Bio-Optical Properties of the Inner Continental Shelf off Santos
Estuarine System, Southeastern Brazil, and their Implications for Ocean Color
Algorithm Performance
bjoce
Brazilian Journal of Oceanography
Braz. j. oceanogr.
1982-436X
1679-8759
Universidade de São Paulo, Instituto Oceanográfico
A classificação ótica de massas de água costeira auxilia na compreensão de processos
físicos e biogeoquímicos e permite otimizar algoritmos de cor do oceano. Neste
estudo, identificamos 3 classes óticas de águas na plataforma continental interna
adjacente à Baía de Santos (Brasil), com base na reflectância de sensoriamento remoto
observada durante outubro/2005 e março/2006. ANOVA indicou influência estuarina
crescente entre as classes 1 a 3, sendo que a última apresentou altos valores de
clorofila-a, nutrientes e coeficientes de absorção da luz. A
matéria orgânica dissolvida colorida (MODC) dominou a absorção da luz em todas as
classes, mostrando forte correlação com a salinidade em outubro/2005, sugerindo
influência da pluma do rio da Prata na região. Os resultados indicam dinâmica
bastante complexa da MODC na plataforma interna de Santos. O desempenho do algoritmo
global para clorofila-a (OC3), testado pelos dados radiométricos e
de clorofila-a
in situ, foi bem inferior em outubro/2005 comparado a março/2006.
Como não houve mudanças substanciais nos espectros de absorção pelo fitoplâncton
entre as duas épocas, os resultados mostram que as propriedades de absorção da luz
pela MODC devem ser consideradas prioritariamente na otimização de algoritmos
bio-óticos na região.
INTRODUCTION
Continental shelves under the direct influence of estuarine discharges display great
variability of ocean color (BUKATA et al., 1995),
which has been an important tools for the detection and quantification of dissolved and
particulate material from different sources (MOREL,
2006). Ocean color products may include, besides the widely used
chlorophyll-a concentration, indices of dissolved organic matter
(MANNINO et al., 2008) and parameters that
provide insights into phytoplankton community structure (e.g. BREWIN et al., 2011). Thus, bio-optical products from ocean color
databases, such as those provided by satellites, may improve biogeochemistry studies as
well as the management of water resources.
Ocean waters were optically classified more than 3 decades ago (MOREL; PRIEUR, 1977) into two main groups. The first of these,
referred to as Case 1, are those in which phytoplankton and their associated and
co-varying degradation products are the main optically active components present, which
thus cause the variability of ocean color. The remaining waters, named Case 2, are those
where inorganic particles and dissolved material govern changes in ocean color. Because
of this, retrieving information on phytoplankton abundance using bio-optical methods in
Case 2 waters is difficult and requires the development of regional algorithms (SATHYENDRANATH, 2000).
Ocean color, or remote sensing spectral reflectance (Rrs
), is determined primarily by two inherent optical properties (IOPs) - the
light absorption and backscattering coefficients. In turbid waters (or Case 2), the high
concentrations of colored dissolved and suspended materials tend to increase the
magnitude of both absorption and scattering coefficients. However, the resulting ocean
color depends on the combined spectral responses of the several substances present in
the water and their relative contribution, thus a number of very distinct optical
classes are present under the general Case 2 types. Usually, the main optically active
components in coastal waters are inorganic sediments, phytoplankton, detritus and
colored dissolved organic matter (CDOM). A number of studies have applied multivariate
approaches for the optical water mass classification, in order to contribute to the
understanding of biological and chemical processes (e.g. ARNONE et al., 2004). Although a number of semi-analytical models is
available (see discussion in BRICAUD et al.,
2012), the simpler approach of optical water type classification can optimize
ocean color algorithm performances (AURIN et al.,
2010), expanding and facilitating the applicability of remote sensing data.
Empirical algorithms (e.g. O'REILLY et al.,
1998a; O'REILLY et al., 1998b) provide an
indispensable computational tool for the monitoring of coastal waters, but because of
the optical complexity of these environments, their performance might be significantly
affected according to the presence of different optical components.
Our study was conducted in the continental shelf waters adjacent to the Santos estuarine
system, which encompasses two estuarine channels and the Santos Bay (Fig. 1), a small and semi-enclosed bay located in São
Paulo State, Brazil. Due to its shallow bathimetry and great tidal mixing (HARARI; CAMARGO, 2003), the plume formed in the bay
is a mixture of the continental runoff and oceanic water masses, composed mainly of
Coastal Water (CW), seasonally mixed with Tropical Water (TW) and eventual contributions
of the South Atlantic Central Water (SACW) (ANDUTTA et
al., 2006). All of the above mentioned water masses also have distinct
chemical and biological characteristics (AIDAR et al.,
1993; ANDUTTA et al., 2006). Thus it is
to be expected that their relative contributions be reflected in the optical properties
of the estuarine plume formed within Santos Bay, which can be identified as distinct
types and monitored by remote sensing spectral reflectances.
Fig. 1
Sampling grid for October 2005 and March 2006 on the inner continental
shelf off Santos estuary, Southeastern Brazil.
Some studies on the optical properties of the Santos estuary and Santos Bay have
discussed the role of spring tides in the resuspension of inorganic sediments and the
estuarine input of organic particles and CDOM during neap tides, whereas CDOM optical
properties are not good predictors of surface salinity (BUCCI et al., 2012). Other surveys have shown that summer phytoplankton
blooms inside the Santos estuarine complex may produce optical dominance of
phytoplankton and are modulated by tides (MOSER et al.,
2005; BUCCI et al., 2012), wind
patterns (MOSER et al., 2012) and fresh water
discharges.
Studies in coastal waters and waters influenced by estuaries, such as Blondeau-Patissier et al. (2009) in estuarine and
coastal waters of the Great Barrier Reef (Australia) and Aurin et al. (2010) in Long Island Sound estuary, have indeed demonstrated
that distinct optical domains resulting from the mixture of waters in the estuary-ocean
interface can be identified by their optical properties.
This study focused on the identification of the different optical domains (or optical
types) in the coastal waters adjacent to the Santos estuarine system. To understand the
physical, chemical and biological influence of the Santos estuary on the adjacent shelf
waters, we have classified the optical water types on the basis of the reflectance
spectra of the sea surface measured in two periods (October 2005 and March 2006). The
optical types were also characterized according to absorption coefficients of CDOM,
phytoplankton and "detritus" (or non-algal particles that include inorganic sediments).
Additionally, the performance of the global algorithm OC3, the standard algorithm of the
Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, was evaluated to estimate
chlorophyll-a concentration from reflectance ratios.
MATERIAL AND METHODS
Two oceanographic campaigns were conducted on the inner continental shelf adjacent to
Santos Bay in October 2005 and March 2006, as part of the ECOSAN research project
"The influence of Santos estuarine complex on the adjacent continental shelf
ecosystem", conducted by the Instituto Oceanográfico da Universidade de São
Paulo on board the R/V Prof. W. Besnard. Each campaign was based on a grid of 40
oceanographic stations distributed along eight sections originating from the same point
in the center of the entrance of Santos Bay (Fig.
1). Temperature and salinity were measured at all the stations using a
Falmouth CTD profiler and the data were used to obtain temperature-salinity (TS)
diagrams, following the thermohaline index proposed by Castro Filho et al. (1987).
Eight-day composites of sea surface temperature (SST) with 4-km spatial resolution for
the period of the sampling (12 to 15 October 2005 and 23 to 26 March 2006) were derived
from the MODIS sensor aboard the Aqua satellite.
Surface water samples were collected at all the stations using 9 L Niskin bottles for
the analyses of inorganic nutrients and chlorophyll-a concentrations
(hereafter referred as Chl), as well as measurements of light
absorption coefficients by CDOM (acdom
), detritus (adet
) and phytoplankton (aphy
).
Nutrient concentrations were determined spectrophotometrically from the filtered water,
in accordance with the methods described in Aminot;
Chaussepied (1983) for ammonium, and in Grasshoff et al. (1983) for phosphate and silicate.
Chlorophyll-a concentration (Chl, mg
m-3) was determined by spectrophotometric methods, in accordance with
SCOR-UNESCO (1966), from samples retained in
AP-40 Millipore filters (SALDANHA-CORRÊA et al.,
2004) and extracted in 90% acetone. Independently, samples for
Chl were also determined using a calibrated Turner Designs 10-005R
fluorometer, equipped with the non-acidification filter set (WELSCHMEYER, 1994). Samples of 500 mL were concentrated on GF/F
filters that were stored in liquid nitrogen immediately after filtration. GF/F filters
were extracted at -10°C or below, for at least 24 h, in pre-cooled 90% acetone: DMSO
solution (6:4 by volume; SHOAF; LIUM, 1976). The
values of Chl derived from both methods were averaged or chosen
according to a set of criteria (not shown). The differences between spectrophotometric
and fluorometric Chl were, in general, below 15%.
Volumes of 300 to 2000 mL were concentrated onto GF/F filters for the measurement of
light absorption coefficients. Filters were immediately preserved in liquid nitrogen and
stored until analysis. Measurements followed the method of Tassan; Ferrari (1995) using a Hitachi U3010 dual-beam
spectrophotometer and the path-length amplification correction described in Tassan et al. (2000). Sample and blank filters were
scanned against air from 750 to 350 nm at both the entrance and the exit of an
integrating sphere. All the filters were then treated with a few drops of 0.5% NaClO for
10 to 15 min and then carefully washed with the 0.2 μm filtered seawater to remove
phytoplankton pigments. Measurements were repeated as above, and the spectral absorption
of phytoplankton was computed as the difference between the scan before (total
particulate) and after ("detritus") bleaching, correcting for the volume filtered and
clearance area. Values between 740 and 750 nm were considered as absorption by detritus
(TASSAN et al., 2000). Measurements of the
detritus spectral absorption were fitted to an exponential decay with wavelength to
yield a value for magnitude at 443 nm (adet
(443), m-1, hereafter referred as adet
) and spectral slope (Sdet
, nm-1). Values between 740 and 750 nm were considered as absorption
by detritus only (TASSAN et al., 2000).
Filtrates from the particulate absorption samples were used for determinations of CDOM
absorption coefficients. Samples were kept in the dark at 4°C until analysis using a 10
cm quartz cuvette and scanning the samples in spectrophotometry against air between 300
and 750 nm. Freshly produced Milli-Q water was used to zero the instrument. The same
fitting procedure for detritus absorption spectra was applied only to the 350-600 nm
spectral absorption range (following BABIN et al.,
2003) for deriving the CDOM absorption parameters (acdom
(443), m-1, hereafter referred as acdom
, and Scdom
, nm-1).
Because of the very similar spectral shape of both CDOM and detritus (i.e., exponential
decay of absorption values with increasing wavelength), most remote sensing methods and
models combine both components into a single variable named colored detrital matter
(CDM); thus, CDM absorption coefficient (acdm
, m-1) is simply the sum of acdom
and adet
.
Phytoplankton absorption spectra were parameterized following Ciotti et al. (2002), modified by Ciotti; Bricaud (2006). Resulting values of the parameterization are a
magnitude (a) corresponding to the average value of phytoplankton
absorption between 400 and 700 nm and the size parameter Sphy
, which is a value constrained to vary between 0 and 1 and to specify the
relative contributions of microplankton and picoplankton to the phytoplankton absorption
spectra, independent of Chl.
Remote Sensing Reflectance
The spectral remote-sensing reflectance was computed from the total upwelling
radiance of surface water, Lt(λ,θ,φ,0), acquired from 400
to 1100 nm (3 nm of resolution) with a spectroradiometer Spectrom SE 590. The
acquisition, correction and processing of the radiometric measurements above water
followed the SeaWiFS validation protocol (MUELLER;
AUSTIN, 1995). First, the effects of skylight reflection and residual sun
glint were minimized with a polarizer filter coupled to the sensor. Measurements of
the total upwelling radiance of water surface
(Lt(λ,θ,φ,0)), reflected sky radiance
(Lsky(λ,θ,φ,π)), and upwelling radiance of
reference plaque (Lp(λ,θ,φ)) were taken sequentially at
least 3 times at each daylight station.
Remote sensing reflectance (Rrs
), or the color of the ocean, is defined as:
where Lw
is the upwelling radiance of surface water measured in a zenithal and
azimuthal angle, θ = 45° and φ = 135°,
respectively, at wavelength λ. Ed
is the downwelling irradiance in all downward directions.
Total upwelling radiance of water (Lt
) is the sum of the upwelling radiance of water (Lw
) containing dissolved and particulate materials and of the sky radiance
(Lsky
) resulting from physical perturbation of the sea surface, mainly the sun
glitter and the presence of bubbles (FOUGNIE et al.,
1999; STEFFEN, 1996; MUELLER; AUSTIN, 1995). In order to compute
Lw(λ,θ,φ) the following correction was applied:
where ρ = 0.022 is the Fresnel reflectance for correction of the
reflection and refraction effects that occur when Ed
and Lu
propagate through a flat surface (MUELLER;
AUSTIN, 1995). The ρ = 0.022 is the mean value for the
period of day at which the measurements were taken, as determined by Kirk's (1994) equations.
Rrs
values were then computed by referencing the upwelling radiance of water
(Lw
) and the upwelling radiance of the reference plaque (Lp
) over all angles (π):
where k is the reflectance of the reference plaque, ideally close to
1 and spectrally flat.
Rrs
spectra between 380 and 899 nm were generated for a total of 49 stations
(26 in October, 21 in March). All values were offset to those computed for 869 nm
(average between 866 and 872 nm) in order to remove any residual specular effects
(DESCHAMPS et al., 2004). To test the ocean
color algorithm performances, the Rrs
spectra were then grouped into bands to represent the same wavelength
channels of the MODIS/Aqua ocean color sensor bands
(http://modis.gsfc.nasa.gov/). The arithmetic means of intervals
of 15 nm, centered at 412 and 869 nm, and 10 nm, centered at 443, 488, 531, 551, 667,
678 and 748 nm were computed.
Statistical Analysis
The water optical type classification was applied to the whole dataset
(n=49) using a Hierarchical Clustering Analysis (HCA) analysis
with the Rrs
spectra (between 407 and 752 nm) as input vector objects. The spectra were
first normalized to Rrs
(551) to minimize possible effects of distinct magnitudes enhancing the
spectrum shape, since variations in the Rrs
at 551 nm are small in oceanic waters (TORRECILLA et al., 2011; AURIN et al.,
2010). The dissimilarity matrix degree was calculated by computing the
Euclidean distance (CLARKE; WARWICK, 2001)
applied to matrix of Rrs
spectra.
The cluster tree, cophenetic matrix and indexes were computed using a complete
linkage algorithm based on the dissimilarity matrix. The cutoff value chosen for the
groups was the inflection point of linkage distances between stations and the
clusters below this point are identified as classes of optical water type (following
SALVADOR; CHAN, 2004). A Multidimensional
Scaling (MDS) ordination analysis was applied on the same resemblance matrix, for
visualization of the clusters obtained in the HCA using the same cutoff value for the
cluster tree.
Significant differences in physical and chemical properties (temperature, salinity
and nutrients) and light absorption parameters (phytoplankton, detritus and CDOM)
were tested between optical water types using a one-way analysis of variance (ANOVA)
with critical p < 0.05 and a
posteriori Tukey test for unequal n. Relationships
between variables, periods and parameters were investigated by linear and non-linear
regression analyses.
Ocean Color Algorithm Performance
Chl concentration (mg m-3) was estimated using the ocean
color algorithm OC3 (O'REILLY, 2000) and the
simulated MODIS/Aqua spectral bands from the in situ reflectance
values. The OC3 performance was tested with linear regression analyses between
estimated and measured in situ Chl (log) for 27
stations in October 2005 and 22 stations in March 2006, as well as for the whole
dataset (n=49).
RESULTS
Optical Water Types
The HCA analysis yielded a cluster tree (Fig.
2) and a cophenetic index of 0.8876, which indicated that the complete linkage
algorithm was appropriate for our data. The ordination of the 49 oceanographic
stations by the MDS, which also used Rrs
spectra as input, obtained a stress value of 0.02, indicating a good fit of
the metric to the dataset. The cut-off value of the cluster tree and MDS was 3.09
(see Figure 3). The HCA and MDS analysis
grouped the Rrs
spectra into three optical water types, arbitrarily named as classes 1, 2
and 3 (Figs 2 and 4). According to the cutoff
value, the Rrs
spectral for one station was considered as an outlier and was not included
in any of the three classes. This outlier was closer to the spectra of class 3, so it
was grouped in this class. Class 1 was composed of only 3 stations, located in the
outermost part of the sampling grid and the furthest from Santos Bay. Class 3
included the stations closest to Santos Bay, and class 2 those located in
intermediate regions. The cluster tree revealed that class 1 and 2 are closer to each
other and equally distant from class 3. Rrs
spectra normalized by Rrs
(551) of each optical water type are shown in Figure 5.
Fig. 2
Cluster tree obtained by HCA analysis for the dataset comprising 49
stations from both sampling campaigns, using Rrs
spectra between 407 and 752 nm as input. The suffix
o and m refers to station numbers
during October 2005 and March 2006, respectively. Squares are for class 1,
dots for class 2 and triangles for class 3.
Fig. 3
Linkage distances between clusters obtained in the HCA (see Figure 2). The dashed line represents the
limit to cut-off the cluster located at the inflection point of the
curve.
Fig. 4
Ordination of the stations by MDS for the 49 stations, using the
Rrs
spectra between 407 and 752 nm. The suffix o and
m after the station numbers refers to October 2005 and
March 2006, respectively. The classes are represented by symbols (squares
for class 1, dots for class 2 and triangles for class 3). The circles around
the dots represent the limits of groups of optical water types.
Fig. 5
Rrs(λ) spectra normalized by
Rrs (551) for some representative stations
of class 1 (dash-dotted line), class 2 (solid line) and class 3 (dashed
line) derived from the cluster and ordering analyses (see Figures 2 to 4).
Significant differences between shapes and magnitudes of the Rrs
spectra corresponding to each class were conspicuous and revealed expected
trends. Class 1 presented a typical spectral shape of open ocean waters, with higher
reflectance in the blue region decreasing exponentially with wavelength, while class
3 exhibited typical spectral shapes for turbid waters. Although class 2 showed
intermediate normalized Rrs
values in the blue region, it was the class with the highest
Rrs
values in red wavelengths.
Physical and Biochemical Properties in the Classes
Significant differences (p < 0.05) were found by ANOVA and Tukey tests applied to
most of the physical, chemical and biological variables from the optical water types
discriminated by the HCA analyses (Table 1).
An exception was noted for temperature and concentration of ammonium, which were
similar (or equally variable) among all classes. The Tukey tests showed that class 1
was similar to class 2 for the remaining variables, except for the concentration of
phosphate, which was similar for classes 2 and 3.
As expected, salinity was lower in class 3 and showed high variability (Fig. 6b). Due to the estuarine influence, class 3
presented higher concentrations and variability of silicate and phosphate
concentrations than classes 1 and 2 (Fig. 6f).
Indeed, the mean concentration of silicate in class 3 was twice as high as in classes
1 and 2. The concentration of phosphate in class 1 was very low, with mean values
close to the limit of detection (assumed here as zero), while the average
concentration values in classes 2 and 3 were 0.37 µM and 0.50 µM, respectively (Fig. 6e). Although ANOVA showed no differences for
concentration of ammonium among classes, the average value of class 3 was twice
higher than those of classes 1 and 2 (Fig. 6d).
Chl concentration (log) mean values and
variability were higher (Table 1) in class 3
than in classes 1 and 2 (Fig. 6c).
Light Absorption Coefficients by CDOM, Phytoplankton and Detritus in the
Classes
ANOVA and Tukey tests that compared light absorption parameters
(acdom
, aphy
, adet
, at
, Scdom
, Sphy
and Sdet
) between each optical water type (Table
2) showed that classes 1 and 2 are similar for acdom
and aphy,
presenting mean values about one order of magnitude lower than class 3 for
both parameters. The tests also indicated significant differences for all absorption
parameters, except for adet
and Sdet
.
Table 1.
Average values for each class of water and results of one-way ANOVA for
critical p < 0.05, and Tukey HSD for unequal n between
discriminated optical water types for comparisons between physical and
biochemical variables. Significant values (p < 0.05) are
shown in bold fonts. Dataset for whole sampling period.
Variable
Class 1
Class 2
Class 3
Differences between classes
F calculated
p value
Salinity
35.10
34.23
32.26
3 < 1 = 2
12.560
0.000
Temperature (°C)
27.61
25.28
24.28
1 = 2 = 3
1.993
0.140
Chl - log (mg m-3)
-0.47
-0.43
0.24
3 > 1 = 2
13.852
0.000
Silicate (μM)
2.66
2.50
5.21
3 > 1 = 2
5.026
0.011
Ammonium (μM)
0.13
0.18
0.37
1 = 2 = 3
0.722
0.492
Phosphate (μM)
0.00
0.37
0.50
1 < 2 = 3
7.052
0.002
Table 2.
Average values of the variables for each class of water and results of
one-way ANOVA, for critical p < 0.05, and Tukey HSD for unequal
n between classes of optical water type for light
absorption parameters. Significant values are in
bold.
Parameter
Class 1
Class 2
Class 3
Difference between the classes
F calculated
p value
acdom (m-1)
0.017
0.050
0.188
3 > 1 = 2
16.970
0.000
aphy (m-1)
0.008
0.009
0.040
3 > 1 = 2
4.441
0.018
adet (m-1)
0.007
0.005
0.053
1 = 2 = 3
2.079
0.137
Scdom
0.023
0.019
0.017
1 > 2 = 3
9.597
0.000
Sphy
0.835
0.836
0.527
3 < 1 = 2
20.789
0.000
Sdet
0.011
0.011
0.012
1 = 2 = 3
0.824
0.445
Fig. 6
Box plots for: (a) salinity; (b) temperature; (c) Chl
a (log); (d) silicate
(log); (e) ammonium (log); and (f)
phosphate (log), for the optical water type classes 1, 2
and 3. In the box plots, the median (line inside the box), lower quartile
and upper quartile (box), minimum and maximum values (whiskers) and outliers
(cross) are represented.
The magnitudes of light absorption parameters (Fig.
7) revealed no significant differences for adet
, although the mean value in class 3 was one order of magnitude larger than
in classes 1 and 2. Class 3 also showed high variability for acdom
, aphy
and adet
(Fig. 7). The magnitude of CDOM
absorption was the most variable parameter among optical types, with higher values in
class 3, as expected. Similarly to what was observed for the magnitudes, the spectral
shapes of light absorption by CDOM and phytoplankton, represented by
Scdom
and Sphy,
respectively, were significantly different according to the ANOVA tests.
Higher values of both Scdom
and Sphy
were found in class 1 than in classes 2 and 3 (Table 2) with class 3 presenting less variability in
Scdom
than classes 1 and 2 (Fig. 8).
According to ANOVA there was no difference in Sdet
as between classes, and the Tukey test indicated similar mean values.
Despite the ANOVA results, the average values of Sdet
were observed to be similar, but class 2 presented higher variability than
other classes (Fig. 8b).
Bio-optical Water Types and Chl Algorithm
Performances Between Campaigns
The TS diagram (Fig. 9) indicates the presence
of estuarine waters over the inner continental shelf and dominance of the Coastal
Water (CW) in October 2005. A large range of salinity was observed in this period:
waters with very low salinities (S < 33.5) were present on the inner and medium
shelf, suggesting not only the influence of Santos estuary but also of waters from
the Rio de la Plata (MÖLLER Jr. et al., 2008;
CASTRO et al., 2008). Outer stations
presented higher salinities, as a result of TW influence. SST images showed the
tongue of cold water (T < 20°C) that originates in higher latitudes
and reached the study area in October 2005 (Fig.
10a).
The main feature observed in the TS diagram (Fig.
9) in March 2006 were the high salinities (S > 33.1), indicating the
prevalence of oceanic water under TW influence, mixed with CW, with high temperatures
(maximum of 29°C) and the confinement of the estuarine plume within
Santos Bay. Further, the SACW signal was detected by low temperatures (<
20°C) and salinities between 35 and 36.4 (CASTRO FILHO et al., 1987). There was no evidence of La Plata
River water influence during that period. In March 2006, the SST was more homogeneous
and above 20°C for the whole southeastern continental shelf (Fig. 10b).
Fig. 7
Box-plots for light absorption magnitudes (log) by CDOM
(acdom
), detritus (adet
) and phytoplankton (aphy
), for the optical water classes 1, 2 and 3. In the box plots, the
median (line inside the box), lower quartile and upper quartile (box),
minimum and maximum values (whiskers) and outliers (cross) are
represented.
Fig. 8
Box-plots for spectral slope of CDOM (a), detritus (b) and phytoplankton
(c), for the optical water classes 1, 2 and 3. In the box plots, the median
(line inside the box), lower quartile and upper quartile (box), minimum and
maximum values (whiskers) and outliers (cross) are represented.
Fig. 9
Temperature-Salinity (TS) diagram for October 2005 (crosses) and March
2006 (dots) cruises, with density lines in the background. The water masses
indicated in the figure (Coastal Water - CW, Tropical Water - TW and South
Atlantic Central Water - SACW) are according to the thermohaline index
proposed by Castro Filho et al.
(1987).
The results of one-way ANOVA and the Tukey test (p < 0.05) between the periods of
October 2005 and March 2006 are summarized in Table
3. Significant differences were observed between periods for salinity,
temperature and nutrients. The unique light absorption parameter that presented
significant differences between periods was the acdom
, with higher values in October 2005 than in March 2006.
The results of regression analysis between the Chl estimated by
global empirical algorithm OC3 and Chl measured in
situ are presented in Table 4.
The March 2006 dataset obtained better adjustments (n=22, r2=0.876) and
lower mean square errors (MSEs) than October 2005 (n=27, r2=0.682) and
also for the whole dataset (n=49, r2=0.683).
Table 3.
Average values of the variables for each sampling period and results of
one-way ANOVA, for critical p < 0.05, and Tukey HSD for unequal
n between sampling period for physical, biological,
chemical and light absorption parameters. The significant
values (p < 0.05) are in bold.
Parameter
Oct, 2005
Mar, 2006
F calculated
p value
Salinity
31.72
34.48
90.046
0.000
Temperature (°C)
22.10
28.03
1297.282
0.000
Chl (mg m-3)
2.26
2.66
0.119
0.732
Silicate (μM)
5.38
3.08
8.400
0.006
Ammonium (μM)
0.46
0.12
5.058
0.029
Phosphate (μM)
0.53
0.31
9.300
0.004
acdom (m-1)
0.17
0.10
6.067
0.018
aphy (m-1)
0.03
0.03
0.092
0.763
adet (m-1)
0.04
0.03
0.121
0.730
at (m-1)
0.27
0.21
0.890
0.350
Scdom
0.02
0.02
0.002
0.966
Sphy
0.62
0.63
0.052
0.821
Sdet
0.01
0.01
1.994
0.165
Table 4.
Correlation (r) and determination (r2) coefficient, p-value
(p), mean square error (MSE) between
Chl estimated by OC3 and in situ (log),
for the two periods and for the whole dataset (n=49).
Algorithm
n
r
r2
p
MSE
OC3
49
0.827
0.683
0.000
0.0368
OC3 - October, 2005
27
0.826
0.682
0.000
0.0255
OC3 - March, 2006
22
0.936
0.876
0.000
0.0165
The relationship between salinity and acdom
for October 2005 and March 2006 is illustrated by the scatter plot with
linear adjustment in Figure 11. Salinity
and acdom
were better correlated in October 2005 (r=-0.8542) than in March 2006
(r=-0.7141).
The scatterplot and trend lines for acdom
and Scdom
are presented in Figure 12. The
inclination of trendlines shows that the relationships are different for each period.
The relationship between acdm
(acdom
plus adet
) and aphy,
for each period (Fig. 13) shows that
acdm
appears to co-vary more linearly with aphy
in March 2006 (see slope of 1.00 and r= 0.8872). In October 2005, the same
trendline was less robust (r=0.6188) and non-linear.
Fig. 10
Sea surface temperature (SST) 8-day average compositions derived from
MODIS/Aqua sensor, with a 4 km spatial resolution: 12-16 October, 2005 (a)
and 22- 29 March, 2006 (b).
Fig. 11
Scatter plot between acdom
(m-1) and salinity for October 2005 (dots) and March
2006 (circles), and their respective linear adjustments, for n=47.
Fig. 12
Scatter plot between acdom
(m-1) and Scdom
(nm-1) (both in log scale) for October
2005 (dots) and March 2006 (circles), and their respective linear
adjustments, for n=47.
Fig. 13
Scatter plot between acdm
(m-1) and aphy
(m-1) (both in log scale) for October
2005 (dots) and March 2006 (circles), and their respective linear
adjustments, for n=47.
DISCUSSION
Optical and Biochemical Properties of the
Optical Water Types
The optical characterization of coastal water masses is important for interpreting
and optimizing ocean color algorithms (ARNONE et al.,
2004; AURIN et al., 2010). Indeed,
operational empirical ocean color algorithms were designed only for Case 1 waters and
are not expected to work well outside this domain. Optical properties of the ocean
are sensitive to their physical and biogeochemical characteristics and, complementary
to temperature and salinity ranges, it is possible to classify a water mass by
identifying which component controls the total absorption coefficient (LEE, 2006).
A number of studies have presented the optical classification of water masses using
multivariate statistical analyses (e.g. AURIN et al.,
2010; TORRECILLA et al., 2011; BLONDEAU-PATISSIER et al., 2009). Similarly to
what was found by Aurin et al. (2010), who
used cluster analyses of IOPs in the estuarine region of Long Island Sound, our study
identified three classes of optical water types in the adjacent waters off Santos Bay
using HCA and MDS analyses. In the Long Island study, the variability of IOPs was
explained by the local effects of riverine suspended sediment and high nutrient
loading, with two optical domains dominated by phytoplankton absorption (of different
proportions) that were directly influenced by the river plume, and a third domain,
further from estuarine influences, dominated by colored detrital matter (CDM).
Our study distinguished 3 optical types (Fig.
5) in the study region. Class 1 represented clear waters, resembling
Rrs
spectra from Case 1 waters (MOREL; PRIEUR,
1977). The spectra for class 2 exhibit intermediate characteristics of
classes 1 and 3, but their biochemical and absorption variables tended to be similar
to those of class 1 for most parameters and variables, except for
Scdom
and phosphate concentration. The class 3 waters were closest to the
entrance of Santos Bay and exhibited the typical Rrs
spectra of coastal waters influenced by estuarine waters with low
reflectance values in the blue region, owing to high CDOM, detritus and phytoplankton
absorption. The minimum Rrs
around 443 nm suggests considerable absorption by
chlorophyll-a. Above 551 nm, Rrs
increases in class 3, probably due to backscattering from suspended
material.
The mixing of waters from the Santos estuary with continental shelf waters was
noteworthy in class 3, which showed highly variable salinity and high nutrient
concentrations associated with the lower salinities. The mixing processes do not
significantly affect the intermediary class 2 waters and even less those of class 1.
For class 1, there was a predominance of the oligotrophic tropical water mass (AIDAR et al., 1993; GIANESELLA; SALDANHA-CORRÊA, 2008) with average nutrient
concentrations about half of those for classes 2 and 3.
Although Moser et al. (2005) ascertained that
the Santos and São Vicente estuaries exported organic and inorganic matter, ammonium
and chlorophyll-a to the bay, especially during the rainy season,
our study suggests that this enrichment is clearly restricted to Santos Bay. Average
Chl concentration in class 3 waters (3.40 mg m-3) was
about an order of magnitude higher than the means of classes 1 and 2 (0.35 mg
m-3 and 0.39 mg m-3, respectively), which presented
Chl concentrations similar to those of other regions of the
southeastern Brazilian continental shelf, far from estuarine contributions, such as
in the Ubatuba and São Sebastião coastal areas (AIDAR
et al., 1993; GIANESELLA; SALDANHA-CORRÊA,
2003; SALDANHA-CORRÊA; GIANESELLA,
2008).
CDOM rather than phytoplankton was the dominant component for the light absorption in
all classes (more clearly so in classes 2 and 3), suggesting the input of dissolved
organic matter from the estuarine system into Santos Bay (MOSER et al., 2005). While the magnitude of the spectral
absorption by CDOM is related to the concentration of CDOM, the spectral slope gives
information about its source and composition, including the ratio of humic to fulvic
acids (TWARDOWSKI et al., 2004; CARDER et al., 1989). The spectral slope for
fulvic acid tends to be greater than that for humic acids (CARDER et al., 1989). An inverse relationship between
acdom
and Scdom
was observed in all the three classes, in agreement with that described by
other authors (e.g. CARDER et al., 1989; TWARDOWSKI et al., 2004, BRICAUD et al., 2012).
Twardowski; Donaghay (2002) suggested that
the high values of Scdom
observed in oceanic waters could be related to photobleaching processes
that result in a shift of CDOM absorption to shorter wavelengths, thus increasing
Scdom
values. Scdom
observed on the inner continental shelf off Santos varied from 0.011 to
0.028 nm-1, which is in the same range of values as that obtained by Ciotti; Bricaud (2006) in waters of the
continental shelf located further north (from 0.008 to 0.028 nm-1), with
no direct influence of the Santos estuarine complex. On average, Scdom
was 0.019 and 0.017 nm-1, respectively, for classes 2 and 3,
which were similar to those obtained by Ciotti;
Bricaud (2006) but slightly higher than the common assumed value for CDOM
in coastal waters (0.015 nm-1; see BABIN
et al., 2003).
In most of the oceans, CDOM is the major light absorption component competing with
phytoplankton (SIEGEL et al., 2002). Keith et al. (2002) studied the effects of CDOM
for phytoplankton in the coastal waters of Rhode Island, and verified that in waters
with high absorption by CDOM and Scdom
< 0.020 nm-1, the phytoplankton requires accessory
photosynthetic pigments at longer wavelengths (532 nm) to collect sufficient light
energy for photosynthesis. The composition of accessory pigments as well as
phytoplankton self-shading (the pigment packaging described by DUYSENS, 1956) controls phytoplankton light absorption (see BRICAUD et al., 1995). Class 3 presented values
of aphy
one order of magnitude greater than classes 1 and 2, as was to be expected
by virtue of the high Chl and nutrient concentrations in the
estuarine system. The range of aphy
values was consistent with those observed by Bricaud et al. (2010) for open
ocean waters in the Southeastern Pacific (0.0008-0.08 m-1).
Both taxonomic composition and the cell size of phytoplankton drive the spectral
shape of aphy
(CIOTTI et al., 2002; BRICAUD et al., 2004). One of the parameters
proposed to describe the spectral shape of aphy
is the size parameter Sphy
(CIOTTI; BRICAUD 2006), that
indicates primarily the dominant size of phytoplankton and during parameterizations
is forced to vary from 0 to 1, indicating dominance by large and small cells in a
continuum (CIOTTI et al., 2002). Class 1 and 2
waters were similar in terms of Sphy
, generally dominated by small cells (high Sphy
values), while the size parameter in class 3 suggested dominance by large
cells (lower Sphy
values), but also high variability in cell size. Differences in the
dominant cell size are a consequence of differences in nutrient availability among
water masses (YENTSCH; PHINNEY, 1989). As
discussed before, Santos and São Vicente channels promote an enrichment of the Santos
Bay waters. Moser et al. (2012) showed that
the microphytoplankton community in Santos Bay changes rapidly in response to wind
speed and direction, increase of precipitation or estuarine discharges, as well as
changes in tides. These fluctuations allow many species to coexist and a specific
phytoplankton group may ocassionally dominate. However, this high diversity appears
to co-vary strongly with Chl, independently of the study period, and
the relationships between aphy
and Chl are fairly robust.
The relative importance of non-algal particles (detritus plus inorganic sediments -
adet
) was one order of magnitude higher in class 3, probably due to the input of
inorganic material from the Santos estuarine channel into Santos bay, while the
contribution of non-algal particles in the classes 1 and 2 waters for total
absorption was lower and equivalent to that of phytoplankton. The spectral shape of
non-algal particles, Sdet
, resembled that of CDOM absorption (exponential decay with wavelength), but
Sdet
in the ocean tend to vary little in general (BRICAUD et al., 1998; BABIN et
al., 2003). The same was observed here, and Sdet
values are generally at the lower limits found in coastal waters,
indicating the predominance of organic particles (BUKATA, 1995).
Optical Water Type Variability Between the two
Periods of Time
A seasonal variability of the thermohaline properties of water masses is a
characteristic feature of the continental shelf off São Paulo state (AIDAR et al., 1993; CASTRO et al., 2008; CASTRO;
MIRANDA, 1998). This seasonality was observed in the present study in
association with the changes in the optical water classes found in both sampling
campaigns. In October 2005, the inner continental shelf was dominated by CW (Fig. 9) with low salinities (S < 34.9, CASTRO FILHO, 1987), driven by continental
run-off (CASTRO; MIRANDA, 1998). Nonetheless,
cold and low salinity waters from other sources were also present on the inner shelf
during the winter months. Coastal waters formed under the influence of Rio de la
Plata plume can reach this latitude (~25°S) during winter (CAMPOS et al., 1996; MÖLLER Jr. et al., 2008; PIOLA et
al., 2008; CASTRO et al., 2008).
This process brings suspended material and CDOM to upper layers, which changes the
optical properties of coastal waters, affecting the estimation of
chlorophyll-a by satellite ocean color data (PIOLA et al., 2008). Consequently, Chl
concentration in the region under the influence of the Rio de la Plata is
highly overestimated by satellite data (PIOLA et al.,
2008; GARCIA et al., 2005; 2006), and a relation between salinity and
satellite-derived Chl is to be expected, as shown by Piola et al. (2008).
Class 1 waters, which are associated with low nutrients and dissolved and particulate
matter, were absent in October 2005. Class 2 waters occurred only at 5 stations
located in the outermost area of the sampling grid, near the 50 m isobath, while
class 3 waters occurred at most of the stations sampled in the intermediate area and
close to Santos Bay. Thus, class 3 waters in fact exemplify a complex mixture of
those from the local estuary with coastal water under La Plata influence.
These temporal differences in the distribution of the classes on the inner and medium
continental shelf are consistent with Castro et al.
(2008), who showed that the sectors of the continental shelf on the
northern coast of São Paulo state present a seasonal dynamic. According to the
authors, in winter the inner continental shelf, located between the coast and the
deep thermal front, is the widest, reaching as far out as 40-80 km from the coast,
between the 50 and 70 m isobaths. During the summer, when the intrusion of the TW
carried by the Brazil Current is intensified, the inner continental shelf gets
narrower, extending seaward for 10-30 km, between the 20 and 40 m isobaths.
During March 2006, as seen in the TS diagram, the surface of the inner continental
shelf was dominated by warm waters (> 26ºC) of high salinity (S > 33.1) under
the more direct influence of TW. There was apparently no contribution of the La Plata
estuary in this period. The class 1 waters were observed in the outermost part of the
grid, the area occupied by class 2 was fairly reduced and class 3 waters were
confined to the entrance of Santos Bay.
This pattern of water mass dynamics is related to the wind regime. The study region
is located in the western portion of the anticyclonic subtropical gyre, associated
with the South Atlantic Subtropical High, which presents seasonal oscillations. In
summer east-northeasterly winds prevail in the coastal zone between 15º and 35ºS
favoring the approach of the Brazil Current to the medium and inner shelf. In winter,
these winds are confined to the 20-25ºS, and TW is positioned offshore (CASTRO et al., 2006). The greater incidence of
frontal systems in this period, characterized by south-southeasterly winds, results
in water mixing events and thermocline disruption.
Indeed, the ANOVA analyses comparing the sampling periods showed
acdom
as the only significantly different bio-optical parameter. An inverse
relationship between acdom
and salinity was more robust during October 2005 than in March 2006.
Despite the direct comparison between these relationships being challenged by the
distinct salinity gradients, it seems that the Santos estuarine complex plume
presents higher acdom
for a given salinity than does the coastal water under La Plata influence
(Fig. 11). The slopes of both linear
regressions are also different (-0.077 and -0.121 for October and March,
respectively); thus, the decrease of CDOM absorption due to the increase in salinity
appears more intense in the plume of the Santos estuarine complex.
The inverse relationship between acdom
and salinity is a common CDOM feature as rivers are important sources of
CDOM (e.g. D'AS; DIMARCO et al., 2009; D'AS; MILLER, 2003). The decrease in CDOM that
accompanies the increase in salinity is a primary response to the mixing of estuarine
waters with the low CDOM open ocean waters (e.g. KEITH et al., 2002). However, processes that form or destroy CDOM also
modify it. For instance, CDOM can be produced locally during phytoplankton blooms
(NELSON et al., 1998; BRICAUD et al., 1981) or be consumed by bacteria and
phytoplankton (CARLSON; DUCKLOW, 1996) or
photo-oxidized by solar irradiance (NELSON et al.,
1998; BRICAUD et al., 1981). On
large temporal scales in the ocean, both acdom
and Scdom
illustrated some of these processes regionally (BRICAUD et al., 2012). It is interesting to note the very
distinct relationships of Scdom
versus salinity found during both sampling periods, which also suggests
CDOM as a more reliable descriptor of the La Plata plume influence. The
acdm
and aphy
were more strongly and linearly correlated in March 2006, suggesting
covariance of the components, where CDM is produced locally by phytoplankton. The
same linear correlation was not observed in October 2005, reinforcing the fact that
the presence of the suspended and dissolved material originates in remote
regions.
The analyses of OC3 performances using in situ
Chl and the simulated R
rs MODIS/Aqua bands showed unsatisfactory results for October 2005
(r2=0.682), while in March 2006, when the influence of CW and
consequently of CDOM on the inner continental shelf was smallest, there was a clear
improvement in the relationship (r2=0.876). In Chl
algorithms based on spectral Rrs
ratios, such as the OC3, all blue absorption optical components present in
the surface water will result in overestimates of the remote sensing algorithms
(CARDER et al., 1989). In accordance with
the author, covariance of marine humic and other ocean color constituents with
Chl (Chl a and pheophytin a) is
a condition that must be fulfilled for waters to be classified as Case 1 and for the
global chlorophyll-a algorithm to be applied to data that are
remotely sensed. This covariance was more evident in March 2006 than in October 2005.
Therefore, Chl empirical ocean color algorithms are sensitive to
changes in phytoplankton community structure with Chl and also to
changes in the relative contribution of absorption and backscattering (CIOTTI et al., 1999; MOREL, 2006). Interestingly enough, the differences in nutrients,
CDOM and salinity did not modify the relationships between Sphy
and Chl observed in either period and follow a highly
consistent pattern. Thus, the performances of the Chl algorithm for
the continental shelf area adjacent to the Santos estuarine complex seem only to be
affected by the relative proportions of CDM and Chl.
CONCLUSIONS
This study demonstrated that three optical water types occur on the inner continental
shelf adjacent to Santos Bay and that these types can be discriminated by spectral
remote sensing reflectance. These optical water types reflect physical, biological and
chemical characteristics of the waters, and vary spatially due to the seasonal dynamic
of water masses and mixing processes between estuarine and shelf waters.
The most important contributor to the light absorption on the inner continental shelf
was the CDOM, especially in the region closest to Santos Bay, under the influence of the
estuary. However, the importance of CDOM in the area is influenced by the seasonal
dynamics of water masses on the shelf, while CDOM characteristics are due to the
distinct origins of the water masses. Consequently, CDOM is likely the principal optical
component affecting the performances of empirical ocean color algorithms. All further
improved algorithms will be more successful when describing CDOM magnitudes and spectral
behavior than in discriminating between distinct phytoplankton communities.
Finally, differences in the performance of the global empirical algorithm (OC3) were
observed regarding sampling period and water types. It is suggested that specific
algorithms should be used for the continental shelf adjacent to Santos Bay, such as
regionally or seasonally fitted empirical algorithms, or semi-analytical models.
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Universidade Federal de Santa Catarina Departamento
de Ecologia - Centro de Ciências Biológicas (Campus Trindade, 88010-970
Florianópolis, SC, Brasil)Universidade Federal de Santa CatarinaBrazilFlorianópolis, SC, BrazilUniversidade Federal de Santa Catarina Departamento
de Ecologia - Centro de Ciências Biológicas (Campus Trindade, 88010-970
Florianópolis, SC, Brasil)
Aurea Maria Ciotti
Instituto de Biociências da Universidade de São
Paulo Centro de Biologia Marinha da USP (Rodovia Manoel Hypólito do Rego, km. 131,5,
Praia do Cabelo Gordo 11600-000 São Sebastiao, SP, Brasil)Universidade de São PauloBrazilSão Sebastiao, SP, BrazilInstituto de Biociências da Universidade de São
Paulo Centro de Biologia Marinha da USP (Rodovia Manoel Hypólito do Rego, km. 131,5,
Praia do Cabelo Gordo 11600-000 São Sebastiao, SP, Brasil)
Sônia Maria Flores Gianesella
Instituto Oceanográfico da Universidade de São
Paulo (Praça do Oceanográfico, 191, 05508-120 São Paulo, SP, Brasil)Universidade de São PauloBrazilSão Paulo, SP, BrazilInstituto Oceanográfico da Universidade de São
Paulo (Praça do Oceanográfico, 191, 05508-120 São Paulo, SP, Brasil)
Flávia Marisa Prado Saldanha Corrêa
Instituto Oceanográfico da Universidade de São
Paulo (Praça do Oceanográfico, 191, 05508-120 São Paulo, SP, Brasil)Universidade de São PauloBrazilSão Paulo, SP, BrazilInstituto Oceanográfico da Universidade de São
Paulo (Praça do Oceanográfico, 191, 05508-120 São Paulo, SP, Brasil)
Rafael Riani Costa Perinotto
Universidade Estadual Paulista - Ciências
Biológicas (Praça Infante Dom Henrique s/n, Parque Bitaru,11330-900 São Vicente, SP,
Brasil)Universidade Estadual PaulistaBrazilSão Vicente, SP, BrazilUniversidade Estadual Paulista - Ciências
Biológicas (Praça Infante Dom Henrique s/n, Parque Bitaru,11330-900 São Vicente, SP,
Brasil)
Universidade Federal de Santa Catarina Departamento
de Ecologia - Centro de Ciências Biológicas (Campus Trindade, 88010-970
Florianópolis, SC, Brasil)Universidade Federal de Santa CatarinaBrazilFlorianópolis, SC, BrazilUniversidade Federal de Santa Catarina Departamento
de Ecologia - Centro de Ciências Biológicas (Campus Trindade, 88010-970
Florianópolis, SC, Brasil)
Instituto de Biociências da Universidade de São
Paulo Centro de Biologia Marinha da USP (Rodovia Manoel Hypólito do Rego, km. 131,5,
Praia do Cabelo Gordo 11600-000 São Sebastiao, SP, Brasil)Universidade de São PauloBrazilSão Sebastiao, SP, BrazilInstituto de Biociências da Universidade de São
Paulo Centro de Biologia Marinha da USP (Rodovia Manoel Hypólito do Rego, km. 131,5,
Praia do Cabelo Gordo 11600-000 São Sebastiao, SP, Brasil)
Instituto Oceanográfico da Universidade de São
Paulo (Praça do Oceanográfico, 191, 05508-120 São Paulo, SP, Brasil)Universidade de São PauloBrazilSão Paulo, SP, BrazilInstituto Oceanográfico da Universidade de São
Paulo (Praça do Oceanográfico, 191, 05508-120 São Paulo, SP, Brasil)
Universidade Estadual Paulista - Ciências
Biológicas (Praça Infante Dom Henrique s/n, Parque Bitaru,11330-900 São Vicente, SP,
Brasil)Universidade Estadual PaulistaBrazilSão Vicente, SP, BrazilUniversidade Estadual Paulista - Ciências
Biológicas (Praça Infante Dom Henrique s/n, Parque Bitaru,11330-900 São Vicente, SP,
Brasil)
Fig. 2
Cluster tree obtained by HCA analysis for the dataset comprising 49
stations from both sampling campaigns, using Rrs spectra between 407 and 752 nm as input. The suffix
o and m refers to station numbers
during October 2005 and March 2006, respectively. Squares are for class 1,
dots for class 2 and triangles for class 3.
Fig. 3
Linkage distances between clusters obtained in the HCA (see Figure 2). The dashed line represents the
limit to cut-off the cluster located at the inflection point of the
curve.
Fig. 4
Ordination of the stations by MDS for the 49 stations, using the
Rrs spectra between 407 and 752 nm. The suffix o and
m after the station numbers refers to October 2005 and
March 2006, respectively. The classes are represented by symbols (squares
for class 1, dots for class 2 and triangles for class 3). The circles around
the dots represent the limits of groups of optical water types.
Fig. 5 Rrs(λ) spectra normalized by
Rrs (551) for some representative stations
of class 1 (dash-dotted line), class 2 (solid line) and class 3 (dashed
line) derived from the cluster and ordering analyses (see Figures 2 to 4).
Fig. 6
Box plots for: (a) salinity; (b) temperature; (c) Chla (log); (d) silicate
(log); (e) ammonium (log); and (f)
phosphate (log), for the optical water type classes 1, 2
and 3. In the box plots, the median (line inside the box), lower quartile
and upper quartile (box), minimum and maximum values (whiskers) and outliers
(cross) are represented.
Fig. 7
Box-plots for light absorption magnitudes (log) by CDOM
(acdom), detritus (adet) and phytoplankton (aphy), for the optical water classes 1, 2 and 3. In the box plots, the
median (line inside the box), lower quartile and upper quartile (box),
minimum and maximum values (whiskers) and outliers (cross) are
represented.
Fig. 8
Box-plots for spectral slope of CDOM (a), detritus (b) and phytoplankton
(c), for the optical water classes 1, 2 and 3. In the box plots, the median
(line inside the box), lower quartile and upper quartile (box), minimum and
maximum values (whiskers) and outliers (cross) are represented.
Fig. 9
Temperature-Salinity (TS) diagram for October 2005 (crosses) and March
2006 (dots) cruises, with density lines in the background. The water masses
indicated in the figure (Coastal Water - CW, Tropical Water - TW and South
Atlantic Central Water - SACW) are according to the thermohaline index
proposed by Castro Filho et al.
(1987).
Fig. 10
Sea surface temperature (SST) 8-day average compositions derived from
MODIS/Aqua sensor, with a 4 km spatial resolution: 12-16 October, 2005 (a)
and 22- 29 March, 2006 (b).
Fig. 11
Scatter plot between acdom (m-1) and salinity for October 2005 (dots) and March
2006 (circles), and their respective linear adjustments, for n=47.
Fig. 12
Scatter plot between acdom (m-1) and Scdom (nm-1) (both in log scale) for October
2005 (dots) and March 2006 (circles), and their respective linear
adjustments, for n=47.
Fig. 13
Scatter plot between acdm (m-1) and aphy (m-1) (both in log scale) for October
2005 (dots) and March 2006 (circles), and their respective linear
adjustments, for n=47.
Table 1.
Average values for each class of water and results of one-way ANOVA for
critical p < 0.05, and Tukey HSD for unequal n between
discriminated optical water types for comparisons between physical and
biochemical variables. Significant values (p < 0.05) are
shown in bold fonts. Dataset for whole sampling period.
Table 2.
Average values of the variables for each class of water and results of
one-way ANOVA, for critical p < 0.05, and Tukey HSD for unequal
n between classes of optical water type for light
absorption parameters. Significant values are in
bold.
Table 3.
Average values of the variables for each sampling period and results of
one-way ANOVA, for critical p < 0.05, and Tukey HSD for unequal
n between sampling period for physical, biological,
chemical and light absorption parameters. The significant
values (p < 0.05) are in bold.
Table 4.
Correlation (r) and determination (r2) coefficient, p-value
(p), mean square error (MSE) between
Chl estimated by OC3 and in situ (log),
for the two periods and for the whole dataset (n=49).
imageFig. 1
Sampling grid for October 2005 and March 2006 on the inner continental
shelf off Santos estuary, Southeastern Brazil.
open_in_new
imageFig. 2
Cluster tree obtained by HCA analysis for the dataset comprising 49
stations from both sampling campaigns, using Rrs spectra between 407 and 752 nm as input. The suffix
o and m refers to station numbers
during October 2005 and March 2006, respectively. Squares are for class 1,
dots for class 2 and triangles for class 3.
open_in_new
imageFig. 3
Linkage distances between clusters obtained in the HCA (see Figure 2). The dashed line represents the
limit to cut-off the cluster located at the inflection point of the
curve.
open_in_new
imageFig. 4
Ordination of the stations by MDS for the 49 stations, using the
Rrs spectra between 407 and 752 nm. The suffix o and
m after the station numbers refers to October 2005 and
March 2006, respectively. The classes are represented by symbols (squares
for class 1, dots for class 2 and triangles for class 3). The circles around
the dots represent the limits of groups of optical water types.
open_in_new
imageFig. 5Rrs(λ) spectra normalized by
Rrs (551) for some representative stations
of class 1 (dash-dotted line), class 2 (solid line) and class 3 (dashed
line) derived from the cluster and ordering analyses (see Figures 2 to 4).
open_in_new
imageFig. 6
Box plots for: (a) salinity; (b) temperature; (c) Chla (log); (d) silicate
(log); (e) ammonium (log); and (f)
phosphate (log), for the optical water type classes 1, 2
and 3. In the box plots, the median (line inside the box), lower quartile
and upper quartile (box), minimum and maximum values (whiskers) and outliers
(cross) are represented.
open_in_new
imageFig. 7
Box-plots for light absorption magnitudes (log) by CDOM
(acdom), detritus (adet) and phytoplankton (aphy), for the optical water classes 1, 2 and 3. In the box plots, the
median (line inside the box), lower quartile and upper quartile (box),
minimum and maximum values (whiskers) and outliers (cross) are
represented.
open_in_new
imageFig. 8
Box-plots for spectral slope of CDOM (a), detritus (b) and phytoplankton
(c), for the optical water classes 1, 2 and 3. In the box plots, the median
(line inside the box), lower quartile and upper quartile (box), minimum and
maximum values (whiskers) and outliers (cross) are represented.
open_in_new
imageFig. 9
Temperature-Salinity (TS) diagram for October 2005 (crosses) and March
2006 (dots) cruises, with density lines in the background. The water masses
indicated in the figure (Coastal Water - CW, Tropical Water - TW and South
Atlantic Central Water - SACW) are according to the thermohaline index
proposed by Castro Filho et al.
(1987).
open_in_new
imageFig. 10
Sea surface temperature (SST) 8-day average compositions derived from
MODIS/Aqua sensor, with a 4 km spatial resolution: 12-16 October, 2005 (a)
and 22- 29 March, 2006 (b).
open_in_new
imageFig. 11
Scatter plot between acdom (m-1) and salinity for October 2005 (dots) and March
2006 (circles), and their respective linear adjustments, for n=47.
open_in_new
imageFig. 12
Scatter plot between acdom (m-1) and Scdom (nm-1) (both in log scale) for October
2005 (dots) and March 2006 (circles), and their respective linear
adjustments, for n=47.
open_in_new
imageFig. 13
Scatter plot between acdm (m-1) and aphy (m-1) (both in log scale) for October
2005 (dots) and March 2006 (circles), and their respective linear
adjustments, for n=47.
open_in_new
table_chartTable 1.
Average values for each class of water and results of one-way ANOVA for
critical p < 0.05, and Tukey HSD for unequal n between
discriminated optical water types for comparisons between physical and
biochemical variables. Significant values (p < 0.05) are
shown in bold fonts. Dataset for whole sampling period.
Variable
Class 1
Class 2
Class 3
Differences between classes
F calculated
p value
Salinity
35.10
34.23
32.26
3 < 1 = 2
12.560
0.000
Temperature (°C)
27.61
25.28
24.28
1 = 2 = 3
1.993
0.140
Chl - log (mg m-3)
-0.47
-0.43
0.24
3 > 1 = 2
13.852
0.000
Silicate (μM)
2.66
2.50
5.21
3 > 1 = 2
5.026
0.011
Ammonium (μM)
0.13
0.18
0.37
1 = 2 = 3
0.722
0.492
Phosphate (μM)
0.00
0.37
0.50
1 < 2 = 3
7.052
0.002
table_chartTable 2.
Average values of the variables for each class of water and results of
one-way ANOVA, for critical p < 0.05, and Tukey HSD for unequal
n between classes of optical water type for light
absorption parameters. Significant values are in
bold.
Parameter
Class 1
Class 2
Class 3
Difference between the classes
F calculated
p value
acdom (m-1)
0.017
0.050
0.188
3 > 1 = 2
16.970
0.000
aphy (m-1)
0.008
0.009
0.040
3 > 1 = 2
4.441
0.018
adet (m-1)
0.007
0.005
0.053
1 = 2 = 3
2.079
0.137
Scdom
0.023
0.019
0.017
1 > 2 = 3
9.597
0.000
Sphy
0.835
0.836
0.527
3 < 1 = 2
20.789
0.000
Sdet
0.011
0.011
0.012
1 = 2 = 3
0.824
0.445
table_chartTable 3.
Average values of the variables for each sampling period and results of
one-way ANOVA, for critical p < 0.05, and Tukey HSD for unequal
n between sampling period for physical, biological,
chemical and light absorption parameters. The significant
values (p < 0.05) are in bold.
Parameter
Oct, 2005
Mar, 2006
F calculated
p value
Salinity
31.72
34.48
90.046
0.000
Temperature (°C)
22.10
28.03
1297.282
0.000
Chl (mg m-3)
2.26
2.66
0.119
0.732
Silicate (μM)
5.38
3.08
8.400
0.006
Ammonium (μM)
0.46
0.12
5.058
0.029
Phosphate (μM)
0.53
0.31
9.300
0.004
acdom (m-1)
0.17
0.10
6.067
0.018
aphy (m-1)
0.03
0.03
0.092
0.763
adet (m-1)
0.04
0.03
0.121
0.730
at (m-1)
0.27
0.21
0.890
0.350
Scdom
0.02
0.02
0.002
0.966
Sphy
0.62
0.63
0.052
0.821
Sdet
0.01
0.01
1.994
0.165
table_chartTable 4.
Correlation (r) and determination (r2) coefficient, p-value
(p), mean square error (MSE) between
Chl estimated by OC3 and in situ (log),
for the two periods and for the whole dataset (n=49).
Algorithm
n
r
r2
p
MSE
OC3
49
0.827
0.683
0.000
0.0368
OC3 - October, 2005
27
0.826
0.682
0.000
0.0255
OC3 - March, 2006
22
0.936
0.876
0.000
0.0165
Como citar
Carvalho, Melissa et al. Bio-Optical Properties of the Inner Continental Shelf off Santos Estuarine System, Southeastern Brazil, and their Implications for Ocean Color Algorithm Performance. Brazilian Journal of Oceanography [online]. 2014, v. 62, n. 2 [Acessado 3 Abril 2025], pp. 71-87. Disponível em: <https://doi.org/10.1590/S1679-87592014044506202>. ISSN 1982-436X. https://doi.org/10.1590/S1679-87592014044506202.
Universidade de São Paulo, Instituto OceanográficoPraça do Oceanográfico, 191 , 05508-120 Cidade Universitária, São Paulo - SP - Brasil, Tel.: (55 11) 3091-6501, Fax: (55 11) 3032-3092 -
São Paulo -
SP -
Brazil E-mail: io@usp.br
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