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A simple optical model to estimate suspended particulate matter in Yellow River Estuary

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A simple optical model to estimate suspended particulate matter in Yellow River Estuary

Abstract and Figures

Distribution of the suspended particulate matter (SPM) concentration is a key issue for analyzing the deposition and erosion variety of the estuary and evaluating the material fluxes from river to sea. Satellite remote sensing is a useful tool to investigate the spatial variation of SPM concentration in estuarial zones. However, algorithm developments and validations of the SPM concentrations in Yellow River Estuary (YRE) have been seldom performed before and therefore our knowledge on the quality of retrieval of SPM concentration is poor. In this study, we developed a new simple optical model to estimate SPM concentration in YRE by specifying the optimal wavelength ratios (600-710 nm)/ (530-590 nm) based on observations of 5 cruises during 2004 and 2011. The simple optical model was attentively calibrated and the optimal band ratios were selected for application to multiple sensors, 678/551 for the Moderate Resolution Imaging Spectroradiometer (MODIS), 705/560 for the Medium Resolution Imaging Spectrometer (MERIS) and 680/555 for the Geostationary Ocean Color Imager (GOCI). With the simple optical model, the relative percentage difference and the mean absolute error were 35.4% and 15.6 gm<sup>-3</sup> respectively for MODIS, 42.2% and 16.3 gm<sup>-3</sup> for MERIS, and 34.2% and 14.7 gm<sup>-3</sup> for GOCI, based on an independent validation data set. Our results showed a good precision of estimation for SPM concentration using the new simple optical model, contrasting with the poor estimations derived from existing empirical models. Providing an available atmospheric correction scheme for satellite imagery, our simple model could be used for quantitative monitoring of SPM concentrations in YRE.
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A simple optical model to estimate suspended
particulate matter in Yellow River Estuary
Zhongfeng Qiu*
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China
*zhongfeng.qiu@nuist.edu.cn
Abstract: Distribution of the suspended particulate matter (SPM)
concentration is a key issue for analyzing the deposition and erosion variety
of the estuary and evaluating the material fluxes from river to sea. Satellite
remote sensing is a useful tool to investigate the spatial variation of SPM
concentration in estuarial zones. However, algorithm developments and
validations of the SPM concentrations in Yellow River Estuary (YRE) have
been seldom performed before and therefore our knowledge on the quality
of retrieval of SPM concentration is poor. In this study, we developed a new
simple optical model to estimate SPM concentration in YRE by specifying
the optimal wavelength ratios (600-710 nm)/ (530-590 nm) based on
observations of 5 cruises during 2004 and 2011. The simple optical model
was attentively calibrated and the optimal band ratios were selected for
application to multiple sensors, 678/551 for the Moderate Resolution
Imaging Spectroradiometer (MODIS), 705/560 for the Medium Resolution
Imaging Spectrometer (MERIS) and 680/555 for the Geostationary Ocean
Color Imager (GOCI). With the simple optical model, the relative
percentage difference and the mean absolute error were 35.4% and 15.6
gm3 respectively for MODIS, 42.2% and 16.3 gm3 for MERIS, and 34.2%
and 14.7 gm3 for GOCI, based on an independent validation data set. Our
results showed a good precision of estimation for SPM concentration using
the new simple optical model, contrasting with the poor estimations derived
from existing empirical models. Providing an available atmospheric
correction scheme for satellite imagery, our simple model could be used for
quantitative monitoring of SPM concentrations in YRE.
©2013 Optical Society of America
OCIS codes: (010.1690) Color; (010.7340) Water; (010.0280) Remote sensing and sensors.
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1. Introduction
Suspended particulate matter (SPM) has a great effect on the transparency, turbidity and water
color of estuarine and coastal waters [1–3]. Knowledge of the loads, spatial distribution and
physical properties of SPM is, therefore, essential to evaluate geomorphologic changes and to
monitor water quality, since they relate total primary production to heavy metal and micro
pollutants [4,5]. Furthermore, SPM in coastal and estuarine waters play an important role in
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biogeochemical cycles. The interaction between SPM and seawater constituents may strongly
modify the nutrient concentration in estuarine systems. The fine-grained particles are an
important carrier of various chemical compounds. Understanding the temporal and spatial
dynamics of SPM in estuarine systems can thus allow for the estimation of the transport of
terrestrial and anthropogenic materials to pelagic oceans. However, estuarial ecosystems
typically exhibit SPM concentrations ([SPM]) with high temporal and spatial variability,
which are often too difficult to be characterized by using traditional field sampling methods
[2,4,6,7].
Fortunately, satellite imagery can be used to rapidly assess the [SPM] in coastal and
estuarine environments at temporal and spatial scales difficult to attain with direct field
measurements [2,3,7–9]. Considerable success in [SPM] estimation has been demonstrated
with data from a variety of sensors with varying radiometric accuracy and sensitivity and
various spatial and temporal resolutions, such as the Sea-viewing Wide Field-of view Sensor
(SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium
Resolution Imaging Spectrometer (MERIS) and the Geostationary Ocean Color Imager
(GOCI) [2,7,9]. Two types of SPM algorithms are widely used for accurate [SPM] estimation:
empirical models and semi-analytical models. The empirical methods are based on
relationships between [SPM] and single-channel or multi-channel remote sensing reflectance
(Rrs) [3,7,10–13]. The semi-analytical algorithm approach consists in connecting Rrs to [SPM]
via a simplified optical model by which reflectance is expressed according to the inherent
optical properties (IOPs) of absorption and backscattering [6,14–16]. The relationships used
in empirical models are normally geographically specific and hardly be directly applied to
other coastal areas. In addition, semi-analytical models depend on accurate information for
the IOPs, which are difficult to be accurately measured especially in a turbid waters with high
[SPM] variation [3,16]. Therefore, only “regional” SPM algorithms are implemented in areas
with similar particle characteristics and where [SPM] products can be validated [9].
Despite considerable success in [SPM] estimation from satellite data, remote sensing of
such a complex system in Yellow River Estuary (YRE) is quite challenging. Optical
properties in YRE are quite complicated due to sediment resuspension and river discharging.
[SPM] in YRE are strongly influence by a combination of hydrodynamic, physico-chemical,
and biological processes [17]. Previously, algorithm development and validation of [SPM] in
YRE have seldom been performed [5], especially for the new ocean color sensor GOCI. The
GOCI sensor, launched by the Korean Ocean Space Center in 2010, is the world’s first ocean
color sensor in a geostationary orbit [18]. This revolutionary design offers very significant
new possibilities for remote sensing of sediment dynamics in tidal regions, because imagery is
acquired every hour during daylight, up to a maximum of 8 images. It is possible to resolve
high temporal variability and to obtain more days with usable data in periods of scattered
clouds. GOCI has great potential to monitor [SPM] in an optically complex estuary such as
YRE. However, to our knowledge, no applicable algorithms have been developed and
validated to accurately derive [SPM] products in YRE by using GOCI data. Furthermore, no
validation for the existing models (such as those listed in Table 3) with in situ observations in
YRE has been published. The existing models might not be applicable to YRE, although they
were successfully developed and used in other waters. Therefore, significant efforts on
improving the accuracy of satellite-derived [SPM] are required in such areas.
In the present study a simple optical algorithm for estimating [SPM] is calibrated and
validated based on in situ observations of 5 cruises during 2004 and 2011 in YRE. The
specific calibration and validation for MODIS, MERIS and GOCI have been performed for
the model's applications to multiple ocean color sensors. The existing empirical models are
also validated by using the in situ observations.
#198298 - $15.00 USD
Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27893
2. Materials and methods
2.1 Study area
The study area locates in the YRE and its adjacent waters (Fig. 1). The Yellow River is one of
the most sediment-laden rivers in the world. The river is about 5460 km long with a drainage
basin covering ca. 752,000 km2 [19]. Historically, the river discharged an average of 574 ×
108 m3 of water and 10 × 108 t of sediment annually, corresponding approximately 50–60%
of the freshwater and more than 90% of the sediment received by the Bohai Sea [20,21]. The
river outflow introduces SPM, dissolved and particulate organic matter into the Bohai Sea,
potentially affecting the marine environment and representing an important flux of carbon.
The riverine material strongly affects the optical properties of the coastal waters as seen in
Fig. 1, making satellite sensors the most suitable tools available to map the river influence on
the Bohai Sea. Previous studies [22] have shown that high spatio-temporal variability is
observed in the distributions of SPM, which are closely related to hydrodynamic processes,
such as winds, tides and currents. Both estuarine gravitational circulation and tidal asymmetry
are important with the complex flow patterns largely determined by the interaction of Yellow
River runoff, tidal currents, and varying topography, along with density differences and
winds. The complicated hydrodynamics lead to very high SPM concentrations and high
turbidity in the YRE.
Fig. 1. Location of stations sampled during 5 cruises between 2004 and 2011. True color image
(composite from MERIS bands 1, 4 and 3) of the Bohai Sea region is acquired by MERIS on
April 3, 2011.
2.2 Field data collection and processing
A total of 5 oceanographic surveys were conducted in YRE between June 2004 and December
2011 (Table 1). Sampling stations covered 119 °E-120 °E and 37.5 °N 38.5 °N (Fig. 1).
Waters are optically complex with the values of the water constitutes varying in a wide range
(in 2-3 orders). The concentrations of chlorophyll a (Chl) were in 0.01 mgm3-62.9 mgm3,
and the concentrations of the SPM were in 1.69 gm3-1896.5 gm3. The [SPM], the Rrs and
other environmental parameters were synchronously measured strictly following the NASA
SIMBIOS ocean optic protocols [23].
Table 1. Time of the 5 cruise surveys to measure ocean properties
# of cruises Time # of stations Measured dataa
1 Jun, 2004 44 Rrs, T, S, Chl, [SPM]
2 Jun, Aug, 2005 17 Rrs, T, S, Chl, [SPM]
1 Jul, 2011 29 Rrs, T, S, Chl, [SPM]
1 Nov-Dec, 2011 56 Rrs, T, S, Chl, [SPM]
aRrs denotes remote sensing reflectance, T denotes temperature, S denotes salinity, Chl denotes chlorophyll a
fluorescence and [SPM] denotes concentrations of suspended particulate matter.
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27894
The Rrs used in this study were measured by an ASD FieldSpec Dual VNIR, covering the
spectral range of 350-1050 nm. The absolute radiance calibration of detectors was performed
before each cruise. During the measurements, the tip of the optical fiber was kept ~1 m above
the water surface by means of a 3 m long hand-handle pole. The zenith and azimuth angles
viewing from the water surface are about 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 measurement location was selected
with minimal shading, reflections from superstructure, ship wake and associated foam patches
as well as whitecaps. Additionally, the measurement location was selected to point easily to a
direction away from the sun glint.
The measured Rrs is calculated using Eq. (1):
sw sky
rs
pp
LρL
RπLρ
= (1)
where Lsw is the radiance received by the ASD above the water surface; Lsky is the radiance of
sky; ρp is the reflectance of the plate; Lp is the radiance received by the ASD above the plate;
ρis the dimensionless air–water reflectance and is calculated with assumption of the black
ocean at wavelengths from 1000 to 1020 nm [24] and wavelength-independent [16]. The
value of ρis always in the range of 0.022–0.05 [25,26]. When ρ is out of the range of 0.022-
0.05, it is determined from the sea conditions [27].
The [SPM] (units are gm3), defined as the dry mass of particles per unit volume of water,
was determined using a standard gravimetric technique [3]. At each station, water samples
were collected just below the sea surface with 10-liter Niskin bottles simultaneously with in
situ optical measurements. The water sample was filtered with 0.45 m filter (Whatman GF/F
filters) and vacuum filtration system. To remove salt, filters were washed with 250 mL of
MilliQ water 3 times after filtration. Filters were dried for 24 h at 40°C and reweighed to
obtain [SPM]. The dry-weight of the filter-pad was weighed by an electronic analytic scale.
The blank filter and sampled filter-pad were weighed until the difference between two
successive [SPM] calculated from the scale reading was within 0.01 gm3.
2.3 Data analysis
After quality control we have the data set of 122 samples with paired Rrs and [SPM]. To
calibrate and validate our SPM model, this data set was randomly divided into two groups,
namely the calibration data set (n = 81) and the validation data set (n = 41).
Statistical analysis (mean value, linear and non-linear fitting) are performed with
MATLAB software. The performances of the retrievals are evaluated by the correlation
coefficient (r2), the relative percentage difference (RPD) and the mean absolute error (MAE).
The calculation of RPD was the same as the previous study [28] and MAE was described as
Eq. (2)
mod, ,
1
m
iobsi
ixx
MAE m
=
= (2)
where xmod,i and xobs,i were the estimated value and the observed value of the ith element,
respectively, and m was the number of elements.
3. Results
3.1 Variation of spectral characteristics and correlation with [SPM]
The Rrs spectra of the water mass with various [SPM] in the 2004 cruise in YRE are shown in
Fig. 2. In general, the Rrs is highly variable over the visible and near-infrared spectral regions.
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27895
In addition, the values of the Rrs increase with [SPM], especially in the red and near-infrared
range (wavelength larger than 600 nm).
In the low turbidity water (with [SPM] <15 gm3) [Fig. 2(a)], the values of the Rrs increase
as a function of the wavelength from the blue to green band, reaching the peak at ca. 570 nm.
The values decrease quickly in the range of 570-600 nm, and finally end with the minimum at
the infrared band (larger than 700 nm). The downslope (from 570 to 600 nm) is generally
steeper than the upslope (from the blue to green band).
In the modulate turbidity water (with 15 gm3<[SPM]<150 gm3) [Fig. 2(b)], the spectra
shapes are similar to those in the low turbidity water in the blue and green bands. The values
decrease slightly during 570- 700 nm and steep downslope is observed in the range of 720-
740 nm. In the near-infrared range the values remain higher than ca. 0.012 Sr1.
In the high turbidity water (with [SPM]>150 gm3) [Fig. 2(c)], the Rrs spectra display an
asymptotic approach to a broad peak ~570-700nm. In the near-infrared range (700-900 nm)
the Rrs spectra vary widely with a second peak located at ca. 810 nm.
Fig. 2. Remote sensing reflectance of typical water in the 2004 cruise in Yellow River Estuary.
(a) [SPM]<15 gm3; (b) 15 gm3<[SPM]<150 gm3; (c) [SPM]>150 gm3.
3.2 Estimation model ofSPM: calibration
The calibration data set contained 81 water samples, with [SPM] ranging from 1.9 to 1896.5
gm3 with a mean value of 88.1 ± 256.8 gm3 (mean ± standard deviation). To find the best
wavelength band, or band ratios, by which to estimate [SPM] in YRE, the single band and the
ratios of any two wavelengths from 400 to 900 nm were tested for correlation with [SPM]
based on the exponential [Eq. (3)] and quadratic algorithms [Eq. (4)].
[
]
()
log SPM aX= (3)
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27896
[
]
()
2
log SPM aX bX C=++ (4)
where X is a single band or band ratio, a, b, c are coefficients.
The quadratic algorithm using single band showed a reasonable correlation with [SPM]
within wavelengths from 700 to 900 nm (Fig. 3). Two valleys near 740 nm and 810 nm were
found in the Fig. 3(a). However, the relative percentage difference is high with the value
larger than 80% at all wavelengths. The exponential algorithm using single band performed
worse than the quadratic algorithm. The results showed that the two algorithms using single
band could not be used to estimated accurately [SPM] in the calibration data set.
Fig. 3. Comparisons of the mean absolute error (a), relative percentage difference (b) and
correlation coefficient (c) using two different algorithms. The color blue represents the
exponential algorithm and red represents the quadratic algorithm.
Fig. 4. Comparisons of the relative percentage difference for the exponential algorithm (a) and
the quadratic algorithm (b) using band ratios. The band ratios are performed as the wavelength
(x-axis) divided by the wavelength (y-axis).
Algorithms using band ratios performed better than the algorithms using single band. For
simplicity, we only present the RPD in Fig. 4 (while RPD < 60%). Both algorithms using
band ratios showed a good precision with the band ratios (600-710 nm)/(530-590 nm), with
the RPD<35%. In addition, low MAE and high r2 are also observed at the band ratios (data
not shown). Therefore, the exponential algorithm using band ratios (600-710 nm)/ (530-590
nm) is recommended, considering the simplicity of the model.
To apply the new model presented in this study to the satellite data, the spectra recorded
by ASD were aggregated using the spectral response functions of the satellite sensors
MODIS, MERIS and GOCI. The model band ratios were determined for MODIS, MERIS and
GOCI spectral bands. After tuning the band ratios are chosen as 678/551 for MODIS, 705/560
for MERIS and 680/555 for GOCI (Table 2). The models for all sensors give a good estimate
#198298 - $15.00 USD
Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27897
of [SPM] for YRE (Fig. 5), with RPD 32.6%, 33.2% and 33.2%, and r2 0.95, 0.98 and 0.95 for
MODIS, MERIS and GOCI, respectively. The model for MERIS (MAE = 16.6 gm3)
performs a little better than the MODIS (MAE = 24.5 gm3) and GOCI (MAE = 26.2 gm3).
Table 2. Band ratios and coefficients of the exponential algorithm
band ratios a b
MODIS 678/551 1.932 0.875
MERIS 705/560 2.239 0.688
GOCI 680/555 1.988 0.877
Fig. 5. Comparisons of the measured and estimated [SPM] from models respectively for
MODIS (a), MERIS (b) and GOCI (c) sensor. The measured [SPM] are from the calibration
data set.
3.3 Estimation model of SPM: validation
To further understand the applicability of the simple optical model to estimate [SPM], we
evaluated its performance using a validation data set of 41 samples. The [SPM] in the
validation data set varied from 1.69 to 393.6 gm3 with a mean value of 51.8 ± 99.7 gm3,
which fell into the range of [SPM] used to calibrate the model.
Comparisons of the measured and estimated [SPM] from the calibrated simple optical
models for MODIS, MERIS and GOCI showed that these values were in close agreement
(Fig. 6), with a highly significant relationship with an r2 of 0.93, 0.93 and 0.94, the MAE of
15.6 gm3, 16.3 gm3 and 14.7 gm3, and the corresponding RPD of 35.4%, 42.2% and 34.2%,
respectively. The measured and estimated values of [SPM] were distributed along the 1:1 line
(Fig. 6), indicating that the simple optical model could be used for the turbid waters of YRE.
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27898
Fig. 6. Comparisons of the measured and estimated [SPM] from models respectively for
MODIS (a), MERIS (b) and GOCI (c) sensor. The measured [SPM] are from an independent
data set from Yellow River Estuary.
3.4 Estimation model of SPM: sensitivity analysis
A sensitivity analysis was performed for the simple optical model by using the validation data
set of 41 samples. Random errors were added 50 times into the in situ Rrs values. The random
errors were drawn from the standard uniform distribution with the mean value 0 and the
standard deviation 5%. Totally we have 2050 samples with <5% errors being randomly added
into the Rrs values.
Comparisons of the measured and estimated [SPM] from the errors-added Rrs
demonstrated that these values were in close agreement. The values of RPD of all three
models varied in the range of <5%. Here for simplify we only presented the MODIS model in
Fig. 7. Comparing Fig. 6(a) and Fig. 7 we can find that the distributions of estimated [SPM]
are very similar in two Figs. The measured and estimated values of [SPM] were distributed
along the 1:1 line. The value of RPD is 39.4% for the [SPM] estimated from the errors-added
Rrs, which is 4% higher than that for the [SPM] estimated from the real in situ Rrs. The value
of MAE is 16.2 gm3 and 0.6 gm3 higher accordingly. Therefore, the sensitivity analysis
indicates that the simple model is robust to estimate [SPM] in the YRE, at least in the ranges
of the data set.
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27899
Fig. 7. Comparisons of the measured and estimated [SPM] from the MODIS model. The
measured [SPM] are from an independent data set from Yellow River Estuary. The <5% errors
were randomly added 50 times into the Rrs values.
4. Discussion
4.1 Variation of the Rrs in YRE
In section 3.1 we introduced the variation of the Rrs over the visible and near-infrared spectral
regions, which is similar to the results published in other turbid waters [3,14]. As shown in
Fig. 2, the dramatic impact of [SPM] on volume reflectance is clear. The shapes of the Rrs are
significantly different between various [SPM]. The [SPM] can substantially increase the
volume reflectance in a manner that becomes more pronounced as the wavelength becomes
longer.
The peak of the Rrs at ca. 570 nm is mainly due to high backscattering from SPM. The
second peak of the Rrs at 810 nm in the turbid waters is the result of both high backscattering
and a minimum in absorption by all optically active constituents including pure water. The Rrs
at 570 nm is normally higher than that at 700 nm for [SPM] < 150 gm3. This relation is
reversed for [SPM] > 150 gm3, because Rrs at 570 nm tends to saturate while [SPM]
increases.
The variation of the Rrs relating to [SPM] is not only determined with [SPM], but also the
different composition (refractive index, density) and size distribution. For example, Bowers
and Binding [29] reported that the smaller sized sediments generally lead to a higher spectral
reflectance. Shen et al. [5] also presented that particle size takes significant effect on Rrs with
a dependency not only on spectral wavelength but also on [SPM] ranges.
4.2 Assessment and application of the simple optical model
In order to compare the estimation precision of our simple optical model with that of previous
models, we firstly calibrated the coefficients for the same model expressions (Table 3) using
the calibration data set, including Miller model [12], Doxaran model [16], Tassan model [13],
and Zhang model [3]. Then we assessed those models by using the same independent
validation data set we used to validate our own model.
Significant differences were found between estimated and measured [SPM] values, with
RPD of 61.6%, 125.5%, 73.31% and 76.49% for Miller model, Doxaran model, Tassan model
and Zhang model, respectively (Table 3). The Miller model used reflectance at 645 nm as the
indicator of [SPM]. Figure 3 have indicated that a single band model is not suitable for the
data set.
The Doxaran model estimated [SPM] with the largest RPD among the four models, which
also presented in Fig. 8. The reason is that the Doxaran model used the Rrs at 840 nm. Figure
2(a) displayed that the values of Rrs in the range of larger than 750 nm are small when [SPM]
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(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27900
are lower than 15 gm3. Therefore, The Doxaran model using ratio Rrs(840)/Rrs(545) is unable
to characterize the Rrs differences in waters with low [SPM]. The estimated [SPM] are almost
10 gm3 when the measured [SPM] varied in the range of 1- 15 gm3[Fig. 8(b)], which
denotes that the Doxaran model is failure in the clear or moderate turbid waters of YRE.
Figure 4 have also indicated that the ratio Rrs(840)/Rrs(545) is not appropriate for estimating
[SPM] in such a data set.
The Tassan model has attained a similar performance as the Zhang model. Both models
used Rrs(555), Rrs(645) and Rrs(488)/Rrs(555). Although the ratio Rrs(488)/Rrs(555) can
represent partly the variation of [SPM], Fig. 4 have displayed that the ratio is not the best
choice for estimating [SPM]. Figure 8 denote that the estimated [SPM] scattering along the
1:1 line.
All the four models are successfully applied in some regions [3,12,13,16], they are not
appropriate for YRE even after an attentive tuning (Fig. 8 and Table 3). One reason is that the
relationship between reflectance and [SPM] are different in various regions. Shen et al. [5]
have introduced that the interrelationships between Rrs and sediment characteristics are
largely difference in the highly turbid waters of the Yangtze River estuary and the YRE. The
second reason is that the variation of the Rrs relating to SPM is not only determined with
[SPM], but also the other properties, such as size distribution. Shen et al. [5] presented that
the effect of particle size of SPM on the observed Rrs is significant and depends on
wavelengths and a [SPM] range. The SPM composition is also observed to affect the values
of Rrs. Therefore, even though a regional algorithm was successfully developed in turbid
water, the algorithm is difficult to directly apply in other regions, such as the turbid waters of
YRE.
Table 3. Comparison between SPM concentration quantitative retrieval models' results
and the measured data
Model Adjust model Memo RPD
(%)
MAE
(gm3) R2
Miller [SPM] = 1934.1X-71.3 X = πL
w
(645)/(F0(645)u0)61.60 32.72 0.66
Doxaran ln[SPM] = 2.0713x + 0.9167 X = Rrs(840)/Rrs(545) 125.5 28.14 0.79
Tassan log[SPM] = 2.575logX + 4.7877 X = [Rrs(555) + Rrs(645)] [
Rrs(488)/Rrs(555)]b73.31 34.76 0.65
Zhang log[SPM] = 0.5937 + 19.02X1-
0.6208X2
X1 = [Rrs(555) + Rrs(645)], X2
= [ Rrs(488)/Rrs(555)] 76.49 38.77 0.55
The design of the new approaches presented in this study is different from the four
models, although they are all empirical algorithms. Our simple optical model used band ratios
(600-710 nm)/ (530-590 nm). Figure 2 indicated that the first peak of Rrs values located in
about 550-590 nm and the values varied significantly with the variation of [SPM] in the range
600-710 nm. Therefore, band ratios (600-710 nm)/ (530-590 nm) is capable to reflecting the
variations of [SPM]. Our simple optical model is based on optical characteristics of Rrs in
waters with various [SPM].
By comparison, our simple optical model produces a superior performance to all of the
four models. Using of our simple optical model for the three sensors in estimating [SPM] in
YRE decreases the RPD values of estimation by > 20%.
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27901
Fig. 8. Comparisons of the measured and estimated [SPM] from (a) Miller model, (b) Doxaran
model, (c) Tassan model, and (d) Zhang model, respectively. The measured [SPM] are from an
independent data set from Yellow River Estuary.
Our simple optical algorithm to estimate [SPM] is calibrated and validated using the in
situ data set. A few caveats need to be considered when attempting to apply the model to
satellite data. The successful applications of our simple model to satellite data depend heavily
on the accuracy of the atmospheric corrections. Our model relies strongly on reflectance in
red and NIR region (for example, reflectance at 705 nm used for MERIS). There are some
specific hurdles that are to be expected. No operational atmospheric correction procedures
have been proved universally robust in the NIR region across waters of varying geophysical
features, especially in the turbid waters of YRE. In theory, the Wang's model seems a feasible
option [30], which involves the use of shortwave infrared (SWIR) bands for aerosol model
selection. However, some practical experiments with MODIS data has shown that even
though ocean color products in turbid coastal waters can be improved using SWIR bands-
based model, the extent of improvement is very limited due to the considerably lower sensor
SNR values for the MODIS SWIR bands [31]. Furthermore, no SWIR bands are available for
GOCI data, which have shown great potentials in monitoring variations of SPM in turbid
waters such as YRE. Nowadays great efforts are being made to improve the accuracy of
atmospheric correction in turbid waters, especially in the waters in China seas, such as [3,7,8].
With the retrieval of accurate reflectance our simple models can be appropriately applied to
estimate [SPM] in YRE.
4.3 SPM mapping based on the simple optical model
Providing an overall SPM model performance evaluation, Fig. 9 shows maps of [SPM] in
YRE on July 16, 2005. Here for simplify we only present SPM mapping based on the simple
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27902
optical model for MERIS. The [SPM] is derived from the atmospheric correction results of
Schroeder's method [32], which has been recently validated by Cui et al. [33].
High [SPM] distributed along the coast areas (Fig. 9), especially in the mouth of YRE and
the west coast of the Laizhou Bay. The concentrations along the coast are higher than in the
outside part in the Bohai Sea. As a result, the apparent offshore decreases of the [SPM]
indicate that waters along the offshore are more turbid than those of the Central Bohai Sea. A
high [SPM] areas is obviously observed in the central Laizhou Bay, which is connected with
the mouth of YRE. This pattern indicated that the SPM from the Yellow River affect mainly
the areas in the Laizhou Bay. The distributions of [SPM] are consistent with the previous
works [19, 20, 22], which indicated that our ratio algorithm is appropriate to estimate [SPM]
in YRE.
Fig. 9. Maps of suspended particulate matter concentration generated from MERIS data based
on the simple optical algorithm. The MERIS data are acquired on July 16, 2005.
5. Conclusions
Distribution of the SPM concentration is a key measure for analyzing the deposition and
erosion variety of the estuary and evaluating the material fluxes from river to sea. Satellite
remote sensing is a useful tool to investigate the spatial variation of SPM concentration in
estuarial zones. In this study, we developed a new simple optical model to estimate [SPM] by
specifying the optimal wavelength ratios (600-710 nm)/ (530-590 nm). The simple optical
model was superior for application in YRE when compared to the published empirical
models, using an independent validation data set. Thus, the simple optical model improved
the [SPM] estimation precision in YRE.
Further study may include determining the temporal-spatial distribution of [SPM] using
the satellite data based on the simple optical model calibrated and validated in this study. The
new algorithms will be used to process large satellite data archives from MODIS, MERIS and
GOCI in YRE. Inter-sensor comparisons will be made for [SPM], providing a quality check
on the GOCI data, and GOCI data will be used to assess the temporal variability from the
MODIS and MERIS archives. Furthermore, the satellite [SPM] data will be combined with
available hydrodynamic and meteorological information on wind speed, tidal currents and
river discharges. The integrated data set will be studied in order to interpret sediment transport
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27903
processes in the region, characterizing SPM variability in terms of tide- and wind-driven
advection and resuspension processes.
Acknowledgments
This study was jointly supported by the Public Science and Technology Research Funds
Projects of Ocean (201005030), the National Natural Science Foundation of China
(41276186), the Fund from NUIST (S8111005001)and a project funded by “the Priority
Academic Program Development of Jiangsu Higher Education Institutions (PAPD)”. We are
thankful to two anonymous reviewers who provided substantial comments and suggestions
that led to the improvement of this manuscript.
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Received 25 Sep 2013; revised 25 Oct 2013; accepted 1 Nov 2013; published 6 Nov 2013
(C) 2013 OSA
18 November 2013 | Vol. 21, No. 23 | DOI:10.1364/OE.21.027891 | OPTICS EXPRESS 27904
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The Chinese Moderate Resolution Imaging Spectroradiometer (CMODIS) was loaded on the China's SZ‐3 spacecraft. Using an empirical line method, the CMODIS radiance is converted to the water‐leaving reflectance, and is applied to inversion of the suspended sediment concentrations in the Yangtze River estuary. The concentrations ranging between 0 mg/L and 1000 mg/L are well validated by the field measurement data. This study demonstrates an example for the feasibility of the CMODIS data for concentration retrieval of the suspended sediment.
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The East China Sea (ECS) is well known for its high concentration of total suspended matter (TSM). Some regions of the ECS have concentrations higher than 5000 g m− 3, exceeding the valid ranges of many TSM remote sensing algorithms. To overcome the limitation of the existing algorithms, a new TSM model, the “complex proxy TSM model” (CPTSM), is developed in this study. The model is established on the basis of a complex proxy of remote sensing reflectance. The proxy is designed to convert the non-linear relationship between reflectance and TSM to a quasi-linear function over the entire range of TSM concentrations. This proxy is deduced from four indices defined by combinations of the reflectance at different bands. The four indices take advantage of the different relationships between the band combinations of the reflectance and total TSM concentrations. The band selections and model parameters are based on correlation coefficients and regression analysis between the indices and TSM. The results show that the correlation coefficient of 0.912 between the proxy and TSM is higher than that between any individual index and TSM. To validate the CPTSM model, TSM, turbidity, and reflectance data were collected in the ECS during 4 cruises in 2006 and 2007. The actual TSM concentration was measured by weighing the samples collected on filter papers. Turbidity was measured by a Seapoint Turbidity Meter. The turbidity data with values higher than 750 FTU were re-calibrated using an empirical equation. All turbidity values were converted to TSM concentrations using a linear equation. The in situ reflectance was measured using the above-water method at 459 stations and the in-water method at 146 stations. A total of 87 pairs of reflectance measured by both methods were used for inter-comparison with a relative difference of 4.5%. The reflectance values were used to retrieve TSM concentrations using the CPTSM model. A comparison with in situ measurements gave a mean relative error of 23%. Applying the CPTSM model to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data and analyzing the errors from a match-up dataset of SeaWiFS and in situ data, we found that the average relative error was 24.5%. We propose to use the CPTSM model to map TSM concentrations from satellite data in the ECS.
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A technique is presented for estimating suspended sediment concentrations of turbid coastal waters with remotely sensed multi-spectral data. The method improves upon many standard techniques, since it incorporates analyses of multiple wavelength bands (four for Sea-viewing Wide Field of view Sensor (SeaWiFS)) and a nonlinear calibration, which produce highly accurate results (expected errors are approximately ±10%). Further, potential errors produced by erroneous atmospheric calibration in excessively turbid waters and influences of dissolved organic materials, chlorophyll pigments and atmospheric aerosols are limited by a dark pixel subtraction and removal of the violet to blue wavelength bands. Results are presented for the Santa Barbara Channel, California where suspended sediment concentrations ranged from 0-200+ mg l−1 (±20 mg l−1) immediately after large river runoff events. The largest plumes were observed 10-30 km off the coast and occurred immediately following large El Niño winter floods.
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An algorithm suitable for the determination of chlorophyll and suspended sediment concentrations in coastal waters from Thematic Mapper data has been obtained through a numerical simulation. The computation has been carried out by a three-component model of sea colour derived from in-situ measurements performed in the Gulf of Naples. The algorithm accounts for large deviations from the mean correlation of chlorophyll and sediment concentrations observed in the area. Since spatial and temporal fluctuations of this basic correlation are characteristic of the coastal zone, the proposed procedure could be put to practical use.