI red tide image

11:30 29 May2011 GOCI RI image

Taizhou Wenzhou

a 15:30 29 May2011 GOCI true color image

b 15:30 29 May2011 GOCI RI image

c 15:30 29 May2011 GOCI red tide image

Taizhou Wenzhou

d

e

f

Fig. 2. GOCI images on 29 May 2011 (27°N to 29°N, 120°E to 122.5°E) showing HAB detection results under two scenarios: clear atmosphere (a–c) and hazy atmosphere (d–f). The P. donghaiense bloom near Wenzhou and Taizhou appears dark in the RGB true color images (a and d). An artificial transect (red line) is overlaid on the images, where reflectance and other properties are extracted and examined. The RI images were derived from GOCI Rrs data using Eq. (1), and HAB patches were delineated with a RI threshold of 4.0. White color represents invalid data or cloud mask, and gray color represents non-bloom waters.

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Fig. 3. (a) GOCI Rrc(555) before normalization on 29 May 2011 along the nearshore–offshore transect (Fig. 2a). (b) nRrc(555) after normalization from the same transect.

and the mean reference was then derived as ΔRrc. Finally, Rrc data were normalized to the clear-water reference as nRrc ¼ Rrc −ΔRrc :

ð3Þ

Fig. 3b shows the nRrc data at 555 nm along the same transect as in Fig. 3a. After normalization, nRrc data over both clear and turbid waters are more consistent over time. These data were used to classify the P. donghaiense HAB after cloud pixels were masked using the method of Nordkvist, Loisel, and Gaurier (2009). This method makes use of the spectral variability of clouds compared to water, and it can recover turbid-water pixels that would otherwise be considered as clouds. In this study, a spectral variability ratio threshold of 2.5 from the GOCI nRrc data was used to mask clouds. 2.2. Red tide index (RI) P. donghaiense blooms cause water discoloration from May to early June nearly every year along the Zhejiang coast in the ECS (Zhou & Zhu, 2006). In areas where P. donghaiense concentrations exceeded 106 cells L−1, surface water appeared brownish red (Lu, 2001) due to reduced blue reflectance and enhanced red reflectance. However, distinguishing P. donghaiense blooms in turbid coastal waters is not trivial, as other water constituents especially suspended sediments in this

area can also cause changes in reflectance (Ahn & Shanmugam, 2006). Thus, after examining the reflectance spectral shapes of various waters (turbid non-bloom water, bloom water, and clear water), a modified RI was developed to mimic the blue–green band ratio design for Chl retrievals while taking account of the sediment effect by normalizing to 443 nm, with the analytical form defined in Eq. (1). Fig. 4 compares RI derived from both Rrs(λ) and nRrc(λ) along the nearshore–offshore transect shown in Fig. 2a. Clearly, they both suppress the effect of suspended sediments, yielding similar RI patterns along the nearshore–offshore transect. For sediment-rich waters Rrs(443) and nRrc(443) are enhanced, leading to low RI values. For bloom waters, reflectance at 555 nm increased while at 490 nm and 443 nm decreased, leading to higher RI values. Thus, RI derived from nRrc data can be used as a bloom indicator for sediment-rich turbid waters. Further, because of the a priori knowledge that the bloom was due to P. donghaiense, in this study RI was used as the cell concentration surrogate for P. donghaiense. 2.3. Tide data The study area is influenced by semidiurnal tides, with approximately two high tides and two low tides per day. For the two study days (29–30 May 2011), hourly tide height data for the study region were collected from local tide tables issued by the National Marine Data and Information

Fig. 4. RI along the nearshore–offshore transect (Fig. 2a) calculated from the GOCI Rrs and nRrc data from the 11:30 measurement on 29 May 2011.

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Service (NMDIS) of China for the tide station shown in Fig. 1. The data were from numerical tidal modeling with an accuracy of better than 0.3 m (NMDIS, 2010). Given the large tidal range (~5 m), this accuracy is deemed acceptable for studying diurnal changes of blooms.

Zhejiang coast between May and June 2011 (Fig. 1). These records included the location, start time, end time, areal extent, species, and algal cell concentration. The information of 3 P. donghaiense bloom records covering the two study days (29–30 May 2011) was used as the ground truth data to validate HAB delineation on GOCI imagery.

2.4. HAB data Since 2001, a HAB monitoring network covering the ECS has been operated (Tang, Di, et al., 2006; Wang & Wu, 2009, http://www. dhjczx.org/). Annual reports on HAB occurrence in the ECS have been issued by the State Oceanic Administration of China (http://www.soa. gov.cn/) and by the Zhejiang Provincial Ocean and Fisheries Bureau (http://www.zjoaf.gov.cn/). Reports on HAB occurrence (timing, location, intensity) in the ECS have been obtained from the above sources and from other public data sources such as local media (e.g., http://www.wzrb.com.cn). From these reports, 11 P. donghaiense bloom records were found along the

3. Results 3.1. HAB detection using GOCI RI data Fig. 5 shows the 8 RGB images and 8 corresponding RI images derived from hourly nRrc data on 29 May 2011. The RGB images showed hazy atmosphere around 8:30 and 15:30, yet the series of RI images showed distinctive spatial patterns of all the coastal waters under all conditions. The diurnal changes of the bloom patterns, as indicated by the high RI values (RI N 2.8, yellow–red colors on the images), can also

08:30 29 May 2011

09:30 29 May 2011

10:30 29 May 2011

11:30 29 May 2011

12:30 29 May 2011

13:30 29 May 2011

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09:30 29 May 2011

10:30 29 May 2011

11:30 29 May 2011

12:30 29 May 2011

13:30 29 May 2011

14:30 29 May 2011

15:30 29 May 2011

Fig. 5. Hourly RGB true color images and RI images from GOCI measurement on 29 May 2011. On the RGB images, the P. donghaiense bloom appears darker than the surrounding sedimentrich waters. On the RI images, the bloom corresponds to high RI values (N2.8, yellow–red colors). A region of interest is outlined in red, from which data were extracted and analyzed to study diurnal changes.

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08:30 30 May 2011

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11:30 30 May 2011

12:30 30 May 2011

13:30 30 May 2011

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Fig. 6. Same as in Fig. 5, but GOCI measurements were taken on 30 May 2011. A region of interest is outlined in red, from which data were extracted and analyzed to study diurnal changes.

be visualized. It appears that the bloom size reached the maximum at 14:30, with horizontal movement between 8:30 and 15:30. Fig. 6 shows RGB and RI images for 30 May 2011. Except for some areas masked by the scattered clouds in the afternoon, bloom patterns as well as their temporal changes are revealed in the RI images, which appear similar to those observed on 29 May 2011 (Fig. 5). Overall, RI appears to be an effective index to delineate bloom patterns in sediment-rich waters. 3.2. Validation Ideally, field measured cell concentrations on the same days of GOCI measurements are required to validate the HAB interpretations from the GOCI RI images in Figs. 5 and 6. However, this was impossible due to lack of precise measurements on those days or nearby days following established protocols for phytoplankton taxonomy. Therefore, qualitative validation was performed using various reports and digital photos from the red tide monitoring network.

In 2011, there were 55 reports of HAB occurrence in the ECS, of which 11 P. donghaiense blooms were reported along the Zhejiang coast (SOA, 2001–2011). The first report of P. donghaiense bloom was on 10 May 2011 along the north Fujian coast, south of the Zhejiang coast. Two days later on 12 May, a P. donghaiense bloom was reported along the south Zhejiang coast, extending from south of Wenzhou to other Zhejiang coastal waters. On 17 May, blooms were reported near Taizhou and Zhoushan (Fig. 1), followed by continuous reports until early June when the bloom disappeared near Wenzhou. The highest P. donghaiense cell concentrations from these reports was 6.1 × 107 cells L− 1, enough to cause water discoloration and other ecological effects (e.g., fish mortality). The numerous reports, based on a variety of field observations, qualitatively validated the delineated HAB patterns on the RI images, as the reported dates and locations generally agreed with those shown in the RI images (compare Fig. 1 and Figs. 5 and 6). For example, Fig. 7a shows the HAB pattern delineated on the 29 May RI image (11:30) where three reports of HAB occurrence (marked as 1–3 on

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3

2 1 a

b

Fig. 7. (a) Distributions of the P. donghaiense bloom (RI N 2.8) on 29 May 2011, 11:30. Overlaid on the map are two bathymetry isobaths (30 and 50 m) and three locations of reported bloom occurrence. (b) A photo taken during the bloom near location #1 showing the P. donghaiense bloom as discolored brownish water.

this image) all coincided with the high RI values. A spot photo taken during the bloom near location #1 also showed discolored bloom patches (Fig. 7b). All these evidence suggest that the bloom patterns delineated from the RI images should be generally valid, although the exact cell concentrations are difficult to determine from the satellite data alone. 3.3. Diurnal changes of the P. donghaiense bloom The qualitatively validated RI images were used to derive diurnal changes in bloom size, which provided important information on the formation and decay of HABs in coastal oceans (Kang et al., 2004). To avoid cloud-cover induced artifacts, for each of the two days, a region of interest was selected to contain cloud-free observation for all 8 hourly images, and the bloom size was estimated as the total number of bloom pixels (each pixel is 0.5 × 0.5 km2), where bloom pixel was defined as RI N 2.8 and RI N 2.2, respectively, to test the sensitivity of the results to the choice of the RI threshold. Fig. 8 shows the diurnal changes of the bloom size for the selected subareas on 29 and 30 May 2011, respectively. Regardless of the RI threshold used, the bloom size on both days increased

significantly from early morning to early afternoon. Using the RI threshold of 2.8, the bloom size on 29 May 2011 increased from ~2000 km2 around 8:30–9:30 to ~4480 km2 around 14:30 (a 124% increase) for the subarea. Similarly, on 30 May 2011 the bloom size increased from 160 km2 at 9:30 to 420km2 at 14:30 (a 163% increase) for the subarea. Using a threshold of 2.2 changed the bloom size estimates, but the relative temporal patterns within a day remained stable. Note that the subareas were selected to study diurnal changes to exclude cloud covers. For the entire study region the bloom size was significantly higher than shown in Fig. 8. For the cloud-free measurements on 29 May 2011, the bloom size (using RI=2.8 as the threshold) changed from 3090 km2 around 9:30 to 6620 km2 around 14:30. In addition to the diurnal changes of the bloom size, the spatial movements of the bloom could also be observed from the RI image sequence. Fig. 9 shows the RI values along the nearshore–offshore transect line (Fig. 2a) extracted from the 29–30 May 2011 RI images. The W–E movement of the bloom, as indicated by the high RI values, is apparent. On 29 May 2011, the bloom moved about 6.0 km eastward from 8:30 to 15:30, with the mean velocity of ~0.85 km h−1 during this period and maximum velocity of 2 km h−1 between 9:30 and 10:30. On 30

Fig. 8. Bloom size (in km2) determined from the RI images as a function of time on 29 May 2011 (a) and 30 May 2011 (b). Although the size changes with the RI threshold (2.8 or 2.2), the temporal patterns remain relatively stable, with maxima around 14:30 on both days. Note that the size was not estimated from the entire image due to the changing cloud cover, but estimated from a small region that was cloud-free for all measurements (outlined by the rectangular boxes in Figs. 5 and 6, respectively).

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a

Turbid water

Bloom water

Turbid water

Clear water

b

Turbid water Turbid water

Clear water

Bloom B water

Fig. 9. RI along the transect line in Fig. 2a on 29 May 2011 (a) and 30 May 2011 (b). The W–E movement of the bloom along this transect over time, as indicated by the high RI values, is apparent.

May 2011, the bloom movement from 8:30 to 15:30 was also eastward for about 5.5 km, with a mean velocity of 0.79 km h−1 and maximum velocity of 2 km h−1 also between 9:30 and 10:30. 4. Discussion 4.1. Algorithm accuracy Detecting and quantifying phytoplankton blooms in optically complex coastal waters have been a problem and also an active research area within the ocean color research community. Empirical blue/green band-ratio algorithms do not differentiate Chl from CDOM or sediments, and therefore not applicable here. Semi-analytical algorithms, which are designed to separate these optical constituents explicitly, also suffer from residual errors due to imperfect atmospheric correction, which are most prominent in the blue wavelengths for coastal waters. While focusing on fluorescence bands (e.g., 678 nm for MODIS and 681 nm for MERIS) may circumvent this difficulty in CDOM-rich waters (Hu et al., 2005), in sediment-rich waters such an approach also fails because of the enhanced backscattering (Gilerson et al., 2007; McKee, Cunningham, Wright, & Hay, 2007). The RI approach presented here

(Eq. 1) takes advantage of an additional band in the blue/green band ratio to suppress the effect of sediment interference on bloom detection algorithms. Application of the approach to GOCI data, as qualitatively validated by field observations, showed the effectiveness of the approach in separating sediment-rich waters from blooms waters. Due to lack of concurrent field data with precise measurements of either Chl or phytoplankton cell concentrations, it is impossible to quantitatively validate the observed high RI values. However, the various reports and spot photos qualitatively validated the bloom locations. Most importantly, the diurnal changes in bloom size appeared to be insensitive to the choice of the RI threshold to delineate blooms. Thus, while future targeted studies are required to perform more rigorous validation of the RI approach, the relative changes in bloom size from early morning to early afternoon should be realistic in reflecting the bloom's diurnal evolution. Note that the analysis here was based on normalized Rayleighcorrected reflectance (nRrc) because the default GDPS software package often created a cloud mask over non-cloud pixels associated with either hazy atmosphere or extremely turbid water, leading to no valid data over such cloud-free pixels. Ideally, Rrs data should be used once the radiometric calibration and atmospheric correction are improved.

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Fig. 10. Vertical profiles of Chl as a function of time, determined from a shipborne mesocosm experiment of P. donghaiense on 22–23 May 2006 (Wang, Li, et al., 2006). Reproduced from the CEOHAB program report.

The most recent advances on GOCI calibration and atmospheric correction (Ahn, Park, Ryu, Lee, & Oh, 2012; Wang, Shi, & Jiang, 2012) make it possible to derive Rrs data under hazy atmosphere once the processing codes are available to the community.

4.2. Causes of diurnal changes Within 7 h from 8:30 to 15:30, it is unlikely that phytoplankton growth can explain the observed 100% increase in bloom size. We therefore speculate that phytoplankton vertical migration could be the

reason. Phytoplankton vertical migration has been identified as a competitive strategy for phytoplankton under conditions where light and nutrients are spatially separated (Jephson & Carlsson, 2009). Photosynthetic dinoflagellates tend to stay close to the surface during the light period and migrate through the pycnocline and assimilate nutrients in the deep water at night (Flynn & Fasham, 2002; Kimura, Watanabe, Kohata, & Sudo, 1999). Vertical migration has been found in many HAB species. For example, in the eastern Gulf of Mexico, Karenia brevis population increased during a day (24 h) with concentrations peaking in the late afternoon (16:00 local time) in the upper 2 m of the water column, and the increase in K. brevis concentration in surface waters resulted in enhanced reflectance with distinct spectral changes (Schofield et al., 2006). P. donghaiense has also been found to migrate during the course of a day (Wang & Huang, 2003). A field survey was conducted on 19–20 May 2000 in coastal waters off Zhoushan, which showed P. donghaiense cells in surface waters around noon and in subsurface waters at night, with cell concentrations in surface waters peaked around 14:30. A shipborne mesocosm experiment of P. donghaiense conducted on 22–23 May 2006 by the CEOHAB program (Wang, Li, et al., 2006) also confirmed the diurnal migration patterns, as shown in Fig. 10. During the experiment, Chl in the surface layer increased from 5.6 mg m−3 at 8:00 to ~100.0 mg m−3 at 14:00, a result of the vertical migration of P. donghaiense. Vertical migration of P. donghaiense makes the species more competitive with other algal species on utilizing light and nutrients. The diurnal changing patterns of the bloom, as reveal by the RI image sequence, are consistent with these field-based observations. There, we believe that the GOCI-observed diurnal changes in bloom size are primarily due to P. donghaiense vertical migration, although their migration speed or the vertical profiles cannot be determined here. It is possible that around the peripherals of the bloom patches the surface concentrations of P. donghaiense are below the RI threshold during night and early morning but vertical migration of P. donghaiense from early morning to early afternoon will increase the surface concentrations to be higher than the RI threshold, leading to the maximum bloom size around 14:30. While the changes in bloom size are likely due to vertical migration of P. donghaiense, changes in bloom positions appear to be driven by physical processes, specifically by tides, which can play an important role in modulating the formation and dissipation of HABs through fronts, eddies, and currents (Tang, Kawamura, Oh, & Baker, 2006). Fig. 11 shows the tide height during 29–30 May 2011 in the study area. On both days, all GOCI data were acquired during the ebb tide phase when water was advected from nearshore to offshore. This explains why the bloom location moved eastward on both days (Fig. 9). In addition to horizontal advection, the ebb tide may have also led to dilution or mixing of the bloom water with non-bloom water, another

Fig. 11. Tide height in the study area on 29–30 May 2011. The dots denote the hourly imaging time of GOCI.

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factor affecting the surface expression of the P. donghaiense bloom. A thorough investigation on such a process, however, is beyond the scope of the current study as it will require a numerical modeling experiment with realistic initial boundary conditions and external forcing. 5. Conclusion Knowledge of diurnal changes of HABs is important to understand their short-term dynamics, yet to date only a handful of field-based studies have been reported, as more synoptic and frequent measurements from satellite remote sensing often suffer from lack of reliable algorithms to differentiate blooms in optically complex coastal waters and, more importantly, because of the lack of multiple measurements in a day. The recent launch of the geo-stationary GOCI makes it possible to overcome the latter difficulty once proper algorithms are developed to normalize the observations from early morning to afternoon. The case study here demonstrates such possibilities through documenting diurnal changes of a harmful P. donghaiense bloom in the ECS using GOCI data and a modified RI (red tide index) approach that appears effective in delineating surface bloom patches in the sediment-rich coastal waters off Zhejiang coast in the ECS. The hourly observations from 8:30 to 15:30 revealed consistent diurnal bloom patterns on 29 and 30 May 2011, which showed significant increases in bloom size from early morning to afternoon (maximum around 14:30) and changes in bloom locations. While the latter changes are driven by tides which may have also contributed to the diurnal changes in bloom size through dilution or tidal mixing, vertical migration of the dinoflagellate from early morning to afternoon, as demonstrated from several previous field-based measurements, may be the dominant reason to explain the size change. Targeted field study with concurrent GOCI measurements, however, is required to confirm these satellite based observations. Acknowledgments This research was supported by the National High Technology Research and Development Program of China (2007AA092003), the State Key Development Program for Basic Research of China (2010CB428704), the Science and Technology Commission of Shanghai Municipality (08DZ1206304), the Key Laboratory of Integrated Monitoring and Applied Technology for Marine Harmful Algal Blooms, SOA (MATHAB201302), and the University of South Florida. Support of X. Lou was provided by a visiting scholarship to USF. We thank the Korea Ocean Research and Development Institute (KORDI) for providing satellite data, and thank Lian Feng (USF) for his help in processing GOCI data using the GDPS software. References Ahn, Y. H., Park, Y. J., Ryu, J. H., Lee, B., & Oh, I. S. (2012). Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI). Ocean Science Journal, 47(3), 247–259. Ahn, Y. H., & Shanmugam, P. (2006). Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia coastal waters. Remote Sensing of Environment, 103, 419–437. Choi, J., Park, Y., Ahn, J., Lim, H., Eom, J., & Ryu, J. (2012). GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity. Journal of Geophysical Research, 117, C09004. http://dx.doi.org/10.1029/2012JC008046. Cullen, J. J., Ciotti, A.M., Davis, R. F., & Lewis, M. R. (1997). Optical detection of and assessment of algal blooms. Oceanology and Limnology, 42, 1223–1239. Fei, H. (1952). The cause of red tides. Chinese Journal of Science and Art, 22, 1–3. Flynn, J., & Fasham, R. (2002). A modelling exploration of vertical migration by phytoplankton. Journal of Theoretical Biology, 218(4), 471–484. Gilerson, A., Zhou, J., Hlaing, S., Ioannou, I., Schalles, J., Gross, B., et al. (2007). Fluorescence component in the reflectance spectra from coastal waters. Dependence on water composition. Optics Express, 15, 15702–15721. Hu, C. (2009). A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 113, 2118–2129. Hu, C., Cannizzaro, J., Carder, K., Muller-Karger, F., & Hardy, R. (2010). Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with

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