Remote Sensing of Cyanobacteria Pigment Concentrations
During Spring Bloom Formation: A Case Study in Taihu Lake
For RSE? [maybe, after some revision. Currently it reads like a technical
report. Focus on this question: what is your major contribution to the
algorithm development for this turbid environment? Also, what’s the
difference between remote sensing of blooms during early stage and
remote sensing of blooms in other stages?]
Abstract:
1.
INTRODUCTION
[Introduction should focus on the existing problems – difficulty in separating PC from
Chla, importance to detect blooms during early stage, unknown accuracy of existing
algorithms in estimating PC in Taihu Lake. Then followed by your objectives. In this
section, the existing algorithm can be cited briefly, with details given in the Method
section. This section must clearly show the current need for robust algorithms to
quantify blooms during bloom formation, current problems in remote sensing, and
then what you want to do]
Freshwater ecosystems occupy less than 1% of the earth’s surface but deliver goods
and services of enormous global value (Johnson et al. 2001). However, with the
human activities and economic development, the freshwater eutrophication especially
1
in inland lakes has become one of the most widespread environmental and social
problems around the world (Smith 2003). Eutrophic inland waters often exhibit
cyanobacteria, and many of them are potentially toxic and thus are often nuisance
organisms as candidates for algal blooms (Kutser et al. 2006). Indeed, it will affect
drinking water, aquaculture, crop irrigation and even recreation and leads to a horrible
disaster, and is attracting the increasing attention of public and government
organizations.
Because of these problems, there is a clear need and have concerted efforts to
monitor cyanobacteria and their toxins to prevent and manage freshwater
eutrophication. However, the traditional method performed by taking ship-borne
water samples and analyzing the samples in a laboratory are time consuming and
labor intensive. Moreover, it is difficult to comprehend the temporal and spatial
pattern for one entire lake. Actually, satellite has become a powerful tool and has been
preliminarily utilized in many freshwater waters such as Laurentian Great Lakes in
USA (Gons et al. 2008; Vincent et al. 2004; Wynne et al. 2008), Lake Loosdrecht,
Loch Leven and Esthwaite Water in Europe (Hunter et al., 2010; Simis et al. 2005b),
Lake Victoria in Africa (Okullo et al. 2007) and Lake Taihu in China (Hu et al. 2010;
Ma et al. 2006b; Wang et al. 2011).
The concentration of chlorophyll a (Chla) has often been used as a proxy for the
amount of phytoplankton of many water bodies (Gons et al. 2002; Kutser et al. 2006).
Several methods for estimating Chla with remote sensing are being investigated, and
three algorithms approaches are found to be used popularly. The ratio of a NIR band
2
(around 700-710nm) over a red band (around 665-685nm) has been successfully
applied to a wide range of turbid water bodies, which can enlarge their differences
between the absorption maximum and the reflectance peak of Chla (Gons 1999; Simis
et al. 2007). This method depends on empirical linear regression to predict Chla of
lakes water. Using similar bands ratio but based on radiative transfer modelling
(Gordon
et
Chla    R(704)
al.
1975),
Gons
developed
a
semi-analytical
algorithm
R(672)   aw(704)  bb   aw(672)  bb / a Chla  672  for Chla retrieval (Gons
1999). Recently, a three-band model
Chla  Rrs-1  1  Rrs-1   2   Rrs   3 was also
developed to estimate Chl-a concentration (Dall'Olmo et al. 2003), and the two bands
ratio model was regarded as a special case of the three-band model (Gitelson et al.
2008).
Chla present in all phytoplankton and it is possible and useful to separate
cyanobacteria from algal species [? Do you distinguish? How do you use Chla to
distinguish cyanobacteria from other species?] based on remote sensing reflectance
during the blooms periods. However, Chla does not provide enough information on
the presence of cyanobacteria groups especially in its early stages before the blooms
formed. Phycobilin pigment phycocyanin (PC) is the characteristic of the presence of
cyanobacteria and possibly a useful indicator of cyanobacterial biomass (Ruiz-Verdu
et al. 2008; Simis et al. 2005a). PC can be detected based on the absorption feature
around 615 nm (Bryant 1981), and current algorithms mainly are based on the
quantification of the reflectance trough at this region in remotely sensed data
(Ruiz-Verdu et al. 2008; Simis et al. 2005a; Simis et al. 2007). The single reflectance
3
ratio uses NIR band reflectance at 705 nm as reference, and then targets the PC
absorption at 620 nm, and this index has been used effectively in previous studies
concerned with turbid inland waters (Hunter et al., 2010; Hunter et al. 2009; Hunter et
al. 2008b). To account for co-absorption by Chla and PC at 620nm, combined with
nested ratio (NIR and red band) (Gons 1999; Gons et al. 2002, 2005), the second
algorithm firstly estimates absorption by Chla at 620 nm and then subtracts this and
pure water absorption from total absorption at 620nm to yield the absorption of PC.
Subsequently, the concentration of PC can be solved using the known specific
absorption coefficient at 620nm (Simis et al. 2005a). Based on the Chla three-band
model (Dall'Olmo et al. 2003), Hunter et al. developed a three-band model to estimate
PC, and λ1 locates at around 620nm (Hunter et al. 2008a). More details will be
discussed under the subsection “METHODOLOGY”.
Life cycles of phytoplankton are complex and in which the organism spends
different stages of its life (Hansson 1996). The blooms formation has been classified
as a series of processes: autumnal sedimentation of declining blooms biomass,
subsequent overwintering in bottom sediments, recruitment in spring, biomass
increase and bloom formation (Kong and Gao 2005). The spring recruitment of algae
often increased total phytoplankton abundance and may have a considerable impact
on dominance patterns in the phytoplankton community in shallow water (Hansson
1996). To detect early stages of cyanobacteria would be of crucial and economic for
public health value, especially if it is on a sufficiently timely basis for a response plan
(Vincent et al. 2004). For Lake Taihu and its nearby waters, current researches mainly
4
concentrate on Chla remote estimation (Duan et al. 2010; Le et al. 2009b; Ma et al.
2006a; Wang et al. 2011; Zhang et al. 2008). However, quantitative studies of PC, a
useful indicator of cyanobacterial biomass, especially in its early stages have not been
done.
The Medium Resolution Imaging Spectrometer (MERIS) onboard the ENVISAT
mission of the European Space Agency (ESA) was primarily intended for ocean and
coastal water remote sensing (Rast et al. 1999). The spatial resolution (300 m) and
spectral properties (15 narrow bands in the visible and near-infrared) of MERIS, and
the revisit period of one to three days (latitude dependent) of ENVISAT, render the
sensor also suitable for at least larger inland waters (Alikas and Reinart 2008; Gons et
al. 2008; Odermatt et al. 2010; Odermatt et al. 2008). The aim of the research
presented in this paper was thus: 1) to examine and compare the performance of
popular algorithms for retrieval of Chla and PC concentrations in highly turbid waters;
2) Optimize these algorithms to meet the requirements of lower phytoplankton
pigments in early spring before the blooms formed; 3) to show the spatial-temporal
variability of phytoplankton pigments using in-situ data and MERIS FR image.
2.
METHODOLOGY
2.1. Research Area
Lake Taihu, with an area of 2,338 km² and an average depth of 1.9 meters, is the third
largest freshwater lake in China. It is a typical shallow lake in the delta of Yangtze
River, at 30◦55'40"–31◦32'58"N and 119◦52'32"–120◦36'10"E, on the border of the
5
Jiangsu and Zhejiang provinces in China. Lake Gehu with an area of 117 km², and
Lake Dongjiu with an area of 8 km² locate at western parts of Lake Taihu basin
(Figure 1). Lake Taihu basin is a depression of land, in which water from all sides
gathers to the center and then diffuses in all directions, forming a complex
hydrosystem that contains interlaced rivers, dense water nets, and dotted depression
lakes of different sizes (Qin 2008). The western parts of the basin are hilly and the
eastern parts are lowland plains; thus, Lake Gehu and Dongjiu belong to the upper
reaches and flow into Lake Taihu through rivers in the west. In recent years, Lakes
Taihu, Gehu and Dongjiu have been plagued by pollution as a result of rapid
economic growth in the surrounding region. Increasing eutrophication and reoccurring
algal blooms, often dominated by Microcystis spp., are a significant threat to the
millions who rely on this lake for water supply (Duan et al. 2009; Guo 2007; Xu et al.
2010).
2.2. Field Data Measured [if the methods to determine ag, ad, aph, etc. have been
published elsewhere, you can simply cite them and give brief descriptions only.
Focus on the particular sampling situation in these lakes: Dates, Time, number of
samples, processed right away or later in the lab, etc.]
Water sampling and measurements were performed from 102 sites visited during a
cruise between April 23 and May 3, 2010, and 5 sites were removed from the dataset
due to lack Chla and PC data (Figure 1). At each station GPS coordinates (0.3-3 m
accuracy) were recorded and water transparency was measured with a secchi disk ~20
6
cm in diameter. Remote sensing reflectance was measured under the guidance of the
NASA protocols by Mueller et al. (2003). Water samples were collected from the
surface to about 30 cm below in the vertical direction with a standard 2 liter
polyethylene water-fetching instrument immediately after measuring spectra. Then
they were held on the deepfreeze half with ice bags for reserving until about four
hours every afternoon, then returned to the laboratory for concentration and
absorption measurement.
Reflectance measurements
Reflectance was obtained from measurements of the radiance above the water (Lsw),
sky radiance (Lsky) emitted from the water surface and sky, and the radiance above the
plate (Lp) using a FieldSpec Pro Dual VNIR (ASD Ltd., USA), following the
guidelines laid out in Mueller et al. (2003). Prior to the field campaign, the absolute
radiance calibrations to the two detectors were performed. The viewing angles from
the water surface were 40° and 135°, respectively, which were determined by a
hand-handle with adjusted-angle equipment. The integration time was chosen
according to the intensity of radiance received by the ASD detector and the dark
reading was obtained each time when the integration time was changed. The
measurements were made from a location that minimizes shading, reflections from
superstructure, ship’s wake or associated foam patches, and whitecaps. Additionally,
the selected location was also easy to point in a direction away from the sun to reduce
specular reflection of sunlight. The measurement also took into consideration to avoid
big pieces of bright cloud by observing with naked eye under help of shelter board.
7
The measured remote sensing reflectance Rrs is calculated from:
Rrs =
Lsw -  Lsky
 Lp  p
(1)
Where ρ is the dimensionless air-water reflectance related to the surface Fresnel
reflectance and often taken as 0.02 for moderate wind speed and moderate solar and
viewing angles (Mobley 1999), and ρp is the reflectance of the plate.
Concentrations of water constituents
Dissolved organic carbon (DOC) concentrations were measured with a type 1020
TOC (OI Corp., USA) after the sample was filtered through Whatman GF/F glass
fiber filters (0.70 μm nominal pore size). Chl-a was measured spectrophotometrically.
Samples were first filtered onto Whatman GF/F glass fiber filters, which were soaked
in 90% ethanol in the dark for 4-6 hours. The sample was then heated to 80-90°C for
3-5 minutes. Absorbance at 665 and 750 nm of the extract were measured with a
UV2401 spectrophotometer (Shimadzu Corp., Japan) and Chl-a was calculated
according to the reference that filtered water. The concentrations of phycocyanin
(5-ml samples) were determined after extraction with 0.05 M pH 7.0 Tris buffer and
using a Shimadzu UV2401 spectrofluorophotometer (Shimadzu Corp., Japan) at an
excitation wavelength at 620 nm and an emission wavelength at 647 nm (Abalde et al.
1998; Zhang et al. 2007).
Suspended
particulate
matter
(SPM)
concentrations
were
determined
gravimetrically from samples collected on pre-combusted and pre-weighed GF/F
filters with a diameter of 47 mm, dried at 95°C overnight. SPM was differentiated
into suspended particular inorganic matter (SPIM) and suspended particular organic
8
matter (SPOM) by burning organic matter from the filters at 550°C for three hours
and weighing again.
Absorption measurement
The absorption by total suspended matter, pigment, and detritus were determined
using the quantitative filter technique. Particulate matter from a known water volume
was concentrated onto a 47-mm Whatman GF/F filter (Yentsch 1962). Absorption of
TSM was measured using a Shimadzu UV2401 spectrophotometer (Shimadzu,
Tokyo, Japan) at 1-nm intervals in the range 400 to 700 nm. The filter was placed in
close proximity of the detector/collimator (distance 2-3 mm), no diffuser was used.
The optical density at 750 nm was subtracted from the absorbance spectrum which
was subsequently corrected for path-length amplification following (Cleveland and
Weidemann 1993):
ODs  0.378OD f  0.523OD2f
OD f  0.4
(2)
where ODf and ODs are the optical densities before and after correction for
path-length amplification. Absorption by total particulate matter (ap) was then
calculated as:
a p ( )  2.303 
S
ODs ( )
V
(3)
where S is the clearance area of the filtration manifold and V the volume of water
concentrated onto the filter.
After measurement of the total particulate absorption, the filter was soaked in
methanol for 4 hours to dissolve phytoplankton, and rinsed with filtered water.
Phytoplankton was dissolved in methanol and the remaining particles on the filter
9
were non-phytoplankton particles. The spectral optical density of non-algal particulate
(ad()) was then measured with the same method as described for ap(). Subsequently,
the phytoplankton pigments absorption aph() was obtained by subtracting ad from ap.
CDOM absorption first filtered through a Whatman GF/F filter, and then refiltered
through a Millipore filter with 0.22-μm pores ， was measured using a
spectrophotometer, with distilled water as reference. Scans were taken at 1-nm
intervals between 280 and 700 nm. The absorption coefficient was calculated as:
ag' ( )(m 1 )  2.303 
OD( )
r
(4)
where r is the path length in the cuvette. To correct for scattering casued by fine
particulates in the filtrate, we applied a baseline correction following (Keith et al.
2002):
ag ( )(m 1 )  ag' ( )  ag' (700) 

700
(5)
where ag′() and ag() are, respectively, the uncorrected and corrected absorption
coefficient.
2.3. MERIS images [I still need to find time for this. Currently so swamped with
the oil proposals]
MERIS full resolution (FR) data was acquired on April 29, 2010. The images were
geolocated and masked for land, clouds and invalid reflectance. Atmospheric
correction was performed using Seadas, which outperformed other previous
atmospheric correction algorithms for turbid inland lakes.
10
2.4. Existing remote sensing algorithms to estimate Chl-a and PC [This is where
you insert the description of the existing algorithms from your Introduction
section]
Corresponding to the bands set of MERIS sensor, the semi-empirical algorithm used
to retrieve Chla and PC were developed (hereafter referred to as the Chla and PC ratio
algorithm):
Chla
 PC 
Rrs (709)
Rrs (665)
Rrs (709)
Rrs (620)
(6)
(7)
The proposed ratios are based on the high sensitivity of the features found at 665
and 620 nm to change in Chla and PC concentration (Randolph et al. 2008).
Absorption in the 620 and 665 nm bands ( a ( ) ) is assumed to be dominated by water
( aw( ) ) and phytoplankton pigments ( aph( ) ) (620 nm: PC and Chla; 650 nm: Chla
alone), thus a(620)=aChla(620)+apc(620)+aw(620) ; and a(665)=aChla(665)+ aw(665) .
From the basic radiative transfer equation presented by Gordon (Gordon et al. 1975),
it is easily transformed as:
a ( 1) 
Rrs ( 2)
  a( 2)  bb   bb
Rrs ( 1)
(8)
where backscatter coefficient bb is considered as a constant independent on
wavelength and can be calculated as (Gons et al. 2005):
bb (778.75)  1.61
Rrs (778.75)
0.082  0.6  Rrs (778.75)
11
(9)
Accompanied by a series of validation work using field data (Gons et al. 2002,
2005) (hereafter referred to as the Gons algorithm),
aChla (665) 
Rrs (709)
 (aw(709)  bb )  bb p  aw(665)
Rrs (665)
 Chla  
aChla  665 

aChla
 665 
(10)
(11)

where p is 1.062, and aw(709) , aw(665) and aChla
 665 (the specific absorption
coefficient of Chla at 665 nm) are 0.70 m-1, 0.40 m-1, 0.0161 m2·mg-1, respectively.
Similar with Chla retrieval algorithm, for PC, the important is to get apc(620):
apc(620)=a(620)  aChla(620)  aw(620)
(12)
Based on Eq. (10), an empirical correction factor  and  was introduces to
relate absorption retrieved at 620 and 665 nm to actual pigment absorption at the same
wavelength (Simis et al. 2005a; Simis et al. 2007) :
 R (709)

a Chla+PC  (620)   rs
 (aw(709)  bb )  bb  aw(620)    1
 Rrs (620)

(13)
 Rrs (709)

 (aw(709)  bb )  bb  aw(665)    1
a Chla(665)  
 Rrs (665)

(14)
With the conversion factor  that relates in vivo absorption by Chla at 665 nm
from Eq. (14) to that at 620 nm and Eq. (13) (Simis et al. 2005a) (hereafter referred to
as the Simis algorithm):
 R (709)

apc(620)   rs
 (aw(709)  bb )  bb  aw(620)    1    aChla(665)
 Rrs (620)

 PC  
where  , 
apc  620 
apc  620 
(15)
(16)
and  are 0.84, 0.68, 0.24, respectively, aw(709) , aw(665) ,
12
aw(620) and apc  620  (the specific absorption coefficient of PC at 620 nm) are
0.727 m-1, 0.401 m-1, 0.281 m-1, 0.007 m2·mg-1, respectively.
Recently, a three-band model is developed to estimate Chla concentrations in lakes
water (Dall'Olmo et al. 2003; Gitelson et al. 2008) and has been approved and can be
used for MERIS data (hereafter referred to as the Gitelson algorithm):
Chla   Rrs-1  1  Rrs-1   2   Rrs   3
(17)
The following assumptions based on: (a) bb is spectrally invariant between 1 and
2; (b) aph( 1) >> aph( 2) ; (c) ad(1) + ag(1)  ad( 2) + ag( 2) . Therefore, 1 locates at
665 nm, 2 at 709 nm, and 3 at 753 nm for MERIS (Gitelson et al. 2008). Compared
to two band model,  Rrs-1  1  Rrs-1   2  reduces the effects of detritus and CDOM
absorption on remote sensing retrieval of Chla. This algorithm was also adapted to
estimate PC concentrations (Hunter et al. 2008a) (hereafter referred to as the Hunter
algorithm):
 PC    Rrs-1  1  Rrs-1   2   Rrs   3
(18)
However, λ1 shifts to the PC absorption peak around 610-630 nm (Hunter et al.,
2010; Hunter et al. 2008a), while it locates at 650-670nm corresponding to the
maximum sensitive to Chla absorption. Although the retrieval of PC using the
three-band model seems slightly problematic and  Rrs-1  1  Rrs-1   2  is difficult to
remove the absorption of other water components from PC at this area, it’s a new
challenge using the medium-independent model to estimate PC in inland waters
(Hunter et al., 2010).
13
2.5. Accuracy assessment
The comparisons among these three algorithms were assessed by means of three
indices, namely the root mean square error (Crabtree et al.) [year?], mean normalized
bias (MNB) and normalized root mean square error (NRMS). These indices are
defined as follows (Gitelson et al. 2008; Yang et al. 2011):
 X  X
N
RMSE 
i 1
esti,i
meas,i

2
N
(19)
MNB  mean( i )%
(20)
NRMS  stdev( i )%
(21)
where N is the number of samples; Xesti,i and Xmeas,i are the estimated and in situ
measured values, respectively; The percent difference between Xesti,i and Xmeas,i was
quantified:
 i  100  ( X esti,i  X meas,i ) X meas,i
(22)
Systematic and random errors were characterized by the mean normalized bias (MNB)
and by the normalized root mean square error (NRMS), respectively.
3.
RESULTS
3.1. Phytoplankton pigments concentrations
A combined dataset shows a large range in both Chla (0.13-46.98 g/l) and PC
concentration (0.05-7.71 g/l) (See Table 1). [It’s better to add a histogram figure to
show the data distributions for each parameter] 89 sampling sites showed PC
concentration of less than 2 g/l, while the other 8 sites had a higher PC concentration
14
from 2.53g/l to 7.71g/l. [what is the criteria to separate < 2 and > 2.5 for the two
groups? A histogram may help?] Most of the high PC sites locate at Lake Gehu
(Figure 1) [where in the Figure? Need to annotate] where algae can be easily seen
from the surface water (Figure 2(b)), but low PC sites cannot see algae with human
eyes (Figure 2(a)). A positive correlation for a low PC dataset exists between in situ
Chla and PC with the coefficient of determination (R2) of 0.687, while the
relationship was negative but higher R2 (=0.8375) for high PC dataset (Figure 3). The
ratio of PC and Chla (PC: Chla) is an indicator of the proportion total algal biomass
that can be attributed to cyanobacteria (Randolph et al. 2008). The mean PC: Chla
value for whole dataset is 0.13, and for low PC dataset is 0.11 while for high PC
dataset is 0.31, respectively. Phycocyanin is more prevalent than chlorophyll a in
Lake Gehu and a few area of west Lake Taihu, ultimately indicative of a
cyanobacteria dominated water body.
3.2. Chla model
The performance of the three algorithms was examined (Table 2). The semi-empirical
algorithms for the retrieval of Chla were derived using quadratic functions of the
[709:665] band-ratio. It showed a linear relationship [the linear relationship is there in
the log-log scale for Chla > 10 only!] between the measured concentrations of Chla
and the band-ratio and provided the marginally better [better than what?] estimation
(R2=0.88; RMSE=4.80 g/l) (Table 2 and Figure 4(a)). However, the algorithm
overestimated Chla with MNB=74.10% and NRMS=206.36%. This algorithm
15
performed better than the semi-analytical Gons algorithm, while it greatly
overestimated
the
concentrations
of
Chla
(R2=0.89,
RMSE=23.99
g/l,
MNB=188.15%, NRMS=233.18%) (see Table 2 and Figure 4(b)) due to a low

aChla
 665 value (=0.0161 m2·mg-1) that perhaps not suitable for the waters of this
research. Table 2 shows that the Gitelson algorithm also provided good estimates of
the actual Chla concentration (R2=0.90) and was better (RMSE=4.47 g/l,
MNB=86.55%, NRMS=278.04%) than those returned by the semi-empirical band
ratio algorithm (see Table 2 and Figure 4(c)). [I don’t like the way the statistics are
presented. They obviously do not work for the low Chla range, and this is a
well-known problem of all 3-band algorithms. You may present the overall statistics,
and separate statistics for high Chla range only. And then emphasize that none of
them works for Chla < X]
In the three algorithms, the highest relative errors (overestimations) occurred at low
Chla concentrations (<1 g/l) which contributed greatly to MNB and NRMS, while
the best predictions were found in the high concentration range (>10 g/l) especially
for the band ratio and Gitelson algorithms. In addition, the Gons algorithm shows a
systematic overestimation bias compares to 1:1 line (Figure 4(b)).
3.3. PC model
Table 2 provides the results of PC algorithms in these three lakes waters. Apparently,
all three algorithms gave a relatively poor precision with R2 varies from 0.13 to 0.17
when they were applied into all data together. However, when the dataset was
16
separated by PC concentrations (2 g/l) into two parts, all three algorithms showed
better results with the increasing R2 and lower RMSE for each dataset. For these
samples that the PC concentrations was lower than 2 g/l collected mainly in Lake
Taihu and Dongjiu, the best-performing algorithm (R2=0.71, RMSE=0.20 g/l) was
developed
using
the
Hunter
algorithm
based
on
a
three-band
model
 Rrs-1  620  Rrs-1  709  Rrs  754 . The band ratio [710: 620] algorithm also performed
strongly (R2=0.67, RMSE=0.22 g/l) when applied to the low PC dataset. The Simis
algorithm performed marginally poorer (R2=0.54, RMSE=29.88 g/l) than the other
two algorithms. The trends of R2 resulting from all three algorithms became more
pronounced when using the high cyanobacterial biomass subset (>2 g/l) from 0.70 to
0.72, compared to the low PC dataset. Especially the Simis algorithm had a
significantly increasing R2 (=0.70) but higher RMSE (=39.21 g/l). Overall, the band
ratio and Hunter algorithms yielded reasonable estimate of PC for the low and high
dataset, respectively (Figure 5(a) and (c)). The Simis algorithm showed a tendency to
overestimate PC at all sites with MNB varied from 1045.90% to 7091.22% while

apc
 620 is 0.007 m2·mg-1 (Table 2 and Figure 5(b)). [Same comments as for Fig. 4.
Present statistics for high-range PC, as they don’t work for the low range]
3.4. Algorithm Comparison
For these semi-empirical algorithms of Chla and PC, the three-band algorithms (the
Gitelson and Hunter algorithms) always provided the best results for each dataset
perhaps due to it can remove the effect of detritus and CDOM in highly turbid lakes
17
(Gitelson et al. 2008). The band ratio (709:665 and 709:620) algorithms were slightly
better than the semi-analytical algorithms (the Gons and Simis algorithms) in some
dataset with lower RMSE. This is perhaps somewhat surprising given that the
coefficients in the empirical algorithms were specifically optimized for this dataset
(Hunter et al. 2010). The parameters a and b of the linear regression equation using
the Gitelson algorithm were re-explained in our previous research, and they are
directly linked to specific inherent optical properties of the water body (Duan et al.
2010). The comparison between the parameters a and b estimated from reflectance
data with respect to those determined from the inherent optical properties during
different measurement campaigns shows a high correlation. It offers a robust
alternative to other Chla estimation approaches presently being used. However, the
parameters a and b of most semi-empirical algorithms do not have a physical
significance, and they have to be acquired by regression fitting with in situ data while
used in the different waters. Therefore, it will be difficult for these algorithms to
estimate Chla or PC concentrations from remote sensing reflectance Rrs directly.
Generally, the semi-analytical algorithms are considered more transferable across
different water types and could be robust. Figure 6 shows that the Gons algorithm
provide reasonable estimates of aChla  665 when applied to the data acquired over
Lake Taihu, Gehu and Dongjiu with a high R2=0.9776 and low RMSE=0.43 m-1.
[This is not a good measure. Use percentage] However, the algorithm predicted Chla
with a relative random uncertainty (NRMS) of 26.57% and with average bias (MNB)
of -27.96%. Although the Simis algorithm underestimated the values slightly with
18
MNB=-10.75% and NRMS=37.86% (Figure 7(a)), [combined Fig. 6 and 7] it did
provide a better aChla  665 (R2=0.9789, RMSE=0.14 m-1) using a new equation (Eq.
(14)). The fact that the accuracy of the semi-analytical algorithms is shown here to be
comparable to that achievable with optimized empirical models merely strengthens
the case for the wider use of analytically-based approaches for the retrieval of
in-water constituents (Hunter et al. 2010). Since the Gons and Simis algorithms are
both based on the basic equation (Eq. (8)) with a same assumption that the total
absorption at 665 nm attributes to Chla and pure water alone, Figure 8 [This figure is
not necessary. Text description is enough] shows that the values of aChla  665
estimated by the Gons strongly correlated with the values estimated by the Simis
algorithm (R2=0.9999) and they also demonstrated a near 1:1 relationship. This
provided a useful benchmark by which to assess the correlation of the two
semi-analytical models. However, there are other water components contributing on
the absorptions at 665 nm such as detritus and CDOM in the real world. In order not
to overestimate aChla  665 , the Gons and Simis algorithms introduced a correction
factor p (Eq. (10)) and  (Eq. (14)), respectively. The correction factors made an
over correction in Lake Taihu and its nearby waters, and both underestimated the
values, especially for the Gons algorithm (Figure 6 and Figure 7(a)).
It is interesting that all algorithms used to retrieve PC concentrations showed a
relatively inconsistent performance for the whole dataset but worked well while used
in two dataset, separately [ not sure what you want to say]. Since these algorithm
targets the band at 620 nm which corresponds to PC and Chla absorption (Ruiz-Verdu
19
et al. 2008; Simis et al. 2005a; Simis et al. 2007), they will perform better while in a
certain type of waters where has a similar PC: Chla ratio that have been confirmed in
many previous researches (Hunter et al., 2010; Hunter et al. 2009; Hunter et al.
2008b). Because the scatters plots of Chla and PC showed a two-phase by a threshold
(2 μg/L) (Figure 4), it’s the possible main reason [what’s the main reason?
Two-phase?] for the inconsistent performance of PC algorithms. The Simis algorithm
provided
a
reasonable
result
of
total
pigment
absorption
at
620
nm
a Chla+PC  620  except for the intermediate range (R2=0.9320) (Figure 7(b)) or the two
dataset by the threshold of 2 μg/L (Low dataset: R2=0.9228; High dataset: R2=0.9975).
[Fig. 7b does not show these two datasets] Apparently, in Taihu and its nearby waters,
the increased contribution of PC to absorption at 620nm from the low PC dataset to
the high dataset, is known to lead to errors on the estimation of PC. Since the
correction for Chl a absorption at 620 nm through a constant fraction ε of aChla(665)
(Simis et al. 2007), the Simis algorithm did not produce a correct apc(620) for all data
or some of the data at least [this is a vague sentence]. The relative contribution of
Chla to absorption at 620 nm is obviously strongly dependent on the abundance of
PC-containing cyanobacteria and the floristic composition of the phytoplankton
community (Hunter et al. 2010). Therefore, it’s necessary to correct the parameter for
the contribution of Chla.
20
3.5. Calibration of the Simis algorithm using local data
The main sources of errors for an algorithm used to retrieval the water color
parameters concentration can be identified as i) uncertainties in the measurement of
at-sensor radiances with a wrong input into the retrieval algorithm, ii) uncertainties in
the model structure due to simplifying assumptions, and iii) uncertainties in the
determination of the parameters appearing in the model, which are often the dominant
contribution to the overall uncertainty (Volpe et al. 2011; Yang et al. 2011). Figure
7(b) shows the relationship between in situ measured and estimated a Chla+PC  620 
from the Simis algorithm with a higher R2=0.9320 and lower RMSE=0.12 m-1. In
additions, the Simis algorithm underestimated a Chla+PC  620  with MNB=-18.04%
perhaps due to another correction factor  (Eq. (13)). Since the correction factors
 and  in the Simis algorithm were optimized using in situ data of lakes water in
the Netherlands (Simis et al. 2005a), they are perhaps not suitable for Lake Taihu and
its nearby waters especially with low phytoplankton pigments in early spring.
Therefore, it’s necessary and significant to re-calibrate the  and  values using in
situ measured data of the current research waters.
Recently, many researches have been concentrated on the Simis algorithm which
has been validated using in situ measurements at a series of lakes and reservoirs in the
Netherlands, Spain and the United States (Hunter et al. 2010; Randolph et al. 2008;
Ruiz-Verdu et al. 2008). It was found to significantly outperform other foregoing
algorithms for PC retrieval. However, the algorithm needs further validation for other
Case-Ⅱ waters and, in particular, for application with airborne and satellite sensors
21
due to atmospheric and adjacency effects can have a significant effect on the retrieval
of pigment concentrations over inland waters (Alikas and Reinart 2008; Hunter et al.
2010). Figure 9(a) shows the relationship between uncorrected retrieval of absorption
and in situ measurements of aph    that was used to find the optimal values for 
and  . Unlike the Simis algorithm, the intercept values were supposed as zero used
in the regression fitting, and the relationship between the uncorrected absorptions and
measured absorptions was considered as a ratio. Eq. (14) with  =1 related to
aChla  665 with a regression slope 0.6369 (R2 = 0.9771), and Eq. (13) with  =1
resulted in a slope of 0.7290 (R2 = 0.8932). All results presented hereafter were
obtained with the adopted values for  (=0.6369) and  (=0.7290), which showed
different slightly compared with the original parameters provided by the Simis
algorithm. Figure 9(b) showed the updated results using new  and  , and the
aChla  665 and a Chla+PC  620  estimated both reduced the RMSE slightly (0.12 m-1
and 0.11 m-1, respectively) (Figure 7 and Figure 9(b)).
Since the PC dataset was separated into two parts [do you mean Fig. 3?], it’ll be
difficult to estimate PC concentrations using satellite image data. Nobody can tell
which model is better for all the waters, or how should we make a borderline so that
we can determine a model which is better for corresponding area. Therefore, it’s
necessary to recalibrate the parameters and build a union algorithm that can be used in
whole dataset [No way – I see no algorithm will work for the low range]. The
empirical and band ratio algorithms are difficult to rebuild a union model as
mentioned in Table 1. However, the Simis algorithm based on the radiative theory
22
did provide a reliable a Chla+PC  620  especially after recalibration. The big problem
is that it did not provide a reliable ε to get accurate aPC  620 . The parameter of ε was
difficult to be derived directly from in vivo absorption measurements since no
phytoplankton samples were present that contained only Chla and no accessory
pigments absorbing in the 600–700 nm region.
The conversion factor  that relates in vivo absorption by Chla at 665 nm to that
at 620 nm was defined as the value where the slope of the linear least-squares fit of
modeled against observed PC concentration was ~1 (Simis et al. 2005a). The
contribution of Chla is relatively insignificant in waters with high PC:Chla ratios, and
it was 0.3–0.4 for Chla in algal species. However, the presence of accessory
photosynthetic pigments can cause errors at estimating ε. The specific in vivo
absorption of Chlb with ε=0.5–0.6 and Chlc with ε=1.7–3.7 [don’t understand this
sentence] . Obviously, with increasing concentrations of Chlb and Chlc, the
ε-correction of 0.24 is increasingly inadequate and the derived PC absorption will be
too high (Simis et al. 2007).
Perhaps due to the larger differences between aPC  620 and aChla  620  among
all samples and the presence of accessory pigments, it was very difficult to get an
optimal value for Lake Taihu and its nearby waters with the slope = 1 and the offset =
0 while using the linear least-squares fit, and the maximum R2 was 0.30. Here a new
parameter  was introduced to explain the proportion of PC among the total
absorption at 620nm, defined as:
 =[PC]:[PC]+[Chla]
23
(23)
where [PC] and [Chla] means the concentration of PC and Chla, respectively. Figure
10 showed that  varied from 0.007 to 7.27, and most located at 0.02-0.1. With the
help of  [what does it mean?], aPC  620 showed a higher linear relationship with
in situ measured PC (R2=0.7367, Figure 11). It proves that  can represent the
proportion of PC relative to all pigments at 620nm. [What difference does Fig. 10
make from Fig. 3?] [Figure 11 is a stand-alone figure – why do you need the help of
 ?]


3.6. Variability of aChla
 665 and aPC
 620
Although the Gons and Simis algorithms provided a reasonable aChla  665 (Figure
6), it tended to overestimate the concentration of Chla in Lake Taihu, Gehu and
Dongjiu (Table 2 and Figure 4(b)). The errors in Chla retrieval might have been partly

the result of using the value for aChla
 665 measured by Gons et al. (2005). The

value of aChla
   ( aph    used usually) was previously considered to be relatively
constant with an averaging approximately 0.016 m2·mg-1 (Banniste.Tt 1974; Gons et
al. 2002, 2005), which was used in many bio-optical models (Kiefer and Mitchell

1983). However, it is now recognized that aph
   can vary in response to changes in
packaging effect and pigment composition (Bricaud et al. 1995). The package effect is
wavelength-dependent and depends on the cell size, pigment content and the
physiological state of phytoplankton (Ma et al. 2006b). The effect of pigment
composition in natural waters has seldom been discussed due to the lack of detailed
pigment data measured (Suzuki et al. 1998). Although previous researches have
24

revealed that aph
   has an negative exponential relationship with Chla contents in
certain water types (Ma et al. 2006b; Pierson and Strombeck 2001; Xu et al. 2009),

Table 3 shows aph
 665 varies differently among different waters. Even in the same
waters but different seasons and years such as Lake Taihu and Lake IJsselmeer,

aph
 665 decreases exponentially corresponding to the increasing Chla. Therefore, it

seems that the assumption of a constant for aph
   would be a significant source of
uncertainty in models for Chla remote estimation.

For Lake Taihu, Gehu and Dongjiu, aph
 665 varies from 0.0106 to 0.6143
m2·mg-1, most (73/97) locates between 0.0400 and 0.0700 m2·mg-1 (Figure 12). The

average value of aph
 665 was 0.0686 m2·mg-1, and its variability was high, with an
SD of 0.022 m2·mg-1. The maximum value was found in Meiliang Bay, Lake Taihu
with low Chla concentration (0.13μg·L-1) but high aph  665 (0.0638m-1), perhaps the
other phytoplankton pigments, for example, Chlb and PC, contributes to the
absorption in part for which the cause remained unknown. In order to estimate Chla

correctly, the median value of aph
 665 with 0.0532 m2·mg-1 was found better for
the Gons algorithm, and Figure 13(a) showed a relatively lower RMSE =7.47 g/l.

apc
 620 was determined as apc  620 divided by the measured PC concentration,
while the absorption by PC ( apc  620 ) was derived from the absorption

measurements as aph  620     aph  665   (Simis et al. 2005a). Unlike aph
 665 ,

apc
 620 is affected not only by the packaging effect and pigment composition, but

also by the parameter of  . In the Simis algorithm, the average value apc
 620 that
0.007 m2·mg-1 for various lakes and reservoirs in Spain and The Netherlands (Simis et
25
al. 2007) and 0.0095 m2·mg-1 for Lake Loosdrecht (Simis et al. 2005a) were used to
calculate the PC concentration from remote sensing reflectance, respectively. The
former value was used more popular. However, it’s also not suitable for Lake Taihu
and its nearby waters due to the low PC concentrations in early spring, and the

accurate  is difficult to be acquired. Based on the new parameter  , apc
 620
showed larger variations from 0.0068 to 0.1399 m2·mg-1, and the average value is

0.0369 m2·mg-1 with SD = 0.0230 m2·mg-1. The apc
 620 value, averaged for all
samples stations, is good for this research with lower RMSE=0.76 g/l (Figure 13(b)).
[Section 3.6 may need to be switched with 3.5]
4.
Discussion
4.1. Temporal changes in the PC:Chla ratio
Chla present in all phytoplankton and is regarded as an indicator of total algal biomass,
while PC is not typically found in other algal classes except cyanobacteria and
possibly a useful indicator of cyanobacterial biomass (Ruiz-Verdu et al. 2008; Simis
et al. 2005a). Therefore, the PC:Chla ratio is a possible indicator of the cyanobacterial
share in total phytoplankton biomass (Simis et al. 2007). Although the total
phytoplankton abundance is increasing from Lake Taihu (average Chla: 9.02μg/L) to
Lake Gehu (average Chla: 36.38μg/L), cyanobacteria are not dominant in the
phytoplankton community with low PC:Chla ratio. During spring recruitment periods,
cyanobacteria coexist with green algae, diatoms, and flagellates, etc. in Lake Taihu,
26
Gehu and Dongjiu (Chen et al. 2003). However, cyanobacteria have a long history of
acquiring remarkable adaptations with nitrogen fixation and gas vesicles, and can
outcompete diatoms and green algae for light and nutrients (Guo 2007). Therefore,
with the increasing of the total phytoplankton, cyanobacteria shared more component
of phytoplankton biomass and the PC:Chla weight ratio also increased from 0.11 to
0.31. While in the summer and autumn that biomass increase and bloom formation,
cyanobacteria would be the dominant algae in Lake Taihu and the PC:Chla ratio could
reach an average value of 6.18 and varied between 1.46~40.73 (Ma et al. 2009). The
data collected over Loch Leven also showed a lower PC:Chla ratio with 0.175 at the
beginning of the clear water phase in April and a higher value with 1.90 while
cyanobacterial dominance in August (Hunter et al. 2010).
Previous research proposed the same stability of the PC:Chla ratio in different
waters may correlate with latitude and rely on differences in average solar irradiance
or temperature (Ruiz-Verdu et al. 2008). But in this research, Lake Taihu, Gehu and
Dongjiu are almost in the same latitude; therefore, there must be other reasons: 1)
Higher nutrient salts including phosphorus and nitrogen: previous research has
revealed that Lake Gehu has more nutrient than Lake Taihu. 2) Shallow water: its
average water depth is 1.57 m while Lake Taihu has an average depth with 2.67 m in
this survey. The shallow lakes are more easily affected by wind and the disturbance
induced by wave can significantly enhance the internal salts loading (Fan et al. 2004).
3) more time with higher temperature: Previous studies have suggested that increases
in surface temperatures of freshwater systems may lead to increased proliferation of
27
toxic cyanobacterial populations (Paerl and Huisman 2008; Wilhelm et al. 2011).
Although the field experiments in Lake Taihu and Gehu were in a same voyage, but
there were 16 days from April 23 to May 3, and Gehu was conducted in the last day
of May 3; perhaps while more than half months pass, the algae grow up especially
with a higher temperature about 20 ºC. It also can be concluded in Taihu Lake that a
similar trend was showed that the sites on May 2 had an average Chla with 38.08
μg/L in Zhushan bay, where has similar eutrophic state in history with Meiliang Bay
(Duan et al. 2009) but has an average Chla with 1.19μg/L conducted on April 23. The
spring recruitment of algae apparently increased soon and have a considerable impact
on dominance patterns.
4.2. Choose the right algorithm for Taihu Lake
[From the above results, for PC and Chla estimates, which algorithm do you use?]
5.
CONCLUSION
[You’ll need to show some regionally tuned algorithm, and imagery results [I’ll try to work on it
soon], to make some conclusion]
Table 3. Phytoplankton pigments specific absorption coefficients at 676 nm in
different waters
aph*(676)
Sampling time
Sites
Chla
(m2·mg-1) (μg·L-1)
(Month, Year)
28
Reference
10, 2004
06, 2007
10, 2008
04-05, 2010
04-05, 2010
11, 2007
10, 2007
03-10, 2003
04, 2003
06, 2003
08, 2003
09, 2003
Total, 2003
Lake Taihu
Lake Taihu, Gehu and
Dongjiu together
Lake Taihu
Shitoukoumen Reservoir
Lake Songhua
Lake Loosdrecht
Lake IJsselmeer
08, 2000
the Baltic Sea
1993–1996
the IJssel Lagoon
0.0222
0.0218
0.0098
0.0638
14.08
12.39
22.35
8.94
0.0535
13.07
This study
0.0210
0.0157
0.0107
0.0153
0.0203
0.0181
0.0165
0.0138
0.0172
0.0210
0.0240
0.0200
0.0146
0.0161
23.44
14.03
2.80
70.90
24.07
64.77
61.29
60.50
59.11
1.6-6.0
1.6-2.0
2.0-6.0
(Le et al. 2009a)
3–185
(Ma et al. 2006b)
(Duan et al. 2010)
(Xu et al. 2009)
(Simis et al. 2005a)
(Seppala et al. 2005)
(Gons et al. 2002, 2005)
ACKNOWLEDGEMENTS
The authors would like to thank Youzhuan Ding, Lin Zhou, Linlin Shang, Kai Xiao,
Yongchao Xing, Chunguang Lv, Cenlu Zhao, Cenwei Liu and Jiawang Rao for their
help with field-sample collection. Thanks also for the financial supports provided by
the Knowledge Innovation Program of the Chinese Academy of Sciences (No.
KZCX2-EW-QN308 and No.KZCX2-YW-QN311), the National Natural Science
Foundation of China (No.40801137 and No.40871168).
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