Advances in Remote Sensing, 2012, 1, 35-51
http://dx.doi.org/10.4236/ars.2012.12004 Published Online September 2012 (http://www.SciRP.org/journal/ars)
35
An Algorithm for Classification of Algal Blooms Using
MODIS-Aqua Data in Oceanic Waters around India
Arthi Simon, Palanisamy Shanmugam*
Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai, India
Received July 15, 2012; revised August 22, 2012; accepted September 11, 2012
ABSTRACT
Increasing incidences and severity of algal blooms are of major concern in coastal waters around India. In this work an
automatic algorithm has been developed and applied to a series of MODIS-Aqua ocean color data to classify and moni-
tor four major algal blooms in these waters (i.e., Trichodesmium erythareum, Noctiluca scintillans/miliaris (green/brown), and
Cochlodinium polykrikoides (red)). The algorithm is based on unique spectral signatures of these blooms previously re-
ported by various field sampling programs. An examination of the algorithm results revealed that classified blooms
agree very well with in-situ data in most oceanic waters around India. Accuracy assessment based on overall, user’s and
producer’s accuracy and Kappa accuracy further revealed that the producer’s/user’s accuracy of the four algal blooms
were 100%/100%, 79.16%/79.16%, 100%/80%, 100%/86.95%, respectively. The Kappa coefficient was 1.01. These
results suggest that the new algorithm has the potential to classify and monitor these major algal blooms and such in-
formation is highly desired by fishermen, fish farmers and public health officials in this region. It should be noted that
coefficients with the new algorithm may be fine-tuned based on more in-situ data sets and the optical properties of these
algal blooms in oceanic waters around India.
Keywords: Algal Blooms; Arabian Sea; MODIS; Automated Algorithm; India.
1. Introduction
Green, red and brown algal blooms have been reported to
increase spatially and temporally in many coastal and
offshore waters around the world. Increased incidences
and severity of such algal blooms could be due to nutria-
ent enrichment of these waters supplied from anthropo-
genic or natural sources, hydrographic changes, or cli-
mate change impacts [1-9]. The appearance, persistence
and epidemic of some of these blooms have also been
reported to cause fish mortality, shellfish poisoning,
physiological impairment, and numerous ecological and
health impacts [1,5] and references therein.
Over the past decades, an increase in the frequency of
algal blooms has also been reported in coastal and off-
shore waters around India [10-17]. Major algal blooms
dominating in this region are Trichodesmium erythareum,
Trichodesmium thiebautii, Noctiluca scintillans, Nocti-
luca milaris, and Cocholodinium ploykrikoides [11,12,14,
15]. Trichodesmium is a typical genus of pelagic blue-
green algae, which has two species T. thiebautii and T.
erythareum—both occurring mostly in tropical and sub-
tropical seas. These species are known as nitrogen fertil-
izer because of their N2 fixing action. They are distin-
guishable by their occurrence on the surface waters, and
are a common feature from February to May when large
areas of the sea (particularly Arabian Sea and coastal
waters around India) are covered with clumps of saw-
dust colored algae features [17].
Until the late 1990s, N. miliaris, a large dinoflagellate,
was a minor component of phytoplankton populations in
the Arabian Sea, appearing sporadically in bloom form in
coastal regions during the summer southwest monsoon
(June-September). Recently, N. miliaris blooms have
begun to appear with increased frequency and intensity
following the winter northeast monsoon (November-Fe-
bruary) [14]. Several cruises conducted by the National
Institute of Oceanography (India) have also documented
the appearance of extensive blooms of N. miliaris in the
late winter to early spring for several consecutive years.
N. scintillans is also published as N. miliaris which turns
the water green or red depending on its pigment varia-
tions and associated constituents. Blooms of N. scintil-
lans have been observed in coastal waters of Oman and
India (northeastern Arabian Sea, Orissa coast, Kochi
coast, and Gulf of Mannar) almost every year with varia-
tions in their abundances [10,12,15]). On the other hand,
C. polykrikoides is a common ichthyotoxic “red water”
bloom species associated with extensive fish kills and
great economic loss in Japanese and Korean waters [5,7].
*Corresponding author.
C
opyright © 2012 SciRes. ARS

A. SIMON, P. SHANMUGAM 36
This species has become a persistent HAB (harmful algal
bloom) problem in the Arabian Gulf and the Gulf of
Oman and its recurrence may be related to increased nu-
trient enrichment of coastal waters from domestic and
industrial inputs, natural meteorological and oceano-
graphic forcings, and the recent introduction of this spe-
cies through ballast water discharge [18].
Phytoplankton serves as the base of the aquatic food
web, providing an essential ecological function for all
aquatic life. However, there are algal blooms that have
negative economic and health impacts. Therefore, it is
very essential to identify the type of algal bloom that
occurs in this region. Traditionally, algal blooms are
recognized and monitored using ship-borne water sam-
pling techniques at discrete locations, but such tech-
niques cannot provide sufficient spatial and temporal
coverage to monitor algal blooms. Satellite remote sens-
ing has become a complementary tool for monitoring
algal blooms with its synoptic coverage, frequent revisit,
high observing efficiency and relatively low cost.
Many algorithms have been developed to estimate
chlorophyll (Chl-a) concentrations (used as an index of
phytoplankton) using satellite ocean color data (e.g., the
SeaWiFS Ocean Chlorophyll four-band algorithm OC4v4;
MODIS-Aqua Ocean Chlorophyll three-band algorithm
OC3) [19,20]. While this approach has been valid in oce-
anic waters where a change in the concentration of Chl-a
mainly causes a shift in the blue to green ratios of up-
welling light fields [21], the ratios used in these algo-
rithms can vary in response to factors besides Chl-a con-
centration and therefore introduce large errors in pigment
retrievals from satellite data in coastal waters with high
colored dissolved organic matter (CDOM) and suspended
sediment (SS) contents [17,22,23]. A number of other
op- tical methods have been developed and used with
satelli- te data to provide a valuable tool for detection
and mapp- ing of algal blooms ([24-32]). More recently,
an improved algorithm (ABI) has been developed and
thor- oughly validated by [9]. This algorithm appears to
de- termine Chl-a concentrations helping to differentiate
blooms from other constituents in complex waters. How-
ever, such a pigment algorithm is not intended for clas-
sifying the different types of algal blooms.
Our objective is to develop an automated algorithm
that can be used for operational classification and moni-
toring of algal blooms in coastal and offshore waters around
India. A sensitivity analysis is performed to evaluate the
new algorithm using satellite data and field observations.
Finally, the accuracy of classification algorithm is as-
sessed and the results are discussed.
2. Data and Methods
2.1. Satellite Data
The data used in this study were MODIS-Aqua Level 1A
(L1A) data of the Arabian Sea, Indian Ocean and Bay of
Bengal of different periods (7 & 8 Oct. 2008, 5 April
2005, 24 Jan. 2006, 9 Feb. 2008, 23 Nov. 2008, 3 June
2009, 17 April 2009) obtained from the NASA Goddard
Space Flight Centre (http://oceancolor.gsfc.nasa.gov/).
These data were processed to obtain remote sensing re-
flectance Rrs (for the bands 412, 443, 488, 531, 547, 667,
678, 748, and 869 nm) using SeaDAS integrated with the
CCS (Coastal Correction Scheme) scheme, [33]. These
data were further processed using CAAS algorithm and
the atmospheric correction problems were eliminated.
Chlorophyll concentration was estimated using ABI al-
gorithm [9]. The remote sensing reflectance data were
further processed using a new algorithm to classify dif-
ferent types of algal blooms in oceanic waters around
India.
2.2. Field Data
Many field sampling programs have previously recorded
and documented the different types of algal blooms in
coastal waters around India (Table 1). Because all the
photosynthetic phytoplankton contain Chl-a, the meas-
urement of Chl-a is a routine work for monitoring phyto-
plankton blooms and its observations during different
bloom periods is presented in Table 1. It should be recog-
nized that Chl-a is an imprecise indicator of phytoplank-
ton biomass so that even accurate determinations of Chl-a
bear uncertain relationships to the abundance and species
composition of phytoplankton [10]. Thus, consideration
must be given to the various physical, chemical and bio-
logical properties associated with algal blooms. During 7
and 8 Oct. 2008, an intense bloom of N. scintillans was
recorded in coastal waters of the Gulf of Mannar where it
turned the waters dark green because of high cell con-
centrations (around 13.5 × 105 cells·L–1). The presence of
N. scintillans was revealed by the microscopic examina-
tion. The size ranged from 400 to 1200 microns [15] and
associated water temperature, salinity, dissolved oxygen
and nutrients measured were characteristically different
from surrounding waters (water temperature 29.5˚C, sa-
linity 34.2 psu, dissolved oxygen 4.86 ml·L–1, phosphate
8.28 μg·L–1 and ammonia 85 μg·L–1) [15]. During 24 Jan
2006 and other occasions in winter, blooms of N. millaris
were sampled and their associated physical, chemical and
biological conditions (pH, nutrient, dissolved oxygen,
Chl and cell concentrations) were recoded in the northern
Arabian Sea [14,17]). In-situ studies [12] reported an
intense bloom of C. polykrikoides on 23 Nov. 2008,
which caused the fish kill in the Gulf of Oman and sig-
nificantly diminished the dissolved oxygen in bloomed
waters. On several occasions in 2008 and 2009, blooms
of T. erythareum were observed with abnormal salinity
and Chl-a levels along the southeast coast of India (Tamil

A. SIMON, P. SHANMUGAM 37
Table 1. Different types of algal blooms recorded and reported by various studies in coastal and offshore waters around In-
dia.
SNo Name of
Bloom Date Location Lat/Long/Chl-a Reference
1 T. erythareum 6th to
20th May 2005
From Mangalore to
Quilon on the southwest
coast of India
12°59'N and 74º31'E Anoop et al. (2006)
2 N. scintillans 2 to 12 Oct 2008 Gulf of Mannar
09°16'47.6"N/79º11'17.1"E to
09º14'28.5"N/78º54'21.6"E/
0.116 mg·m−3
Gopakumar et al.
(2009)
3 Noctiluca
Blooms July to December 2008 South east coast of IndiaMandapam & Keelakari coast Anantharaman et al. (2010)
4 N. scintillans 5 April 2005 Orissa coast Chl-a high Mohanty et al.
(2007)
5 T. erythareum 16th March 2007.
Kalpakkam waters on
the southeast coast of
India
12°33'N/80°11'E/Chl-a
increased 20 times than the
pre-bloom values.
Satpathy et al. (2007)
6 N. miliaris 3rd-19th Jan 2003, 27th Feb 5th Mar 2003)/24 Jan
2006
Arabian sea Chl-a 0.63 mg·m–3 Gomes et al. (2008)
7 N. miliaris 17 Sept 2004. Kerala coast High Chl-a Joseph et al. (2008)
8 C. polykrikoides 13 Jan 2009 Gulf of Oman
http://www.riob.fr/I
MG/pdf/Muthanna_
Alomar.pdf
9 C. polykrikoides 23 Nov 2008 Gulf of Oman Gheilani (2009)
10 T. erythareum 19 Feb 2008
Kalpakkam waters on
the southeast coast of
India
Chl-a 42.15 mg·m–3 Mohanty et al.
(2010)
11 T.erythareum 29 May to 12 June 2009Kollam and Kochi coast(08°59.492'N, 75°59.334'E)
(09°56.183'N, 75°54.948'E)
Padmakumar et al.
(2010)
Nadu coast), southwest coast of India (from Mangalore
to Quilion and Kollam to Kochi), west coast of India
(Goa and Gujarat) [11-13,17,34]. Similar observations
made by others reported these blooms to consistently
occur in the same locations and spread to the offshore
waters (Table 1).

A. SIMON, P. SHANMUGAM 38
3. Background of the New Algorithm
Remote sensing reflectance spectra were obtained from
MODIS-Aqua data (7 & 8 Oct. 2008, 5 April 2005, 24
Jan. 2006, 23 Nov. 2008, 3 June 2009, 17 April 2009 and
9 Feb 2008) for four algal blooms (reported/known) in
various coastal waters around India. These blooms are
previously well-documented by various field sampling
programs, namely T. erythareum, N. scintillans, N. mil-
laris, and C. polykrikoides. It was found that the remote
sensing reflectance spectra and their derivatives of each
algal bloom have the unique signatures forming the basis
of developing a new classification algorithm (Figure 1).
3.1. Spectral Analysis
The features of the reflectance curves are important as
they give insight into the spectral characteristics of dif-
ferent algal blooms. Figure 2 displays the MODIS-Aqua
reflectance spectra of four types of algal blooms that
were sampled by many field programs in different coastal
areas [11-13,15,17,34,35]. It is observed that the re-
flectance spectra of different algae are distinct because of
their unique absorption and reflectance characteristics.
All four algal blooms have a minimal value in the blue
region which is due to the combined absorption by
phytoplankton pigments, CDOM and non-algal particles
(NAP); a maximal value in the green region due to the
minimal values of total absorption; a minimal value in
the red region (around 667 nm) due to chlorophyll ab-
sorption and a lower value in the near-infrared (> 750 nm)
due to increased absorption by sea-water itself. Chloro-
phyll fluorescence is observed with its peak position at
around 678nm, although it shifts towards the longer
wavelengths owing to the species composition and con-
centration [36-39]. Trichodesmium contains both the
Chl-a and bilin pigments (phycoerythrin and phyco- cya-
nin) which have characteristic absorption spectra. As an
outcome of this, T. erythatreum shows a smooth varia-
tion in the magnitude from Rrs(531) to Rrs(443). The re-
flectance values are high at 531 and 547 nm, which im-
ply high pigment concentrations with high backscattering
coefficients at these wavelengths. The peak at 678 nm is
due to Chl-a and the peaks at 531 and 547 nm are due to
bilin pigments and perhaps other pigments [40]. It was
reported that during the bloom period ammonia was sig-
nificantly high (about 392.80 µ·mol·L−1), especially on
the day of high cell density. This could be due to the di-
azotropic nature of Trichodesmium. Earlier reports
showed a two fold increase of ammonia concentration in
bloomed waters of Trichodesmium at the same locality
[12].
The N. miliaris bloom shows a reflectance peak at
Rrs(748), which may be due to the combined effect of
Chl-a absorption, shifted fluorescence and particulate
backscattering. This peak could be caused by floating of
these algae in the water [28]. The N. scintillans bloom
shows a decrease in reflectance (due to absorption) at
Rrs(443), a steep rise from Rrs(488) to Rrs(531), and a
linear line from Rrs(531) to Rrs(547). A smooth rise to-
wards Rrs(412) may be the result of atmospheric correc-
tion or algal matter itself. By contrast, the reflectance
spectra of C. polykrikoides collected at different times (in
coastal waters of Oman) are consistently the same .The
spectral reflectance curves showed an overall increment
in magnitude which is due to the increase in cell abun-
dance and Chl-a concentration. The steepness in the
range of 443 to 531 nm; a prominent peak at 547 nm; a
distinct trough at 667 nm and a peak at 678 nm were also
observed. Low Rrs(λ) values observed in the blue/green
domain are due to the outcome of the interacting absorp-
tion characteristics of both chlorophyll and carotenoid
pigments. Overall, the noticeable increment in Rrs(λ) that
peaked at 547 nm is due to the outcome that the absorp-
tion by chlorophylls and carotenoids were minimal and
in consequence backscattering by cells remained the
main factor governing Rrs(λ) [36,39]. The position of this
peak around 547 nm is considered a distinctive feature of
chlorophyll containing algae and is regarded as an indi-
cator of their presence in natural waters [36].
Different algal blooms absorb or reflect energy from
different wavelengths in unique way; this provides the
ability to identify the presence or absence of different
algal blooms. An investigation of the distinctive spectral
reflectance characteristics of different algal blooms al-
lowed determining certain band ratios and reflectance
differences and using these in the classification algorithm
to classify four types of algal blooms from satellite data
sets in oceanic waters around India. Since the classifica-
tion algorithm is based on the specific reflectance signa-
tures of different phytoplankton, the contribution of
CDOM or SS to these reflectance features are expected
minimal or their impacts are expected negligible in the
classification outputs (mainly clustering of pixels rather
than determining concentrations like Chl-a).
3.2. Derivative Analysis
Derivatives of second order or higher are relatively less
sensitive to variations in illumination intensity, as well as
spectral variations of sunlight and skylight (Tsai and Phil-
pot, 1998). The derivative analysis, which amplifies spec-
tral inflections and enhances detection of small spectral
variations, can be used to closely examine the spectral
reflectance patterns. This technique provides information
regarding the convexity and concavity of a given reflec-
tance spectrum. In this classification technique, we ex-
amined the second derivative of Rrs(λ) (dλ2Rrs) to indicate
the centre of the secondary peak and trough of Rrs(λ).
dλ2Rrs is derived using.

A. SIMON, P. SHANMUGAM 39
MODIS-Aqua Level 1A Data
Derive Level 2
Products using combined SeaDAS and CCS
scheme: R
rs
412, R
rs
443, R
rs
488, R
rs
531,
R
rs
547, R
rs
667, R
rs
678, R
rs
748, R
rs
869
R
rs
678 >0.0009
Y
es
No
((R
rs
488)/ (R
rs
547-
R
rs
488))
(d
2
(R
rs
488- R
rs
443))
{((R
rs
488)/ (R
rs
547-
R
rs
488)) - (R
rs
443/
R
rs
547)  (d
2
R
rs
488-
d
2
R
rs
443
)}
Obtain Second Derivative
Products: d
2
R
rs
412, d
2
R
rs
443, d
2
R
rs
488,
d
2
R
rs
531, d
2
R
rs
547, d
2
R
rs
667, d
2
R
rs
678,
d
2
R
rs
748, d
2
R
rs
869
R
rs
547 -R
rs
443
< 0.0025 and
>0.006
{((R
rs
488)/ (R
rs
547-
R
rs
488)) 
(d
2
(R
rs
488- R
rs
443))}
> -910
-8
and <
-110
-6
R
rs
547 -R
rs
443
< 0.004 and
>0.0055
C2
((R
rs
547-R
rs
488)/2
- (R
rs
531- R
rs
488)) <0
Turbid Waters
Y
es
No
C1
R
rs
547 >0.014
Y
es
No
{((R
rs
488)/ (R
rs
547-
R
rs
488)) 
(d
2
(R
rs
488- R
rs
443))} > (-
410
-6
and <
-110
-5
) and > (10
5
and <
10
7
)
R
rs
547 -R
rs
443 <
0.0001 and
>0.008
d
2
R
rs
488 > 0
T.erythareum
C1
{((R
rs
488)/ (R
rs
547-
R
rs
488)) - (R
rs
443/ R
rs
547)
 (d
2
R
rs
488- d
2
R
rs
443)} (>
-510
-8
and < -10
-6
) And
(> 10
-6
and < 9*10
-6
)
R
rs
748 > R
rs
678
Y
es
No
N. mi liar i s N. scintillans
(R
rs
547-R
rs
443)
> 0.001 and
< 0.006
d
2
R
rs
667 > d
2
R
rs
531
and
d
2
R
rs
667 >
-0.000004
C. polykrikoides
Y
es
Y
es
C2
Figure 1. Flowchart (referred as “neqn”) describing the algal bloom classification algorithm.

A. SIMON, P. SHANMUGAM 40
Figure 2. Remote sensing reflectance spectra obtained from.
2
2
(( )2
rsrs irs
dR RR
d 

11
2
()( ))
irs i
R 

 (1)
where the finite band resolution Δλ = (λi – λi + 1 ). It was
found that each bloom has distinct derivative spectra as
shown in Figure 3. The derivative spectra have a nega-
tive maximum at 667 nm and a negative minimum value
at 531 nm for the T. erythareum bloom. N. scintillans has
two negative troughs at 531nm and 667nm of the deriva-
tive spectra. In the derivative spectra of N. miliaris a
positive maximum peak is observed at 667 nm and a
minimum peak at 531 nm. Two negative troughs at 531
nm and 667nm are observed in the derivative spectra of
C. polykrikoides. In these derivative spectra the promi-
nent peak at 667 nm reflects the absorption maxima of
Chl-a, whereas the peaks appearing at 488 nm and 531
nm reflect the absorption feature of carotenoids [40].
In this analysis, two sets of reflectance difference/ra-
tios along with a difference in derivative spectra were
used to differentiate the four types of algal blooms, as
follows:


22
488 443
rs
dR



488
547 488
rs rs
rs rs
RdR
RR





for T. erythareum (2)
22
443
rs
dR
 

( 488)443488
547 488547
rsrs rs
rs rsrs
RR
dR
RR R





for N. scintillans, N. miliaris and C. polykrikoides (3).
A scatterplot of the output from Equations (2) and (3)
(Figure 1) versus reflectance difference (547 - 443) is
shown in Figure 4 where all four types of algal blooms
are well clustered and distinguished. The means of each
cluster of the algal blooms were calculated and for each
algal bloom type the distances toward class means were
calculated (Figure 4).
Then, the classification was performed as follows:
• The shortest distance to a class mean was found
• If the shortest distance to a class mean was nearest
to the user-defined threshold, then this class name is as-
signed to the output pixel.
• Else the undefined value was assigned to the nearest
algal bloom type.
4. Accuracy Assessment
The error matrix can be used for a series of descriptive and
analytical statistical techniques. Perhaps the simplest
descriptive statistic is overall accuracy which is com-
puted by dividing the total correct (i.e., the sum of the
major diago being correctly classified and is really a meas-
ure of nal) by the total number of pixels in the error ma-
trix. In addition, accuracies of individual categories can
be computed in a similar manner. However, this case is a
little more complex in that one has a choice of dividing
the number of correct pixels in that category by either the
total number of pixels in the corresponding row or the
corresponding column. Traditionally, the total number of
correct pixels in a category is divided by the total number
of pixels of that category as derived from the reference
data (i.e., the column total). This accuracy measure indi-
cates the probability of a reference pixel omission error.
This accuracy measure is often called “producer’s accu-
racy” because the producer of the classification is interested

A. SIMON, P. SHANMUGAM 41
Figure 3. Second-order derivative spectra for the four algal bloom types.
Figure 4. Scatterplot of neqn (Figure 1) vs Rrs difference at 547 and 443 nm.
in how well a certain area can be classified. If the total
number of correct pixels in a category is divided by the
total number of pixels that were classified in that cate-
gory, then this result is a measure of commission error.
This measure, called “user’s accuracy” or reliability, is
indicative of the probability that a pixel classified on the
image actually represents that category on the ground
[41-43].

A. SIMON, P. SHANMUGAM 42
Thus, an error matrix was generated for the classifica-
tion technique. Each row of the table was reserved for
one of the remote sensing class used by the classification
algorithm. Each column displays the corresponding
ground truth classes in an identical order. This table was
used to properly analyze the validity of each class as well
as the classification as a whole. In this way one can
evaluate in more detail the efficacy of the classification
Overall accuracy = 100%
D
N



 (4)
D: Total number of correct classifications,
N: Total number of classifications.
The overall accuracy of the classification technique
was calculated. But just because 90% classifications were
accurate overall, does not mean that each category was
successfully classified at that rate. So the accuracy of
each class type was also performed. The user’s accu-
racy and the producers accuracy was calculated;
User’s Accuracy = 100%
D
R



 (5)
R: Number in row total.
The User’s Accuracy was computed for each row;
Producer’s Accuracy = 100%
D
C



 (6)
C: Number in column total.
The Producer’s Accuracy was computed for each
column. The user’s accuracy and producer’s accuracy for
the four types of algal blooms (T. erythareum, N. scintil-
lans, N. millaris, and C. polykrikoides) were calculated.
Another measure of map accuracy is the Kappa coeffi-
cient, which is a measure of the proportional (or per-
centage) improvement by the classifier over a purely
random assignment to classes. For an error matrix with r-
rows, and hence the same number of columns, where
A = the sum of r diagonal elements,
B = sum of the r products (row total × column total).
Kappa Coefficient,2
N
AB
K
N
B



(7)
where N is the number of pixels in the error matrix (the
sum of all r individual cell values). If the Kappa coeffi-
cient is 1, then the classification can be said to be 100%
correct.
5. Results
The new algorithm was applied to a series of MODIS-
Aqua data to classify different types of algal blooms in
coastal oceanic waters around India (Arabian Sea, Indian
Ocean and Bay of Bengal). The scattering particles that
cause the water to be turbid can be composed of many
things, including sediments and phytoplankton. In addi-
tion to the spectral criteria for each bloom, thresholds
were thus applied to exclude extremely turbid waters in
coastal regions (e.g., the Gulf of Kutch and Cambay)
where the concentration of suspended sediments is con-
sistently high through out the year and dominates phyto-
plankton populations. Of several examples, an intense
bloom of N. scintillans was observed in coastal waters of
the Gulf of Mannar where it turned the waters dark green
(field data on the right of Figure 5) because of high cell
concentrations (around 13.5 × 105 cells·L−1) [15]. The
classified bloom was consistent with the field data,
ABI_Chl-a and color composite images and appeared to
spread along the coast of Gulf of Mannar during 2 to 13
Oct. 2008; no cloud-free scenes were available except 8
Oct. 2008 (Figure 5).
It was noted that the occurrence and persistence of this
bloom at the narrow strip of coastal area from Kilakarai
to Pamban were due to the favorable environmental con-
ditions, i.e., high air temperature, high sea surface tem-
perature and salinity, low pH, absence of water currents,
high concentration of nutrients, absence of rain and the
favorable wind along the shore. All these factors influ-
enced the sustenance of the bloom around the Islands and
coastal waters, where the cell density ranged from 5.1 ×
105 cells·L–1 to 13.5 × 105 cells·L–1 [15]. They also ob-
served high suspended sediments and Chl-a (115.89
μg·L–1) during the bloom event. The environmental pa-
rameters during the waning phase of this bloom were
notably at increased levels; e.g., surface water tempera-
ture 29.5˚C, salinity 34.2 psu, dissolved oxygen 4.86
ml·L –1, phosphate 8.28 μg·L–1 and ammonia 85 μg·L–1[15].
During 5 April 2005, an intense N. scintillans bloom
was detected by MODIS-Aqua data in coastal waters off
the Rushikulya River mouth (Figure 6). The color com-
posite image showed the linear spread of the bloom and
it can be confirmed by the ABI_Chl-a image (Figure 6,
bottom panels). An in-situ study observed a prominent
discoloration of the surface water in this region caused
by dense and red-colored patches of this bloom of ap-
proximately 16 square kilometres (Lat. 19°22'N and Long.
85°02'E) [12]. A relatively low-moderate temperature
(26.7˚C to 30.6˚C) was reported to trigger the appearance
of N. scintillans bloom in this area.
Figure 7 shows an example of N. milaris bloom with
consistent spatial patterns (in the ABI_Chl-a and color
composite images) in the Gulf of Oman during 24 Jan.
2006. Field observations indicate that the bloom covered
a wide area and turned the water dark green with streaks
of red patches. It persisted for a fortnight in the Gulf and
accounted for almost 69% of the phytoplankton popula-
tion at off station on 24 Jan. 2006.
The MODIS-Aqua image on 23 Nov. 2008 showed an in-
tense bloom of C. polykrikoides covering a wide area from
coastal waters of Oman to the Gulf of Aden (Figure 8).

A. SIMON, P. SHANMUGAM 43
MODIS/Aq
8 Oct. 2008
Gulf of
Kutch
INDIA
OMAN
Gulf of Eden
Red Sea
Gulf of
Mannar
Kerala
coast
Goa
Gulf of
Cambay
Arabian Sea
Indian Ocean
Bay of B
ua
engal
ABI-Chla Rrs547_443_488
Field photo
Cochlodinium
p
ol
y
krikoides
Noctiluca milaris
Clear Water
Tricodesmium
er
y
thareum
Noctiluca scintillans
Figure 5. Algal bloom map of the Gulf of Mannar generated from MODIS-Aqua data (8 Oct. 2008) using the new algorithm
(for detecting N. scintillans bloom). Right image is the field photograph of this bloom which was sampled by Gopakumar et al.
(2009) during this period. Location: Lat. 09˚14'28.5"N, Long. 78˚54'21.6"E. Note that the color scale used for ABI_Chla is
kept same for other images in Figures 6-11. In the color composite image, white color represents sediment dominated waters
and red color represents bloom waters.
This bloom was responsible for a massive fish kill in
these waters, where dissolved oxygen dropped from 5
mg· L –1 to 0.1 mg·L–1 within a day, [35].
An example of T. erythareum bloom detected from
MODIS-Aqua data in the southeastern Arabian Sea dur-
ing the onset of the southwest monsoon (3 June 2009) is
shown in Figure 9. Field observations confirmed that the
bloom developed off Kollam (08°59.492'N, 5°59.334'E),
with a pale brown to pinkish red surface water discolora-
tion, spreading over an area of approximately 10 km2 on
3 June 2009. Pale brown indicated healthy algae at the
peak of its photosynthetic activity, while pinkish red in-
dicated the presence of photosynthetically less active
filaments. The bloom area was very fertile with copious
quantities of dissolved oxygen (6.85 ml·L–1), PO4-P
(0.108 μmol· L –1) and SiO4 (1.29 μmol· L –1). Lower
NO3-N (0.028 μmol· L –1) values in the bloom area did
not appear to affect T. erythareum growth from molecu-
lar nitrogen fixation. However, lower NO3-N values al-
tered the normal phytoplankton composition of this area
[17]. T. erythareum bloom is a very common phenome-
non in coastal waters of the eastern Arabian Sea (Man-
galore to Bombay), and is well-documented by scientists
at the National Institute of Oceanography (Figure 10)
[44]. It is interesting to note that a filament pattern of T.
erythareum bloom and their spatial distribution is clearly
captured in the ABI_Chl-a and color composite images.
The spatial distribution of T. erythareum bloom is
shown in Figure 11 for Kalpakkam coastal waters of the
Bay of Bengal (19 Feb 2008). An in-situ study indicated
that this bloom appeared during the relatively high tem-
perature and salinity (> 31 psu) conditions, low nitrogen
and high phosphate and total phosphorus conditions (Mo-
hanty, et al. [12]). As an effect, the contribution of

A. SIMON, P. SHANMUGAM 44
MODI
(5 Ap
Orissa coast
S/Aqua
ril 2005)
ABI-Chla
Rrs531_488_
Field ph oto
443
Figure 6. Algal bloom map of the Orissa coast (Rushikulya River mouth) on the Bay of Bengal gene rated from MODIS-Aq ua
data (5 April 2005) using the new algorithm (for detecting N. scintillans bloom). Right image is the field photograph of this
bloom which was sampled by Mohanty et al. (2007) during this period. In the color composite image, white color represents
sediment dominated waters and red color represents bloom waters.
ABI-Chla R rs 531_488_443
MODIS/Aqu
(24 Jan. 2006)
a
Field photo
Figure 7. Algal bloom map of the Gulf of Oman generated from MODIS-Aqua data (24 Jan. 2006) using the new algorithm
(for detecting N. milaris bloom). Right image is the field photograph of this bloom from Gomes et al. (2008) during this pe-
riod.

A. SIMON, P. SHANMUGAM 45
MODIS/Aqua
(
23 Nov. 2008
)
ABI-Chla R
rs
667_531_
Field photo
488
Figure 8. Algal bloom map of the Gulf of Aden and Oman generated from MODIS-Aqua data (23 Nov. 2008) using the new
algorithm (for detecting C. polykrikoides bloom). Right image is the field photograph of this bloom which was sampled by
Gheilani (2009) during this period.
MODIS /Aqu
3 Jun e 2009 a
ABI -Ch l
a
Rrs6
Fiel
78_531_443
d photo
Figure 9. Algal bloom map of the eastern Arabian Sea generated from MODIS-Aqua data (3 June 2009) using the new algo-
rithm (for detecting Trichodesmium A bloom). Right image is the field photograph of this bloom which was sampled by
Padmakumar, et al. (2010) during this period. In the color composite image, white color represents sediment dominated wa-
ters and green color represents bloom waters.

A. SIMON, P. SHANMUGAM 46
T. erythareum to phytoplankton density ranged from
7.79% to 97.01% and concentrations of chlorophyll and
phaeophytin increased abnormally (42.15 mg·m–3 and
46.23 mg·m–3 respectively) during the bloom period [12].
To validate the efficacy of the new algorithm, accu-
racy assessment was performed for all four types of algal
blooms. The overall accuracy was found to be 92.3%.
But just because 93.2% classifications were accurate over-
all, does not mean that each category was successfully
classified at that rate. Thus, the accuracy of each bloom
type was also calculated. The user’s accuracy and the
producer’s accuracy were 100% and 100% for C. polyk-
rikoides, 79.16% and 79.16% for N. scintillans, 100% and
80% for N. millaris, 100% and 86.95% for T. erythareum
(Table 2). The Kappa coefficient was 1.01. These statis-
tics demonstrate that the new algorithm is very effective
in terms of detecting and classifying all five algal bloom
types, making MODIS-Aqua data offer unrivalled utility
in monitoring the algal blooms in coastal and offshore
waters around India.
Overall Accuracy = ((13+19+8+20)/65) ×100 = 92.3%
Producer’s Accuracy, User’s Accuracy
CP = (13/13) × 100 = 100%, CP = (13/13) × 100 =
100%,
NS = (19/24) × 100 = 79.16%, NS = (19/24) × 100 =
79.16%,
NM = (8/10) × 100 = 80%, NM = (8/8) ×100 = 100%,
TE = (20/20) × 100 = 100%, TE = (20/23) × 100 =
86.95%,
Kappa Coefficient = NA-B/N2-B,
N = 65: A = (13+19+8+20) = 60,
B = ((13 × 3) + (19 × 9) + (8 × 8) + (20 × 20)) = 674,
Kappa Coefficient = 1.01.
6. Discussion
Recently, a new bio-optical algorithm was developed to
provide quantitative assessments of (chlorophyll) of algal
blooms in complex waters around India [9]. This algo-
rithm seems reasonable in discriminating pigment patches
from other water constituents, but is not intended to clas-
sify the different types of algal blooms in these waters. In
this study, an automated algorithm involving certain
band ratios and band differences has been developed to
classify and monitor four dominant algal blooms (T.
erythareum, N. scintillans, N. milaris, and C. polykri-
koides) in coastal and offshore waters around India. The
band ratios/differences and criteria used in this algo-
rithm are based on the unique spectral signatures of each
bloom type. It should be noted that atmospherically dis-
torted and improbable negative values at Rrs(412) and
Rrs(678) caused by the standard atmospheric correction
were neglected or flagged out. The grey color in the clas-
sified image and black color in the ABI-Chl image rep-
resent cloud/no data in the Figures 5-11. The perfor-
mance of this algorithm was tested on several MODIS-
Aqua imageries and the classified blooms were validated
with the in-situ data, chlorophyll image and color com-
posite images. Comparison with previous studies showed
that the spectral shape of the reflectance spectra of C.
polykrikoides from this study were similar to those of the
same species documented in coastal waters of Korea [30]
and at Bahía Fosforescente [22]. Its absorption and back
scattering properties were already described by Kutser 26.
However, the reflectance spectra of other species/bloom
types were quite different from those reported in the pre-
vious studies from other regions. It is important to note
that the difference in reflectance spectra could be attrib-
uted to many factors, such as pigment concentration,
atmospheric correction, bloom patchiness and sub-pixel
variability (MODIS-Aqua pixel resolution of 1.1 km may
include phytoplankton, suspended sediments and clear
waters). It is therefore difficult to compare satellite pixel
measurements of reflectance with the in-situ point meas-
urements. The algal bloom classified as T. erythareum
(in Figure 9) by the new algorithm was consistent with
field measurements, which showed that the surface water
discoloration was caused by the accumulation of T.
erythraeum and that water column also contained a col-
ony of T. thiebautii [17]. The surface water color in the
bloomed region varied from pale brown to pinkish red.
Pale brown indicated healthy algae at the peak of its
photosynthetic activity, while pinkish red indicated the
presence of photosynthetically less active filaments. The
T. erythareum bloom was observed off Kollam (08°
59.492'N, 75°59.334'E), with a pale brown surface water
discoloration, spreading over an area of approximately
10 km2 on 3 June 2009. Qualitative and quantitative
analyses of bloom samples revealed that in bloomed wa-
ters Trichodesmium erythraeum contributed 90% of the
surface phytoplankton population. The remaining 10%
was predominantly composed of diatoms and dinoflagel-
lates. Cell density was 1.14 × 106 filaments L–1, 1.968 ×
106 filaments L–1 and 1.51 × 106 filaments L–1 at blooms
sites off Kollam. [17]. The MODIS-Aqua cholorphyll im-
age and the color composite image also revealed the pres-
ence of T. erythareum bloom along the Kollam coast.
T. erythareum (Figure 10), which was classified along
Table 2. Accuracy assessment of the new algorithm for dif-
ferent types of algal blooms in coastal and offshore waters
around India.
CP NS NM TE Total
CP 13 0 0 0 13
NS 0 19 0 0 19
NM 0 2 8 0 10
TE 0 3 0 20 23
Total 13 24 8 20 65
CP: C. polykrikoides; NS: N. scintillans; NM: N. milaris; TE: T. erythareum.

A. SIMON, P. SHANMUGAM 47
MODIS/Aqua
(17 April 2009)
Cruise location map
(
NIO Re
p
ort
)
Bombay
ABI-Chla
R
rs
667_547
Mangalore
Field ph
_488
oto
Figure 10. Algal bloom map of the eastern Arabian Sea generated from MODIS-Aqua data (17 April 2009) using the new
algorithm (for detecting Trichodesmium A bl oom). Right image is the field photograph of this bloom which was sampled by
scientists at NIO (NIO report, 2009) during this period. In the color composite image, white color represents sediment domi-
nated waters and green color represents bloom w a ters.
MODIS/Aqua
Feb. 2008) (19
ABI-Chl
a
Rrs 443_531_678
Field Photo
Figure 11. Algal bloom map of Kalpakkam coastal waters generated from MODIS-Aqua data (19 Feb. 2008) using the new
algorithm (for detecting Trichodesmium B bloom). Left bottom image is the field photograph of this bloom which was sam-
pled by Mohanty, et al. (2010) during this period. In the color composite image, white color represents sediment dominated
waters and green col o r re presents bloom waters.

A. SIMON, P. SHANMUGAM 48
the Mangalore coast on 17 April 2009, was previously
sampled and reported by the NIO researchers [10]. It was
observed during their cruises that the diazotrophic algal
blooms of Trichodesmium dominated the entire coastal
region from Mangalore in the south to Ratnagiri in the
north. The blooms were visible on the surface of waters
with their characteristic brown color, similar to sawdust
sprinkled over water and long winding streaks. During
the decay phase of this bloom, Noctiluca populations and
slaps were also found in these waters. These findings
conclude that the T. erythareum bloom could trigger
other species during its decay phase. The color composite
image showed the linear spread of the bloom and it can
be confirmed by the Chl-a image (Figure 10). The new
algorithm also captured the T. erythareum bloom on 19
Feb 2008 in coastal waters of Kalpakkam (south-eastern
part of India), where an in-situ study reported a promi-
nent discoloration of the surface water caused by dense
and yellowish green colored streaks of about 4 to 5 m
width and 10 m - 20 m long patches (at point measure-
ment scale). The phytoplankton responsible for discol-
oration was confirmed to be Trichodesimum (specific
species not reported) and T. erythareum bloom had the
similar reflectance signature of similar blooms observed
on 3 June 2009. The color composite and Chl-a images
also confirmed the existence of these blooms.
There have been two different spectral signatures rec-
ognized from the MODIS-Aqua data for Noctiluca blooms,
which are often reported as N. scintillans and N. milaris.
However, some studies have recorded N. milaris as N.
scintillans. The difference in the spectral reflectance sig-
natures of these blooms may be due to their cell size,
shape and patchiness and perhaps their associated con-
stituents in water. Noctiluca is a heterotrophic dinoflag-
ellate and some have photosynthetic symbiont and also
feeds on other plankton which might influence their op-
tical characteristics [45]. These blooms appear green or
red color on different occasions. A prominent discolora-
tion of the surface water off the Rushikulya River mouth
on 5 April 2005 (Figure 6) is an example of the noticea-
bly dense and red-colored blooms of N. scintillans cov-
ering a wide area of approximately 16 sq.km (field
measurement scale) [12]. Their qualitative and quantita-
tive analyses of phytoplankton revealed that the density
of N. scintillans was 2.38 × 105 cell·L–1 against the total
cell count of 3.01× 105 cell·L–1, sharing almost 80% of
the total phytoplankton standing crop. This bloom was
reported to be associated with 29 other species of phyto-
plankton, which included nine species of dinoflagellates,
19 species of diatoms and one species of cyanobacteria
(Trichodesmium erythraeum) [12]. From their analyses it
is evident that the classification algorithm accurately
classified the different types of blooms. Though the N.
scintillans bloom appeared many times along this coast,
its appearance on 5 April 2005 was unique because of the
reported cell density being relatively higher than the
usual cell densities and exhibiting visible changes in
physical and chemical properties of seawater off the
Rushikulya River mouth and along the Orissa coast. The
large coverage of the blooms of N. scintillans was also
evident in Arabian Sea waters (Figure 5, Figure 6, and
Figure 8) and these blooms had the spectral signatures
similar to those of N. scintillans blooms previously con-
firmed by the field measurements on 5 April 2005.
Intricate patches of the C. polykrikoides bloom were
distinguished and classified from other algal blooms with
its distinct spectral signatures. The negative values at
Rrs(412) of C. polykrikoides spectra were the result of the
atmospheric correction problem in productive waters
[46-48] and therefore not used in the present analysis.
Satellite imagery showed two intense blooms of C. poly-
krikoides and N. scintillans in coastal and offshore waters
of Oman on 23 Nov. 2008 (Figure 8). While the former
one was confirmed by the field measurements, [34], the
latter one had the reflectance signatures similar to the N.
scintillans bloom signatures observed on 5 April 2005.
The color composite and Chl-a images indicated the pat-
terns of these blooms off Oman.
Many of the previous studies have focused in detecting
a particular bloom type or its special features/charac-
teristics (e.g., [26,28,30,49,50]). On the contrary, the new
algorithm simultaneously classified four major algal blooms
in oceanic waters around India. The use of the band ra-
tio/differences along with the necessary criteria resulted
in good discrimination between these algal bloom types,
however the coefficients and thresholds used in our algo-
rithm may require being fine-tuned based on more in-situ
data sets and when an accurate atmospheric correction
algorithm is developed for complex waters. However, the
criteria used to identify turbid water pixels work very
well in many coastal regions around India, and therefore
no further changes are warranted at this stage.
7. Conclusions
The new algorithm has proved useful in classifying the
four types of algal blooms in coastal and offshore waters
around India. These algal blooms are characterized by
different spectral signatures. The peaks and troughs ob-
served in the reflectance spectra are related to different
phytoplankton properties, e.g., chlorophyll-a and other
pigment absorption, cell density/bloom intensity and as-
sociated optical properties. This algorithm takes advan-
tages of these specific reflectance features and utilizes
the combination of reflectance band difference/ratios and
derivative signatures for bloom classification. The classi-
fied blooms are closely consistent with field measure-
ments data, ABI_Chla and color composite images; thus

A. SIMON, P. SHANMUGAM 49
its classification accuracy is high for all the classified
blooms. These results suggest that the new algorithm has
the potential to classify and monitor the investigated al-
gal blooms. The new algorithm will become increase-
ingly valuable as further improvements are made based
on more in-situ data sets for different types of algal
blooms. The algorithm coefficients may require being
fine-tuned when an accurate atmospheric correction al-
gorithm is developed for retrieval of water-leaving radi-
ance in complex waters around India. Our further work
will therefore focus on improving these parts and pro-
viding a detailed validation with large in-situ data sets.
8. Acknowledgements
This research was supported by INCOIS and IITM-ISRO
Cell under the grants (OEC1011102INCOPSHA and
OEC0910129ISROPSHA).
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