INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017
http://dx.doi.org/10.1080/01431161.2017.1308034
TECHNICAL NOTE
The effect of phytoplankton pigment composition and
packaging on the retrieval of chlorophyll-a concentration
from satellite observations in the Southern Ocean
Babula Jena
Polar Sciences, ESSO, National Centre for Antarctic and Ocean Research, Ministry of Earth Sciences (MoES),
Goa, India
ABSTRACT
ARTICLE HISTORY
The Antarctic waters are known to be optically unique and the
standard empirical ocean colour algorithms applied to these
waters may not address the regional bio-optical characteristics.
This article sheds light on the performance of current empirical
algorithms and a regionally optimized algorithm (ROA) for the
retrieval of chlorophyll-a (chl-a) concentration from AquaModerate Resolution Imaging Spectroradiometer (Aqua-MODIS)
and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in the
Indian Ocean Sector of Southern Ocean (IOSO). Analysis indicated
that empirical algorithms used for the retrieval of chl-a concentration from Aqua-MODIS and SeaWiFS underestimate by a factor
varying from 2 to 2.9, resulting in underestimation when in situ
chl-a exceeds about 0.3 mg m−3. To explain these uncertainties, a
study was carried out to understand the effect of phytoplankton
pigment composition and pigment packaging on remote-sensing
reflectance (Rrs,λ), from the analysis of phytoplankton-specific
absorption coefficient (aph,*λ). The spatial variation of phytoplankton groups analysed using diagnostics pigments (DP) indicated
shifting of the phytoplankton community structure from offshore
to coastal Antarctic, with a significant increasing trend for diatoms
and a decreasing trend for haptophytes population. The diatomdominated population exhibits lower aph,*λ in the 405–510 nm
region (with relative flattening in 443–489 nm) compared with the
aph,*λ spectra of the haptophytes-dominated population that
peaks near 443 nm. The flattening of aph,*λ spectra for the diatom-dominated population was attributed to its larger cell size,
which leads to pigment packaging (intracellular shading) and in
turn results in higher Rrs,λ. The relationship between pigment
composition (normalized by chl-a) and blue:green absorption
band ratios (aph,*443:aph,*555 and aph,*489:aph,*555) corresponding
to the Aqua-MODIS and SeaWiFS bands showed in-phase associations with most of the pigments such as 19ʹ-hexanoyloxyfucoxanthin, 19ʹ-butanoyloxyfucoxanthin, peridinin, and zeaxanthin. In
contrast, the out-of-phase association observed between the blue:
green absorption ratios and fucoxanthin indicated apparent deviations from the general pigment retrieval algorithms, which
assumes that blue:green ratios vary in a systematic form with
chl-a. The out-of-phase correspondence suggests that the increasing trend of fucoxanthin pigments towards the Antarctic coast was
Received 24 August 2016
Accepted 9 March 2017
CONTACT Babula Jena
[email protected]
ESSO, National Centre for Antarctic and Ocean Research, Ministry
of Earth Sciences (MoES), Headland Sada, Goa 403804, India
© 2017 Informa UK Limited, trading as Taylor & Francis Group
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associated with the decreasing trend of blue:green absorption
ratios and in turn results in higher Rrs,λ. Therefore, an increase in
Rrs,λ leads to underestimation of chl-a from Aqua-MODIS and
SeaWiFS in the IOSO region.
1. Introduction
Sea-surface chlorophyll-a (chl-a) concentration is considered one of the essential parameters for the estimation of primary production and study of carbon dioxide (CO2)
dynamics. The observations are important to link the role of the Southern Ocean pelagic
ecosystem in the global biogeochemical cycle. In situ observations are sparse around the
Southern Ocean because of adverse weather condition, navigational hazards, remote location, and inhospitable environment with high sea states driven by strong winds. Satellite
remote sensing of the Southern Ocean colour provides synoptic and time-series coverage of
near-surface chl-a concentration dynamics affected by seasonality (Johnson et al. 2013). The
synoptic coverage of chl-a concentration is widely used to investigate the dynamics of
regional oceanographic features such as fronts, eddies, gyres, upwelling zones, plumes, and
surface current patterns. These oceanographic features are useful to study the Southern
Ocean ecosystem, which supports large assemblages of phytoplankton, zooplankton, seabirds, seals, and whales. This signifies the need for the retrieval of chl-a from ocean colour
sensors with greater accuracy over the Southern Ocean. However, the retrieval is complicated at the regional and local scales as the spectral inherent optical properties (IOPs) of the
ocean influencing the ocean colour are complex (Sauer et al. 2012).
The satellite-derived chl-a concentration in the Indian Ocean of Southern Ocean
(IOSO) may have large uncertainties owing to its different bio-optical characteristics, in
addition to sparse matchup observations (in situ and satellite), being used for algorithm development and validation. For example, in order to address the regional biooptical characteristics of the IOSO region (60° E to 100° E and 40° S to the Antarctic
coast) for retrieval of chl-a concentration, only 15 in situ bio-optical profiles are
available at National Aeronautics and Space Administration (NASA), bio-Optical
Marine Algorithm Data set (NOMAD). Mitchell and Holm-Hansen (1991) applied the
Coastal Zone Colour Scanner (CZCS) global algorithm (GA; Gordon et al. 1983) in the
coastal waters of the western Antarctic Peninsula and the adjacent open ocean waters
of Drake Passage. They noticed underestimation of CZCS-derived chl-a and the study
highlighted the need for regional algorithm development and validation. In Ross Sea,
Moore et al. (1999) validated Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-derived
chl-a with in situ-extracted concentration for a range of 0.1–1.5 mg m−3 and the results
revealed an underestimation of the SeaWiFS-derived chl-a value. Carder et al. (2003)
analysed the Southern Ocean chl-a concentration using the SeaWiFS OC4 algorithm
(O’reilly 2000) and revealed significant underestimation of the values between 0.2 and
10.0 mg m−3. Similarly, Korb, Whitehouse, and Ward (2004) reported that the SeaWiFS
OC4 algorithm output values were only 87% of the in situ chl-aflu for concentrations
lower than 1.0 mg m−3 and only 30% for concentrations above 5.0 mg m−3 in the
South Georgia area, Southern Ocean.
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In contrast to earlier studies, Marrari, Hu, and Daly (2006) reported that high-resolution
(1 km) daily SeaWiFS chl-a data generated using the global OC4v4 algorithm was an
accurate measure over the waters of Antarctic Peninsula, if concurrent in situ data were
generated using high-performance liquid chromatography (HPLC). Further investigations
by Szeto et al. (2011) showed that SeaWiFS chl-a using the global OC4v4 algorithm
consistently underestimates the chl-a value by 50%, although HPLC-based measurements
were considered as suggested by Marrari, Hu, and Daly (2006). A recent study by Johnson
et al. (2013) reported that the relationship between the maximum band ratio of remotesensing reflectance (Rrs,λ) and in situ chl-a concentration in the Southern Ocean was poorly
described by global empirical algorithms and concluded that the current algorithms
significantly underestimated the chl-a concentration. They attempted to develop sitespecific algorithms over the entire Southern Ocean for Aqua-Moderate Resolution
Imaging Spectroradiometer (Aqua-MODIS) and SeaWiFS, yet most of the matchup biooptical observations were located approximately along the 150° E meridian. The developed
algorithms could improve the coefficients of determination (R2) from 0.26 to 0.51 for AquaMODIS and from 0.27 to 0.46 for SeaWiFS. The algorithm development process conducted
by Johnson et al. (2013) has not addressed the issues associated with atmospheric corrections. In addition, the algorithm is yet to be validated in the IOSO waters where the regional
and seasonal variations in the environment may lead to different bio-optical characteristics.
Hence, the present study was carried out over the IOSO waters (60° E to 100° E and 40° S to
Antarctic coast, Figure 1) with the detailed objectives as follows: (i) employ the current GA
(O’reilly 2000) and the southern ocean algorithm (SOA) (Johnson et al. 2013) for the
retrieval and validation of chl-a concentration from SeaWiFS and Aqua-MODIS; (ii) analyse
Figure 1. Study area shows ETOPO 1 (Earth Topography One Arc-Minute Global Relief Model, 2009)
bathymetric information along with locations of in situ bio-optical observations and HPLC phytoplankton pigments from station 1 to station 10 (filled circles) collected on cruise on board the R/V
Roger Revelle (I8SI9 N), used for the development of regional optimized algorithm (ROA). In situ
chlorophyll-a concentration collected on cruise on board the R/V Roger Revelle (filled triangles) and
world ocean database (squares) were used for validation of ROA.
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the possibility of developing a regionally optimized algorithm (ROA) for the estimation of
chl-a concentration; (iii) validate the robustness of the developed ROA, and (iv) diagnose
the possible causes of uncertainties in satellite retrieval, particularly the effect of phytoplankton pigment composition on specific absorption coefficient (aph,*λ) through the
pigment package effect.
2. Data analysis and methodology
2.1. Satellite measurements of chl-a concentration using current algorithms
Generating chl-a data from ocean colour satellites involves two major steps, namely the
atmospheric correction of visible bands to derive the water-leaving radiance (Gordon
and Wang 1994a, 1994B; Bailey, Franz, and Werdell 2010) and the development of a
suitable bio-optical algorithm to estimate the chl-a concentration (O’reilly 2000). NASA’s
Ocean Biology Processing Group (OBPG) processes the global Level-0 ocean colour
global data sets in near real-time mode and successively processes to generate
science-quality product of Rrs,λ. In this article, Level-3 global Standard Mapped Images
(SMI) of weekly mean Rrs,λ from Aqua-MODIS and SeaWiFS were acquired from the
website (oceancolor.gsfc.nasa.gov). Afterwards, chl-a concentrations from SeaWiFS and
Aqua-MODIS are retrieved using various algorithms such as (i) GA (O’reilly 2000), (ii) SOA
(Johnson et al. 2013), and (iii) ROA for the IOSO region. The forms of GA, SOA, and ROAM
are described in Equations 1–14.
2.1.1. Global algorithms
2.1.1.1. SeaWiFS, OC4v6
ChlorophyllðchlaÞ ¼ 100:32722:9940R þ 2:7218R 1:2259R
Rrs;443 > Rrs;490 > Rrs;510
;
R ¼ log10
Rrs;555
2
3
0:5683R4
;
(1)
(2)
where Rrs is the remote-sensing reflectance
2.1.1.2. Aqua-MODIS, OC3 M-547
.
2
3
4
ChlorophyllðchlaÞ ¼ 100:24242:7423R þ 1:8017R þ0:0015R 1:2280R :
Rrs;443 > Rrs;490
R ¼ log10
:
Rrs;547
(3)
(4)
The above algorithm (OC4v6) yields a strong correlation coefficient (r = 0.892) with in
situ chl-a concentration on a global scale, which includes samples from all water types
(O’reilly 2000). Nevertheless, the accuracy of this algorithm varies regionally. In addition,
to address the regional differences, various band combinations and regression coefficients were developed for different water types, with similar band-ratio forms. For
example, empirical algorithms were proposed earlier to estimate chl-a concentrations
in the Southern Ocean (Johnson et al. 2013) as described next.
INTERNATIONAL JOURNAL OF REMOTE SENSING
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2.1.2. SOA by JOHNSON ET AL. (2013)
2.1.2.1. SeaWiFS, OC4
.
2
3
ChlorophyllðchlaÞ ¼ 100:67362:0714R0:4939R þ 0:4756R :
Rrs;443 > Rrs;490 > Rrs;510
R ¼ log10
:
Rrs;555
(5)
2.1.2.2. Aqua-MODIS, OC3
.
2
3
ChlorophyllðchlaÞ ¼ 100:69942:0384R0:4656R þ 0:4337R :
Rrs;443 > Rrs;490
R ¼ log10
:
Rrs;547
(7)
(6)
(8)
The chl-a concentration retrieved in the Southern Ocean using the above-mentioned
algorithms yields R2 of 0.51 and 0.46 with in situ chl-a for Aqua-MODIS and SeaWiFS,
respectively (Johnson et al. 2013). However, the performance of these proposed algorithms is yet to be validated in the IOSO region. In this article, uncertainties in the
retrieval of chl-a concentration from GA and SOA were analysed initially by validating
with fluorometrically determined surface in situ chl-aflu during the austral summer
(January–February 2004; January–February–March 2006; and February–March 2007).
High-quality chl-aflu observations were obtained from World Ocean Database (WOD),
National Oceanographic Data Center (NODC), and NOMAD. Details of collection of water
samples, data generation, calibration, and quality control for WOD and NOMAD are
given in Boyer et al. (2009) and Werdell and Bailey (2005), respectively. A total of 76 in
situ chl-aflu observations were available in the study region (Figure 1). The matchups
between in situ and satellite observations were generated based on the optimum
combinations of spatial and temporal averaging strategy (3 × 3 pixel mean and 8 days
mean), a method adopted by several authors (Marrari, Hu, and Daly 2006; Johnson et al.
2013). After implementation of this validation strategy, the number of matchups
reduced significantly (Table 1 and Figure 2).
Table 1. Validation of chlorophyll-a concentration (mg m−3) derived from Aqua-MODIS and SeaWiFS
using various ocean colour (OC) empirical algorithms such as global algorithm (GA), southern ocean
algorithm (SOA; Johnson et al. 2013), and regionally optimized algorithm (ROA).
Aqua-MODIS
SeaWiFS
SOA
GA (OC3) (OC3)
ROA (OC3) GA (OC4) SOA (OC4) ROA (OC2) ROA (OC4)
Coefficient of determination (R2) 0.889
0.42
0.84
0.996
0.98
0.99
0.99
Standard error
0.27
0.10
0.27
0.03
0.11
0.04
0.06
4.2–03
4.62–06
1.42–04
4.95–05
1.39–05
p-value
4.31–05 6.6–03
Slope
0.47
0.14
0.13
0.26
0.33
0.27
0.22
Intercept
0.06
0.33
0.19
0.08
0.17
0.14
0.12
No. of observations
10
10
10
06
06
06
06
The rate of underestimation
2.30
2.35
2.46
2.90
2.01
2.32
2.71
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Figure 2. Observation on significant underestimation of chlorophyll-a concentration from (a) AquaMODIS and (b) SeaWiFS using the current empirical algorithms.
2.2. In situ bio-optical measurements for regional optimized algorithms
(ROAs)
Considering the uncertainties associated with current algorithms (GA, SOA), it was attempted
to develop ROAs by utilizing NOMAD-based in situ bio-optical measurements collected on
cruise on board the R/V Roger Revelle (I8SI9 N) (Figure 1). The measurements were sparse over
the study region having a combination of HPLC- (n = 10) and Fluorometric (n = 05)-determined
chl-a concentration corresponding to the period of 17 February 2007 to 6 March 2007. ChlaHPLC is considered for the development of ROAs because HPLC data sets were largely
collocated with in situ optical observations and it also accomplishes the main scope of the
article: studying the impact of phytoplankton pigment composition on satellite retrieval
algorithms. The coincident chl-aHPLC and in situ water-leaving radiance (Lw,λ) observations
were extracted for 10 observations. Since Rrs,λ is the standard input for the derivation of
geophysical parameters, the Lw,λ converted into Rrs,λ by dividing with spectral solar irradiances
(Es,λ) available for MODIS and SeaWiFS bands. Furthermore, empirical ROAs were developed by
using coincident Rrs,λ and in situ chl-aHPLC (Equations 9–14, Figure 3), for the retrieval of chl-a
concentrations from Aqua-MODIS and SeaWiFS. Earlier studies found that ocean colour
satellite retrievals were always underestimated irrespective of comparison with chl-aflu or
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Figure 3. Relationship between in situ chlorophyll-a concentration and the band ratios of in situ
remote-sensing reflectance (Rrs,λ) symbolized as dashed lines with ‘+’ representing regionally
optimized algorithms (ROAs). The algorithms were applied to (a) Aqua-MODIS, (b) SeaWiFSOC2,
and (c) SeaWiFSOC4 for the estimation of chlorophyll-a concentration. Circle represents the relationship between in situ chlorophyll-a concentration and Rrs,λ satellite products from Aqua-MODIS and
SeaWiFS as processed by NASA’s Ocean Biology Processing Group (OBPG) .
chl-aHPLC (Stuart et al. 2004; Szeto et al. 2011). Hence, the performances of ROAs were
evaluated using statistical and graphical criteria considering the in situ chl-aflu from WOD
and NOMAD, in the absence of an adequate number of chl-aHPLC observations for validation
(Table 1 and Figure 4). In addition, the spatial pattern of the oceanographic features from ROAderived chl-a concentration images was evaluated for its validity (Figure 5). The vertical profiles
of in situ chl-a concentration and temperature data available with WOD were analysed to
explain the variability of surface oceanographic features from ROA-derived chl-a concentration
images (Figure 5).
To explain the uncertainties in satellite retrievals, an analysis was carried out to understand the effect of phytoplankton pigment compositions and packaging on Rrs,λ through
aph,*λ. The phytoplankton groups were classified through HPLC-determined marker diagnostics pigments (DP) after referring to the methodology given in Hirata et al. (2011)
(Table 2), compiled from earlier works of Sieburth, Smetacek, and Lenz (1978), Uitz et al.
(2006), and Brewin et al. (2010). The spatial variability of pigment compositions and
phytoplankton groups is shown in Figure 6(a,b), respectively. Absorption by the phytoplankton component (aph,λ) was obtained by subtracting the detrital absorption coefficient
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Figure 4. Chlorophyll-a concentration retrieved from (a) Aqua-MODIS and (b) SeaWiFS using various
empirical algorithms such as global algorithm (squares), southern ocean algorithm (circles), and
regionally optimized algorithm, with (+ and) indicating significant underestimations for elevated
values (ellipses) owing to the pigment packaging effect.
(ad,λ) from particulate absorption (ap,λ) (data source: NOMAD). The aph,*λ was then calculated
as a ratio of aph,λ to HPLC-determined chl-a concentration. The aph,*λ spectra of the
dominant phytoplankton groups for various chl-a concentrations were analysed and compared with earlier studies by Ciotti, Lewis, and Cullen (2002) and Bricaud et al. (1995)
(Figure 7). In addition, it was analysed to study the variability of aph,*443:aph,*555 and aph,
*489:aph,*555 band ratios to determine their relationship with various pigments normalized
by chl-a concentration (Figure 8). The effect of pigment composition and pigment packaging on satellite-based Rrs,λ was described in Equation 15.
2.3. Additional data sets
To classify the sampling stations belonging to the onshore, offshore, and Kerguelen Plateau
waters, ETOPO1 (Earth Topography One Arc-Minute Global Relief Model, 2009) geo-referenced bathymetric data (21,601 by 10,801 cells) was acquired from National Geophysical Data
Center (NGDC) and subsetted for the study region (Figure 1). The northeast shifting of pixel
location in ETOPO1 was rectified as the method specified in Jena et al. (2012). The data on
mean dynamic topography (MDT) and Southern Ocean fronts were analysed to compare the
physical features with the biological features (chl-a) derived from Aqua-MODIS and SeaWiFS
(Figure 5(a,b)). The MDT of the sea surface was derived from the combined analysis of drifters,
satellite altimetry, wind, and GRACE (Gravity Recovery and Climate Experiment) satellite-based
geoid (Maximenko and Niiler 2005). Locations of various Southern Ocean fronts were
obtained from Australian Antarctic Data Centre (Orsi, Whitworth, and Nowlin 1995).
3. Results and discussion
3.1. Validation of chl-a retrieved using standard GA
In situ chl-a data sets during the austral summer (January–February 2004; January–
February–March 2006; and February–March 2007) are presented in Figure 1. The chl-a
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Figure 5. Application of regionally optimized algorithm (ROA)-derived chlorophyll-a images from (a)
Aqua-MODIS and (b) SeaWiFS for the period of 18–25 February 2006, indicating elevated values all
along the Antarctic coast, the Kerguelen plateau waters, and along the southern boundary of the
Antarctic Circumpolar Current Front (sACCf). These features were evident in the WOD in situ profiles
along (c–d) 60° E, (e–f) 70° E, and (g–h) 80° E transects. The deep-sea chlorophyll-a maxima (DCM)
varied from 50 m to 100 m and correspond well with the temperature minimum layer (TML), which
is known to be the winter remnant of the Antarctic Surface Water (AASW) that extends northwards
from the coastal Antarctic. The mean dynamic topography (MDT) isolines (solid lines) during 18–25
February 2006 were overlaid to interpret the colour features associated with the circulation pattern.
Locations of various major Southern Ocean fronts were represented as dashed lines (Orsi, Whitworth,
and Nowlin 1995) .
concentrations from 76 observations range from about 0.012 to 19 mg m−3 in the study
region. After implementation of the validation strategy, the number of matchups
between in situ and satellite observations reduced significantly, and the chl-a concentrations range between 0.14 and 5.16 mg m−3 with a mean value of 0.455 mg m−3. The
comparative result revealed that in situ measurements in the IOSO waters had significant
in-phase correspondence with chl-a concentrations derived from Aqua-MODISGA
(R2 = 0.889, p = 4.31–05) and SeaWiFSGA (R2 = 0.996, p = 4.62–06) (Table 1 and
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Table 2. A description on phytoplankton size classes (PSCs) and phytoplankton functional types
(PFTs) represented by their pigments (Hirata et al. 2011).
PSCs/PFTs
Microplankton (>20 μm)b
Diatoms
Dinoflagellates
Nanoplankton (2–20 μm)a
Green algae
Prymnesiophytesd
(Haptophytes)
Picoplankton (0.2–2 μm)a
Prokaryotes
Pico-eukaryotese
Prochlorococcus sp.
Diagnostic pigments
Estimation formula
Fucoxanthin (Fuco), Peridinin (Perid)
Fuco
Perid
19ʹ-Hexanoyloxyfucoxanthin (Hex)
Chlorophyll-b (chl-b)
Butanoyloxyfucoxanthin (But)
Alloxanthin (Allo)
chl-b
Hex, But
1.41 (Fuco + Perid)/Σ(DP)b
1.41 (Fuco)/Σ(DP)b
1.41 (Perid)/Σ(DP)b
(Xn*1.27 Hex + 1.01 chl-b
+ 0.35 But + 0.60 Allo)/Σ(DP)c
Zeaxanthin (Zea), Hex, chl-b
Zea
Hex, chl-b
Divinyl chl-a (DVchl-a)
(0.86 Zea + Yp1.27 Hex)/Σ(DP)c
0.86 (Zea)/Σ(DP)b
1.01 (chl-b)/Σ(DP)b
0.74 (DVchl-a)/(chl-a)
a
Sieburth, Smetacek, and Lenz (1978)
b
Σ(DP) = 1.41 Fuco + 1.41 Perid + 1.27 Hex + 0.6 Allo + 0.35 But + 1.01 chl-b + 0.86 Zea = chl-a (Uitz et al. 2006)
c
Xn indicates a proportion of nanoplankton contribution in Hex. Similarly Yp indicates a proportion of picoplankton in
Hex (Brewin et al. 2010)
d
Given that the contributions of Allo to nanoplankton were only a few percentage points in our data set, haptophytes
were approximated to Nano minus Green Algae (see also Figure 2 caption of Hirata et al. 2011)
e
Pico-eukaryotes can be determined from picoplankton minus prokaryotes (see also Figure 2 caption of Hirata et al.
2011).
Figure 6. Distribution of HPLC-determined (a) surface phytoplankton pigment compositions and (b)
different phytoplankton community structures from onshore (station 1) to offshore region (station
10) during 17 February to 6 March 2007, indicating shifting of the phytoplankton community
structure with a significant increasing trend of diatom abundance from offshore to the Kerguelen
plateau waters and coastal Antarctica. Station locations are depicted in Figure 1.
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Figure 7. Variability of specific absorption spectra of phytoplankton (aph,*λ) for two typical samples
in the Indian Ocean sector of Southern Ocean (dashed lines with circles) compared with earlier
studies by Ciotti, Lewis, and Cullen (2002) and Bricaud et al. (1995). The mean spectral shape of aph,
*λ for the size fractionation of phytoplanktons (red, green, and blue colour solid lines) was analysed
to assess the extent to which the phytoplankton cell size drives the spectral variation of aph,*λ. The
spectral characteristics of diatom-dominated phytoplankton populations exhibit lower aph,*λ in the
405–510 nm region (with relative flattening at 443–489 nm) compared to the aph,*λ spectra of
haptophytes, which peak near 443 nm. An inverse relationship was observed between chlorophyll-a
concentration and aph,*λ, meaning that when phytoplankton abundance increases, larger cell sizes
are added incrementally to a background of smaller cells, which leads to pigment packaging
(intracellular shading) and in turn aph,*λ decreases.
Figure 2). However, there was a systematic underestimation in case of chl-a concentrations derived from Aqua-MODISGA and SeaWiFSGA when the in situ measurements
exceeded about 0.3 mg m−3 (Figure 2). The overall magnitude of underestimation for
Aqua-MODISGA and SeaWiFSGA was roughly a factor of 2.3 and 2.9, respectively (Table 1).
These results of underestimation by satellite observations were well supported by the
earlier studies over the Southern Ocean (Moore et al. 1999; Carder et al. 2003; Korb,
Whitehouse, and Ward 2004; Stuart et al. 2004; Szeto et al. 2011). To address this issue,
Johnson et al. (2013) developed a site-specific algorithm for the entire Southern Ocean
with most of the bio-optical matchup observations located approximately along the
150° E meridian. They reported that Aqua-MODISSOA and SeaWiFSSOA (Equations 5–8)
were the improved algorithms for the retrieval of chl-a concentration over the Southern
Ocean, compared with the operational existing GAs (Equations 1–4).
3.2. Validation of chl-a retrieved using SOA
To evaluate the SOA over the study region and validate the retrieved chl-a, Rrs,λ values from
Aqua-MODIS and SeaWiFS were processed using Equations 5–8, which are further validated
with in situ observations. Table 1 lists the statistics of comparative analysis. The R2 values for
Aqua-MODISGA and in situ-measured chl-a values (R2 = 0.889) were much better than those
measured from Aqua-MODISSOA and in situ (R2 = 0.42), indicating a better performance of
Aqua-MODISGA in the study region. The overall rate of underestimation of chl-a concentration
from Aqua-MODISGA (factor of 2.3) remains unaffected even after employing Aqua-MODISSOA
(factor of 2.35). The R2 between SeaWiFSSOA and in situ chl-a measurements (R2 = 0.98) had no
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Figure 8. (a) Evident indication of a decrease in the blue:green absorption ratios corresponding to
SeaWiFS and Aqua-MODIS bands as associated with an increase in Fucoxanthin:chlorophyll-a pigment.
In contrast, other pigments that showed in-phase correspondence with the absorption ratios were (b)
19ʹ-Hexanoyloxyfucoxanthin, (c) 19ʹ-Butanoyloxyfucoxanthin, (d) Peridin, and (e) Zeaxanthin.
improvement compared to SeaWiFSGA and in situ chl-a (R2 = 0.99). However, the rate of
underestimation of chl-a concentration from SeaWiFSSOA was lesser (factor of 2.01) than that
of SeaWiFSGA (factor of 2.9). Although SOA performed better than GA in terms of improvement
in slope (Johnson et al. 2013), the SOA-derived chl-a concentration (both for Aqua-MODIS and
for SeaWiFS) indicated significant underestimation at high chl-a concentrations and overestimation at low chl-a concentration in the IOSO region (Figures 2 and 4). Considering the
uncertainty in GA and SOA, it was further attempted to narrow down the search for a suitable
bio-optical algorithm for retrieval of chl-a concentration in the IOSO region.
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3.3. Development of ROA and its application
A variety of algorithms developed earlier for the retrieval of chl-a were based on
theoretical, empirical, analytical, semi-analytical, or combinations of these
approaches, which need to be fine-tuned or improved. The main problem associated
with algorithm development or model formulation for parameter retrieval is the
complexity of physical processes involved and the uncertainties associated with
them (Jena, Swain, and Tyagi 2010). The development of a regional bio-optical
algorithm requires a large number of in situ measurements, which is practically
difficult to collect in the Southern Ocean considering the adverse weather condition
and navigational hazards. In the absence of large bio-optical data sets in the study
region, it was experimented with limited observations (n = 10) from NOMAD to
construct an ROA. The data set encompasses surface chl-a concentration between
0.149 and 1.547 mg m−3 in the sampling stations from 1 to 10 (Figure 1). Typical chla concentrations between 0.05 and 1.50 mg m−3 were also reported earlier in the
Southern Ocean (Arrigo et al. 1998; El- Sayed 2005). Interaction of irradiance, deep
mixing, iron, and grazing is known to limit phytoplankton growth all over the
Southern Ocean (Boyd 2002; Daly et al. 2001; Moline and Prézelin 1996; Venables
and Moore 2010). Elevated chl-a concentrations between 1 and 30 mg m−3 were also
reported in regions of continental shelf and ice edge areas (El- Sayed 2005; HolmHansen et al. 1989; Moore and Abbott 2000).
The ocean colour algorithms based upon blue to green ratios of Rrs,λ have been used
widely to derive chl-a concentration from ocean colour sensors (Carder et al. 2003;
Gordon et al. 1988; O’reilly et al. 1998; O’reilly 2000). In this article, the NOMAD-based Rrs,
λ versus in situ chl-aHPLC coincident data sets during 17 February 2007–6 March 2007
were utilized to develop empirical ROAs for the retrieval of chl-a concentration from
Aqua-MODIS and SeaWiFS. The ROAs are presented below in Equations 9–14, and as a
dashed line in Figure 3.
3.3.1. Aqua-MODISROA, OC3
ChlorophyllðchlaÞ ¼ 100:0803:705R þ 18:41R 42:41R
Rrs;443 > Rrs;490
R ¼ log10
:
Rrs;547
2
3.3.2. SeaWiFS
ROA,
3
þ 30:02R4
:
(9)
(10)
OC2
ChlorophyllðchlaÞ ¼ 100:1062:907R þ 8:885R
Rrs;490
R ¼ log10
:
Rrs;555
2
12:27R3
:
(11)
(12)
3.3.3. SeaWiFSROA, OC4
ChlorophyllðchlaÞ ¼ 100:2285:416R þ 25:1R 53:30R
Rrs;443 > Rrs;490 > Rrs;510
R ¼ log10
:
Rrs;555
2
3
þ 36:34R4
:
(13)
(14)
14
B. JENA
The Aqua-MODISROA OC3 output yielded a strong R2 of 0.84, with in situ chl-a. Yet,
the chl-a concentrations were underestimated by Aqua-MODISROA compared with in
situ measurements (Figure 4). The SeaWiFSROA OC2 and OC4 outputs yielded a strong
and one of the highest R2 with in situ chl-a (R2 = 0.99) (Table 1). The rate of
underestimation and standard error were minimal in case of OC2 compared to that
in OC4 (Table 1). The performance of OC2 was better than OC4 for SeaWiFS-based
chl-a retrieval, because it employs the 490:555 nm band ratio and the atmospheric
correction is much better in the 490 nm band compared to that in the 443 nm band
(Carder et al. 2003).
To evaluate the spatial pattern of oceanographic features from the ROA-derived chl-a
images, the Rrs,λ from Aqua-MODIS and SeaWiFS was processed using Equations 9–14,
for the period of 18–25 February 2006, and further compared with the WOD in situ
profiles (Figure 5). Aqua-MODISROA and SeaWiFSROA chl-a images indicated elevated
values all along the Antarctic coast, Kerguelen Plateau waters, and along the southern
boundary of Antarctic Circumpolar Current Front, sACCf (Figure 5(a,b)). The elevated
values of chl-a concentration from Aqua-MODISROA and SeaWiFSROA agreed well with
the WOD in situ data collected along the 60° E, 70° E, and 80° E meridional transects
(Figure 5(c–h)). The depth of deep-sea chl-a maxima (DCM) varied from 50 m to 100 m
and corresponds well with the temperature minimum layer (TML), which is known to be
the winter remnant of the Antarctic Surface Water (AASW) that extends northwards from
the coastal Antarctic (Figure 5(c–h)) (Holm-Hansen, Kahru, and Hewes 2005). The surface
oceanographic features such as fronts, eddies, and meanders were well captured by
Aqua-MODISROA- and SeaWiFSROA-retrieved chl-a images. The features agreed well with
the regional circulation pattern as inferred from the MDT data sets. Both Aqua-MODISROA
and SeaWiFSROA images clearly indicated intense phytoplankton blooms above the
Kerguelen plateau waters and its downstream section along the polar front (PF). The
sedimentary source of iron from the island and the surrounding shallow plateau can
fertilize the mixed layer, and possibly cause the bloom (Figure 5(a,b)). These blooms are
important both as the base of the regional food web and for the carbon that they
capture and export to the deep oceans (Jena 2016; Mishra, Naik, and Anilkumar 2015).
Albeit the surface water above the Kerguelen plateau is rich in chl-a, an exception of the
oligotrophic condition dominated in between the north Kerguelen plateau (NKP) and
the south Kerguelen plateau (SKP) due to apparent intrusion of iron-limited high
nutrient-low chlorophyll (HNLC)-regime waters from the Enderby basin through the
Fawn trough current (Figure 5). All these distinct features were also observed by the
chl-a concentration images retrieved using GA and SOA (figures not shown), and
supported the validity of ROA in terms of the spatial patterns of features.
Although the oceanographic features were well captured from Aqua-MODIS and
SeaWiFS chl-a images using all empirical algorithms, an accurate quantitative estimate
of phytoplankton biomass in the Southern Ocean remained inconclusive and challenging. For example, evidence from our present analysis in the IOSO waters in addition to
earlier studies suggested that all empirical algorithms (GA, SOA, and ROA) used for the
retrieval of chl-a concentration from Aqua-MODIS and SeaWiFS were found to underestimate by a factor of 2 at least and can be as large as 2.9 (Table 1). In particular, there
was a significant underestimation by Aqua-MODIS and SeaWiFS chl-a concentrations
when the in situ measurements exceeded about 0.3 mg m−3 (Figure 2) even after
INTERNATIONAL JOURNAL OF REMOTE SENSING
15
employing any empirical algorithms. To explain these observed uncertainties, an analysis
was carried out in the following section to understand the effect of phytoplankton
pigment compositions on the marine Rrs,λ from the analysis of IOPs. Besides, the possible
sources of uncertainties were also investigated when the standard atmospheric correction scheme was applied to the IOSO region.
3.4. Diagnosis on the potential causes of observed uncertainties
The Antarctic waters are known to be optically unique and the standard empirical ocean
colour algorithms applied to these waters may not address the regional bio-optical
characteristics. Being Case-1 water, the algorithms that relate blue to green Rrs,λ ratio
(OC2, OC3, OC4, etc.) should not fail to measure the chl-a concentration in the Antarctic
waters. The reason for this failure is hypothesized first to the presence of optically complex
waters due to the influx of glacial and sea-ice melted water, which can increase particle
concentrations in the nearshore region and result in a conducive environment for a
distinct phytoplankton assemblage with the dominance of large cell size planktons (e.g.
large diatom cells). For example, few important studies by Smith and Nelson (1985) and
Arrigo et al. (2012) provided evidence on increased phytoplankton growth and accumulation within the marginal ice zone (MIZ) due to melt water production and stratification.
Second, the mesoscale physical process induced by baroclinic instabilities is hypothesized
to alter the bio-optical characteristics of the Kerguelen Plateau waters and its downstream
region after Antarctic Circumpolar Current (ACC) encounters the Kerguelen Plateau topography. The Kerguelen Ocean and Plateau compared Study (KEOPS) by Maraldi et al.
(2009) showed an increase in chl-a concentration above the plateau and attributed it to
the availability of iron coming up from the seafloor after the interaction of strong currents
with the Kerguelen Plateau. The physical processes are known to enhance the particle
concentrations and trigger phytoplankton blooms (Figure 5(a,b)) in the euphotic zone
through nutrient injection, often accompanied by large-sized cells (e.g. large diatom cells).
The increase in phytoplankton pigment concentration and size can have a significant
impact on Rrs,λ through the variability of IOPs, and in turn the standard empirical algorithms may fail to accurately measure the chl-a concentration. These hypotheses were
experimented further by determining the effect of phytoplankton pigment compositions
on aλ and Rrs,λ.
Since Rrs,λ depends both on the variability of IOPs (e.g. phytoplankton absorption and
scattering coefficients) and the geometric distribution of the light field, IOPs can be used
to model Rrs,λ. A brief summary of the Rrs,λ model used by Lee et al. (1994) and Carder
et al. (2003), is described as follows:
Rrs;λ ¼
fλ
Qλ
2 t
bb;λ
;
n2
aλ þ bb;λ
(15)
where f(λ) is an empirical factor. Q(λ) is calculated as the upwelling radiance (Lu) to the
corresponding upwelling irradiance Eu (Q = Eu/Lu), which describes the non-isotropic
character of the radiance field. Both f and Q factors are variable and strongly dependent
on the solar zenith angle, geometric distribution of the light field, and IOPs. t is the
16
B. JENA
transmittance of the air–sea interface, and n is the refractive index of sea water. The
value of t2/n2 is equivalent to 0.54, but can vary with sea-state condition and is relatively
independent of wavelength (Carder et al. 2003). aλ and bb,λ are absorption and backscattering coefficients, respectively. The spectral behaviour of bb,λ is not as dynamic as
that of aλ (Carder et al. 2003). Considering the variability of parameters described in the
above-mentioned equation, aλ was hypothesized to affect Rrs,λ dominantly. To investigate it, the impact of phytoplankton pigment compositions on aλ was studied. The
impact of aλ on Rrs,λ can explain the observed underestimation of satellite-retrieved chl-a
concentration in the study region.
3.4.1. Phytoplankton pigment composition and spectral absorption properties
Figure 6(a) shows the distribution of surface phytoplankton pigment composition during
austral summer (17 February 2007 to 6 March 2007), indicating shifting of the phytoplankton community structure from the coastal Antarctic (station 1) to the offshore
region (station 10). The most dominant pigments were fucoxanthin and 19ʹ-hexanoyloxyfucoxanthin (19ʹ-HF), in addition to the presence of a smaller proportion of diadinoxanthin, 19ʹ-butanoyloxyfucoxanthin (19ʹ-BF), peridinin, zeaxanthin, and other pigments.
The chl-aHPLC concentration during this period ranges from 0.149 to 1.547 mg m−3, with
the highest values observed near the Antarctic coast. The pigment compositions in the
coastal Antarctic (station 1) and Kerguelen Plateau waters (stations 2–4) were characterized by the highest proportion of fucoxanthin pigments, indicating the presence of
diatoms. In these waters, the phytoplankton populations were dominated by 53–78% of
diatoms as determined using DP analysis (Table 2 and Figure 6(b)). Furthermore, the
offshore stations off Kerguelen Plateau (stations 5–10) were characterized by the highest
proportion of 19ʹ-HF pigments (indicative of haptophytes), dominated by 48–69% of
phytoplankton populations (Figure 6(b)). Analysis indicated that there was a shifting of
phytoplankton community structure from offshore to coastal Antarctic, with a significant
increasing trend of diatoms population and a decreasing trend of haptophytes population (Figure 6(b)). The spectral absorption characteristics of these dominant phytoplankton groups were analysed further to determine its effect on Rrs,λ.
The spectral characteristics of aph,*λ are known to vary as a function of pigment
composition and pigment packaging, attributed to intracellular shading (Morel and
Bricaud 1981). The decreased absorption of pigments in cells compared with the
absorption potential of the same amount of pigments is being described as the pigment-packaging effect (Duysens 1956; Kirk 1976, 1994; Geider and Osborne 1987; Morel
and Bricaud 1981; Sathyendranath, Lazzara, and Prieur 1987). Pigment packaging occurs
when the cell size increases (e.g. large diatoms) or the internal concentration of pigments increases (Kirk 1976; Morel and Bricaud 1981; Sosik and Mitchell 1994), which
yields flattening of absorption peaks in the blue spectrum (Dierssen and Smith 2000;
Stuart et al. 2004). To examine the effect of pigment composition and packaging, the
aph,*λ spectra of the dominant phytoplankton groups for various chl-a concentrations
were analysed and compared with earlier studies by Ciotti, Lewis, and Cullen (2002) and
Bricaud et al. (1995) (Figure 7). Analysis indicated that the spectral characteristics of
diatom-dominated phytoplankton populations (diatoms = 74.1%; haptophytes = 21.7%)
exhibit lower values of aph,*λ in the 405–510 nm region (relatively flattening at
443–489 nm) compared to the aph,*λ spectra of haptophytes-dominated phytoplankton
INTERNATIONAL JOURNAL OF REMOTE SENSING
17
populations (haptophytes = 69%, diatoms = 17.6%) that peak near 443 nm. Increase in
chl-a concentration was associated with a decrease in aph,*λ, which indicated that when
the phytoplankton abundance increases, larger cell sizes are added incrementally to a
background of smaller cells (Ciotti, Lewis, and Cullen 2002), leading to pigment packaging (intracellular shading) and in turn decreasing aph,*λ (Figure 7). To assess the extent
to which the phytoplankton cell size drives the spectral variation of aph,*λ, the mean
spectral shape of aph,*λ for the size fractionation of phytoplankton was studied by Ciotti,
Lewis, and Cullen (2002). The study revealed the cell size (pico, nano, and micro
phytoplanktons) could explain more than 80% variability in the spectral shape of aph,
*λ. The effect of pigment packaging is prominent for microphytoplanktons (with the
flattening of the blue region aph,*λ spectra) compared with pico- and nanophytoplankton (Figure 7). The relative flattening of the aph,*λ spectra in the 443–489 nm region for
the diatom-dominated phytoplankton population can be attributed to pigment packaging (Figure 7), to the fact that the sizes of diatoms in the Antarctic waters are much
larger (Brody et al. 1992). The observed flattening of the aph,*λ spectra (blue band) for
the diatom-dominant phytoplankton population and the elevated chl-a concentration
results in greater Rrs,λ since absorption is inversely related to Rrs,λ (Equation 15).
Therefore, an increase in Rrs,λ leads to an underestimation of satellite-retrieved chl-a
concentration for elevated in situ chl-a concentration.
Furthermore, the variability of aph,*443:aph,*555 and aph,*489:aph,*555 band ratios (corresponding to SeaWiFS and Aqua-MODIS bands) was analysed to determine their relationship with pigment composition normalized by chl-a concentration (Figure 8). In-phase
association was observed between blue:green band ratios and pigments such as 19ʹ-HF,
19ʹ-BF, peridinin, and zeaxanthin (Figure 8(b–e)). The proportion of 19ʹ-HF to chl-a could
explain about 76% of the variance (r = 0.87) in aph,*443:aph,*555, and for aph,*489:aph,*555, it
was about 73% (r = 0.85). In contrast, out-of-phase association was observed between
blue:green band ratios and fucoxanthin pigments, indicating a decrease in the absorption ratio as associated with increase in fucoxanthin pigments (Figure 8(a)). Analysis
indicated that 72.9% of the variance (r = −0.85) in aph,*443:aph,*555 could be explained by
the proportion of fucoxanthin:chl-a, and for bands aph,*489:aph,*555 the variance
explained was about 68% (r = −0.82). The out-of-phase correspondence suggests that
the increasing trend of fucoxanthin pigments towards the Antarctic coast was associated
with the decreasing trend of blue:green absorption ratios. The decrease in blue:green
absorption ratio leads to greater Rrs,λ, which in turn underestimates the chl-a concentration from various empirical algorithms used for Aqua-MODIS and SeaWiFS.
3.5. Possibility of improper atmospheric correction algorithm
In addition to the evidence of pigment composition and packaging effect on the
empirical algorithms, the processes involved in atmospheric correction can be a major
source of uncertainty for the derivation of Lw,λ and in turn effect the retrieval of chl-a
concentration from satellite observations. High concentrations of secondary organic
aerosols like isoprene found over the coastal Antarctica, especially in the Prydz Bay
and the fluxes, were reported up to 1200 μg m–2 day–1 (Hu et al. 2013). Investigations
are required to study the potentially complicated mixtures of different aerosol types and
18
B. JENA
their spatio-temporal variations over the IOSO region, which can produce significant
error in the atmospheric correction procedure.
The classical atmospheric correction procedure assumes the Lw,λ is zero in the nearinfrared part of the electromagnetic spectrum. This assumption is known to be invalid
over a high concentration of water-scattering constituents and shallow bathymetry (causing bottom reflectance). The recent atmospheric correction algorithms therefore account
for the non-zero water-leaving signal in the Near Infrared (NIR) by applying a coupled
approach for the separation of atmospheric and aquatic contributions to the signal
(Sathyendranath 2000). In case of Antarctic waters, the presence of sea ice and icebergs
in the field-of-view of the sensor would also contaminate the surrounding desired Lw,λ.
Gregg and Casey (2004) provided evidence on the contamination of ocean colour observations by sea ice in their effort to validate the SeaWiFS chl-a concentration data. Similarly,
Belanger, Ehn, and Babin (2007) and Wang and Shi (2009) examined the impact of
backscattered photons from snow and sea ice on the desired Rrs,λ in the Arctic region,
and indicated the impact can be several kilometres from the ice edge due to adjacency
effect.
The large solar zenith angle in the study area can be another source of uncertainty
while performing atmospheric correction. Both f and Q in Equation 15 are known to be
dependent on the solar zenith angle. The Southern Ocean regions have large solar zenith
angles and the proportion of diffuse sky light is high, especially in the blue part of the
spectrum (Dierssen and Smith 1997). Morel and Gentili (1993) modelled the changing
trends in f:Q with latitudes and reported that the f:Q ratios are 5% greater in the polar
regions than in the temperate regions. Since the f:Q ratio is directly proportional to Rrs,λ, an
increase in the f:Q ratio would result in a higher Rrs,λ value (Equation 15) and in turn can
lead to lower chl-a concentration. The effect of f:Q ratio can have a propounded effect on
Rrs,λ in the study region considering the high latitudes, which range from 40° S to 41° S
(Coastal Antarctic). Once all these issues are accounted for, the Lw,λ can be assessed
precisely to further retrieve the chl-a concentration from the satellite observations.
4. Conclusion
An accurate quantitative estimate of chl-a concentration from the satellite observations in the southern IOSO region remained inconclusive and challenging. For
instance, it is confirmed that the chl-a concentration retrieved from Aqua-MODIS
and SeaWiFS using empirical algorithms (GA, SOA, and ROA) indicated significant
underestimations for elevated in situ values owing to the dominance of fucoxanthin
pigments (a biomarker of diatom) and pigment packaging effect. Hence, the utilization of these empirical algorithms could lead to large erroneous result while studying
the long-term trends of chl-a concentration and phytoplankton productivity in the
context of climate change.
An optimized bio-optical algorithm is required based on the absorption coefficient to
account for the differences in the bio-optical characteristics of a diatom group of
phytoplanktons. The utility of the algorithm must be improved and validated with the
collection of more in situ bio-optical observations. The algorithm is essential for the
monitoring of diatom-dominated phytoplankton blooms and for the precise estimation
of the oceanic role in the biogeochemical cycle of carbon, specifically the importance of
INTERNATIONAL JOURNAL OF REMOTE SENSING
19
the Southern Ocean as a sink for atmospheric CO2. The need for continuous long-term
ocean colour observations indeed exists for the regular monitoring of Antarctic marine
ecosystems.
Acknowledgements
Continuous support from Dr M. Ravichandran, Director, National Centre for Antarctic and Ocean
Research (NCAOR), Dr Thamban Meloth (Group Director, Polar Sciences, NCAOR), and Dr Alvarinho
Luis (NCAOR), is gratefully acknowledged. MODIS and SeaWiFS data sets were provided by National
Aeronautics and Space Administration (NASA) Goddard Space Flight Center (NASA’s GSFC). The
authors would like to acknowledge all scientists and crew members of R/V Revelle cruise (I8SI9N)
who collected and analysed in situ bio-optical profiles and pigments that have gone into the NASA
bio-Optical Marine Algorithm Data set (NOMAD). In situ chl-a and temperature profiles were obtained
from National Oceanographic Data Center (NODC). Earth Topography One Arc-Minute Global Relief
Model, 2009 (ETOPO1) bathymetric data was acquired from National Geophysical Data Center (NGDC).
This is NCAOR contribution no 09/2017.
Disclosure statement
No potential conflict of interest was reported by the author.
Funding
Continuous support from Dr M. Ravichandran, Director, National Centre for Antarctic and Ocean
Research (NCAOR), is gratefully acknowledged.
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