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Remote-sensingreflectancecharacteristicsof
highlyturbidestuarinewaters?Acomparative
experimentoftheYangtzeRiverandtheYellow
River
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International Journal of Remote Sensing
Vol. 31, No. 10, 20 May 2010, 2639–2654
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Remote-sensing reflectance characteristics of highly turbid
estuarine waters – a comparative experiment of the Yangtze
River and the Yellow River
FANG SHEN*†‡, MHD. SUHYB SALAMA‡, YUN-XUAN ZHOU†,
JIU-FA LI†, ZHONGBO SU‡ and DING-BO KUANG§
†State Key Laboratory of Estuarine and Coastal Research, East China
Normal University, Shanghai 200062, China
‡International Institute for Geo-information Science and Earth Observation,
Enschede 7500AA, The Netherlands
§Shanghai Institute of Technical Physics, Chinese Academy of Sciences,
Shanghai 200083, China
(Received 1 May 2007; in final form 18 October 2008)
An outdoor tank experiment is carried out to analyse the interrelationships between
remote-sensing reflectance and sediment characteristics in the highly turbid waters of
the Yangtze River and the Yellow River estuaries. The results show that the sensitivity of remote-sensing reflectance to water turbidity is inversely related to suspended
sediment concentration (SSC). SSC estimation in the highly turbid waters (SSC .
0.15 g l-1) is best achieved by using ocean colour ratios, especially the ratio at 810 nm:
700 nm. The effect of particle size of suspended sediment matter (SSM) on the
observed remote-sensing reflectance is significant and depends on wavelengths and
a SSC range. The mineral composition of SSM has a weak effect on observed
reflectance in comparison to that of particle size.
1.
Introduction
Sediment fluxes to the Yangtze River and the Yellow River estuaries play an important role in the geomorphologic evolution of their coastal environments (delta and
mud flat). Suspended sediment matters (SSM) have a great effect on the transparency,
turbidity and water colour of estuarine and coastal waters. Knowledge of the loads,
spatial distribution and physical properties of SSM is, therefore, essential to evaluate
geomorphologic changes and to monitor water quality, since they relate total primary
production to heavy metal and micro pollutants (Vos et al. 1998, Raineya et al. 2003).
Remote sensing offers the only realistic means of acquiring the required measurements to study this important, and often inaccessible, marine ecosystem.
Several studies have demonstrated the capabilities of remote sensing to quantify
marine bio-geophysical parameters using different sensors: e.g. Sea-viewing Wide
Field-of-view Sensor (SeaWiFS) (Tassan 1994, Warrick et al. 2004), Satellite Pour
l’Observation de La Terre (SPOT) High Resolution Stereo sensor (SPOT-HRS)
(Doxaran et al. 2002), Landsat Thematic Mapper (TM) (Islam et al. 2002), Moderate
Resolution Imaging Spectroradiometer (MODIS) (Hu et al. 2004), and Indian Remote
*Corresponding author. Email: [email protected]
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online # 2010 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/01431160903085610
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2640
F. Shen et al.
Sensing (IRS)-1C Wide Field Sensor (IRS-1C WiFS) (Mishra 2004). Remote sensing of
such complex systems (the Yangtze River and Yellow River estuaries) is quite challenging due to the failure of atmospheric correction methods in turbid water and the
empirical nature of the retrieval algorithms, which are limited to a specific range of
concentration, area and season. A generalized retrieval algorithm is, however, hindered
by the large natural variability of sediment concentration, colour, mineralogy, index of
refraction and particle size (Curran and Novo 1988). Significant efforts on improving the
accuracy of satellite-derived suspended sediment concentration (SSC) are therefore
required in such areas. Doxaran et al. (2003) showed, through in situ measurements,
that using a reflectance ratio may reduce the effects of variable sediment types and
illumination conditions on the retrieved values of SSC. On the other hand, Bale et al.
(1994) performed laboratory experiments to study the influences of SSM particle size
distribution and concentration on the spectral reflectance in two estuaries in England.
They showed that small particles of SSM are more efficient for backscattering than large
particles, i.e. inverse relationship between albedo and particle size.
Due to the complex hydrodynamical conditions in the Yangtze River estuary (Shen
et al. 2006), a laboratory experiment is designed to control the various factors
encountered in the field. This paper introduces a new outdoor tank experiment
(using sunlight as an illumination source) on water laden sediments collected from
the Yangtze River and the Yellow River estuaries. The experiment demonstrates the
interrelationships between the physical properties (concentration, particle size distribution and mineral composition) and optical signatures of SSM for extremely turbid
waters e.g. SSC . 300 mg l-1. The experiment and performed analysis introduced in
this paper will provide a profound understanding of the relationship between Rrs (l)
and SSM physical properties, which will improve the accuracy of Earth Observation
(EO) derived SSC products for highly turbid waters.
2.
2.1
Study areas and collection of samples
The Yangtze River estuary
The Yangtze River is the largest river in China and the third largest in the world (after
the Nile and the Amazon). The Yangtze River estuary is an extreme example of highly
turbid, sediment dominated, case 2 waters (figure 1). It was estimated (at the Datong
hydrological site from 1951 to 2000) that its annual average water and sediment inputs
are up to 9.04 1011 m3 and 4.35 108 tonnes, respectively. Due to the complex
hydrodynamic system that controls the Yangtze River estuary, the concentration and
granularity of the SSM have large horizontal variability and heterogeneity.
The magnitude of SSC in the Yangtze River estuary exhibits a steep spatial gradient
from the outer to the inner part and from the Hangzhou bay to the Yangtze River
estuary (Chen et al. 2003). The Yangtze River estuary was roughly divided into four
regions in terms of SSM concentrations (He et al. 1999): (1) highly turbid waters, located
at 121 550 E–122 150 E, with average concentration values of approximately 0.5–0.9 g l-1
and a maximum value up to 2.5 g l-1 in the south passage; (2) moderately turbid waters,
located at 122 150 E–122 300 E. This region corresponds to the principal part of the
Yangtze River delta that is under water. It has average SSC values of about 0.25–0.5 g l-1;
(3) low turbid waters, which corresponds to shallow sea regions with water depth of
15–30 m (. 122 300 E). The average SSC values are about 0.08–0.25 g l-1; (4) clear
waters, e.g. located beyond 30 m water depth, with SSC values below 0.08 g l-1.
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Remote sensing reflectance characteristics
2641
Figure 1. The study area showing hydrological (triangles) and sampling (stars) sites. The Yangtze
River estuary has three branches and four entrances near the East China Sea. The Yellow River
estuary has one major entrance to coastal waters at Bohai bay, northeast coast of China.
The temporal variations of SSC exhibit a clear seasonal and tidal gradient of each
part of the estuary. The inner part has higher SSC values in the summer than in winter,
while the outer estuary shows higher concentration in winter than in summer. The
SSC values vary with the neap-spring tidal cycle, which is consistent with a tidal semimonthly cycle of approximately 14.765 days, typically with higher SSC during spring
tides and lower SSC during neap tides (He et al. 1999, Chen et al. 2003, Zuo et al.
2006). This high spatiotemporal variability of SSC is, in turn, associated with variability in the physical characteristics of SSM. The median particulate diameter (D50) of
SSM decreases gradually from 217.8 mm of the Jiangyin (32 N/120 340 E) to 12.1 mm
of the outer estuary. Offshore, the D50 is 48.4–22.4 mm in the north passage and
22.4–12.1 mm in the south passage. However the D50 has a very large dynamical range
in the turbid maximum-zone (Liu et al. 2007).
A sediment sample (about 30 kg in weight), composed of silt and clay-grade
material, was collected in a barrel from the east tidal flat of the Chongming Island
(see figure 1) in June 2006 and prepared for the experiment. Generally, the D50 of this
sample (collected from the upper tidal flat) is 11.7–42.5 mm (Liu et al. 2003), which is
smaller than particles taken from the lower tidal flat. This can be explained by the
tidal dynamics in the area in which most of the fine particles are not re-washed away
by the tide. An extra quality check is performed to guarantee that the natural status of
the particles (being river-carried) has been preserved. This is realized by comparing
2642
F. Shen et al.
the particle size distribution of the sample to the size distribution of collected SSM
samples (explained in the following paragraph).
Additionally, water samples were collected during different tidal cycles and climate
conditions as follows: sprint/neap and flood/ebb tidal cycles and wet/dry season. It is
anticipated that these findings will contribute to advance our understanding of the
current status of suspended sediment distribution and relevant marine bio-geophysical
parameters and other environmental factors in the Yangtze River and the Yellow River
estuaries.
The Yellow River estuary
The Yellow River is the second largest river in China. The Yellow River (figure 1) is
well known for its high discharge of sediment. It was estimated (on the basis of long
term observation data at the Lijing hydrological observation site of the Yellow River
mouth) that the annual average water discharge into coastal waters is 3.245 1010 m3
from 1950 to 2003, which is 30 times lower than that of the Yangtze River. The
maximum discharge (9.731 1010 m3) occurred in 1964, whereas the minimum river
discharge (0.186 1010 m3) occurred in 1997. The annual average sediment flux of the
Yellow River is about 8.107 108 tonnes, which is two times higher than that of the
Yangtze River, the maximum sediment flux reached 20.97 108 tonnes in 1958 and
the minimum flux was 0.02 108 tonnes in 2001 (from statistical data of the Lijing
hydrological site from 1950 to 2003).
Since the Yellow River estuary has relatively fewer water inputs, with excessive
sediment loads and weak tidal dynamics compared to the Yangtze River, the SSC
distribution in the estuary is therefore influenced by the seasonal variability of water
and sediment inputs. For instance, the average magnitude of sediment flux in the
flood season (about 4 months each year) reached 6.875 108 tonnes and accounted
for 85% of the total amount of the annual flux during the period between 1950 and
2003 (data from the Lijing hydrological site). Figure 2 shows that the runoff and the
sediment discharge exhibited an obvious progressive decrease from the 1950s to
30.00
10
Sediment discharge
25.00
River inputs flux
8
20.00
6
15.00
4
10.00
2
5.00
0.00
1950
1960
1970
1980
Years
1990
2000
River inputs flux (1010 m3 )
Sediment discharge (108 tonnes)
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2.2
0
Figure 2. Annual flux of river inputs and sediment load in the Yellow River estuary from 1950
to 2003 (observed data in the Lijing hydrological site of the Yellow River estuary).
Remote sensing reflectance characteristics
2643
2000s. The ratio of annually averaged sediment load to runoff remarkably increased,
which may indicate a drastic geomorphologic change in estuarine and coastal areas
resulting in a coastline shift (Pang et al. 2000b).
The SSC sharply decreased from about 38.65–45.85 g l-1 in the inner estuary to
0.93–2.53 g l-1 in the outer estuary (Pang et al. 2000a). The fine sand content was
approximately 50–70%, and the clay content was approximately 30–40% (Li et al.
2005). The D50 of SSM was 19.7 mm on average (data from Pang et al. 2001).
A sediment sample (about 50 kg in weight) was collected (in May 2006) and
prepared for comparative experiments. The sample was from a tidal channel on the
tidal flat of the Yellow River delta.
The likely differences in sediment physical properties collected from two different
estuaries facilitate the inter-comparison between their compositions and spectral
signatures through the proposed comparative experiment.
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3.
3.1
Method and data collection
Laboratory analysis: mineral decomposition of SSM
Sediment samples were analysed at the State Key Laboratory of Estuarine and Coastal
Research (SKLEC) for their mineral compositions, organic contents and particle size
distribution. The results showed that the sediment sample from the Yangtze River
estuary (which was visually dark in colour) contained organic and mineral constituents;
the organic fraction accounted for 0.904% of the total materials. The mineral fraction
was composed of 95% light minerals such as quartz and feldspar (92% in total) and
carbonatite (,3%), and 5% heavy minerals such as micas (,1%), anti-weathering
minerals (,3%) and Fe-Mn black metal minerals (1%). The sediment sample from the
Yellow River estuary was visually yellow in colour; the organic fraction accounted for
0.634% of the total materials. The mineral fraction consisted of quartz and feldspar (92%
in total) and heavy minerals (8%) mainly including chlorite (, 1%), micas (, 0.5%),
zoisite and epidote (, 3%), amphibole (2%) and Fe-Mn black metal minerals (1%).
Through x-ray diffraction analysis, the clay sample from the Yangtze River estuary
contained four minerals: montmorillonite (4.17%), illite (66.62%), kaolinite (16.55%)
and chlorite (12.67%). The clay sample from the Yellow River estuary contained, also,
four minerals but with a slight difference in proportion: montmorillonite (4.62%),
illite (65%), kaolinite (16.45%) and chlorite (13.94%).
The magnitude of SSC was determined gravimetrically by filtering the water sample
on 0.7 mm Whatman GF/F glass-fibre filters. All filters were rinsed with Milli-Q water
to remove salts, dried, and then reweighed on a high precision balance.
The particulate diameter of sediment was measured by a LS100Q laser particle
analyser (a product of the American Coulter Company) in the laboratory. The used
method is described as follows: 5–10 g of sediment sample was put into a 50 ml beaker,
then 10 ml of 10% H2O2 was added to remove organic matter and then placed
statically for 24 h, and thereafter 4% [NaPO3]6 with 10 ml was added. After that,
the particles were subjected to ultrasonic vibrations for 15 min.
3.2
Resampling of particle size distribution
Each of the two estuarine samples were classified into two types: fine particles with
diameter D , 40 mm and coarse particles with diameter D . 40 mm. This threshold
(i.e. 40 mm) was selected for two main reasons: (i) SSM with D , 40 mm was
F. Shen et al.
predominant (cumulative volume . 85%) in many areas of the two estuaries. For
instance, through the analyses of 21 in situ collected samples from the Yangtze River
estuary, the cumulative volume with D , 40 mm was . 86%, although there was a
wide diameter range between 0.375 and 213.2 mm. However, it should be noted that
the predominant particle size of sediment could become larger than (8–63 mm) in the
maximum turbidity-zone due to re-suspension of coarser particles (Li et al. 1994); (ii)
the lower threshold value could not be set due to the limitation of the used technical
instrument. Therefore, each raw sample was re-sampled and filtered (through a 40 mm
pore diameter nylon sieve) into two sediment portions of fine (D , 40 mm) and coarse
particles (D . 40 mm). The samples were first immersed in pure water for 3 days at
least. Successively, each portion was separated into a series of sub-samples of 25 g
weight, where each sub-sample was packed into a sealed bag.
In order to examine the outcomes of re-sampling, we analysed the particle size
distribution of each portion with the LS100Q laser particle analyser in the laboratory.
The cumulative volumes of fine particles (D , 40 mm), from both estuaries (95.4%
Yellow and 89.8% Yangtze), are shown in figure 3(a). The cumulative volumes of
Differential volume (%)
6
5
Yellow River estuary
Yangtze River estuary
4
3
2
1
0
1
5
50 100
10
1000
Particle diameter (μm)
(a)
10
Differential volume (%)
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2644
Yellow River estuary
Yangtze River estuary
8
6
4
2
0
1
5
10
50 100
1000
Particle diameter (μm)
(b)
Figure 3. Particle size distribution of filtered sediment samples from the Yellow River (black
line) and the Yangtze River (grey line) estuaries (a) fine-particle size distribution, (b) coarseparticle size distribution.
Remote sensing reflectance characteristics
2645
coarse particles (D . 40 mm), from both estuaries (70.2% Yellow and 84.1% Yangtze),
are shown in figure 3(b). These results imply that the re-sampling could indeed satisfy
the needs of further radiometric measurements.
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3.3
Experiment setup
Comparative experiments and measurements were designed for two cases: (i) different
mineral compositions with similar granularities and (ii) different granularities with
similar mineral compositions. This setup is to facilitate the analysis of observed
remote-sensing reflectance with respect to SSM physical characteristics. Two experimental schemes were designed: (i) fixed particle size and variable compositions; (ii)
fixed composition with variable particle size.
A black experimental tank (height: 1.5 m, diameter of cylinder top: 1.5 m and
diameter of cylinder bottom: 1.4 m) was placed on a spectrally uniform and flat
ground. The experiment was implemented in four stages: (i) optical measurements
were taken respectively with and without natural water filled in the tank; (ii) one
sediment sample 25 g in weight, pre-weighted and prepared before the measuring, was
put into the tank filled with water; (iii) the water body was continually churned up
until close to uniform turbid water. The depth gradient of SSC was measured with a
top-bottom sliding turbidity meter (optical backscatter sensor, OBS-5); (iv) optical
radiometric measurements were acquired when the vertical distribution of SSC was
approximately homogenous. After each optical measurement a water sample was also
collected. The measurements from (ii) to (iv) were repeated with increasing SSC. Note
that the experiment for each case was conducted at the same local time to guarantee
similar illumination conditions, i.e. measuring once per 10 min from 9:30 am to 3:00
pm and four cases conducted respectively on 17, 19, 29 and 30 July 2006 under clear
blue sky conditions and low wind speed.
3.4
Above-water radiometric measurement
Radiometric measurements of in-water and above-water were carried out to estimate the
spectral reflectance. The in-water measurement reached its limit in highly turbid waters
(SSC . 300 mg l-1) where the up-welling radiance signal at . 0.01 m depth was negligible
(i.e. equal to zero or even slightly negative) (Doxaran et al. 2006). Hence, the above-water
radiometric measurement was adopted for determining the remote-sensing reflectance
based on the outdoor tank experiment in this work (see figure 4).
Optical data were recorded with an ASD Field Pro Spectroradiometer (ASD Inc.,
Boulder, CO, USA) from wavelength 350 nm to 2500 nm (at 1.4 nm sampling intervals
in 350–1000 nm spectral domain and at 2 nm intervals in 1000–2500 nm spectral
domain). Before the measurement, the sensor was calibrated for a dark current and a
reference target (a diffuse reflector with a known albedo) in the laboratory and on site.
During the measurement, the stability of the signal was observed. Five spectra of
radiance were respectively recorded for each case.
Glint effects on above-water observations (Hooker et al. 2004) were minimized by
following the measurement method described in Mobley (1999). The main steps of
this method are: (i) nadir measurements of the solar down-welling radiance with a
reference panel; (ii) measuring the up-welling radiance at 40 viewing angle and
azimuth angle difference of 135 ; (iii) sky diffuse scattering radiance was measured
by circumvolving the sensor upwards to the sky with 180 in the same viewing plane.
2646
F. Shen et al.
θ
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ϕ
Figure 4. Setup and illumination viewing geometry of the outdoor tank experiment and
measurement.
Extra attention was paid to perform the target and reference measurements with
small time intervals between them to ensure identical illumination conditions.
The remote-sensing reflectance Rrs with a unit of inverse steradians sr-1 is computed
from the measured water-leaving radiance and downwelling irradiance (Carder et al.
1993, Mobley 1999):
Rrs ð; j; lÞ ¼
Lw ð; j; lÞ
Edþ ðlÞ
(1)
Here, and f specify the viewing zenith and azimuth angles, respectively, and l is
the wavelength. Edþ ðlÞ, in W m-2 nm-1, denotes the downwelling spectral plane
irradiance onto the surface, which can be approximated by the radiance measurement of a grey panel (which has a known diffuse reflectance factorRp (l)). Edþ ðlÞ is
nearly equal to: pLd ðlÞ=Rp ðlÞ(Lee et al. 1996), where the solar downwelling radiance Ld ðlÞ can be directly measured (step I). Lw ð; j; lÞ, in W m-2 sr-1 nm-1, denotes
the water-leaving spectral radiance in the direction (, f), which is estimated from
the total measured radiance following Whitlock et al. (1981) and Lee et al. (1996) as
Lt ðlÞ rLs ðlÞ. Lt ðlÞ is the total radiance above the water surface, and can be
directly obtained (step II), with a viewing nadir angle (e.g. 40 ) and a relative
azimuthal angle f (e.g. 135 ). rLs ðlÞ denotes the surface-reflected part of the
incident sky radiance Ls ðlÞ. Ls ðlÞ can be measured directly (step III); r is the
water-surface reflectance factor that depends on sky conditions, solar zenith
angle, viewing geometry and wind speed. r is assumed to have constant values:
0.0337 for clear blue sky and 0.028 for thin-cloudy sky (Mobley 1999).
Remote sensing reflectance characteristics
4.
Results and discussion
Characteristics of Rrs (l) with respect to SSC variations
Figure 5 shows the measured remote-sensing reflectance in the tank experiment. The
response of Rrs (l) to increased SSM concentrations is wavelength-dependent. The
spectral curve of Rrs (l) presents three peaks at wavelengths from the 400 to 900 nm
domain; a similar result from in situ measurements has been reported with the same
spectral characteristics (Shen et al. 2006). The first spectral peak is located at around
590 nm, the second at around 700 nm and the third at around 810 nm. Rrs at 590 nm is
normally higher than that at 700 nm for SSC , 0.1 g l-1. This relation is reversed for
SSC . 0.15 g l-1, because Rrs ð590Þ tends to saturate while SSC increases. Rrs ð810Þ has
basically low values SSC , 1.5 g l-1, and increases proportional to SSC. High
turbidity is observed in the turbidity maximum zone of the Yangtze River estuary
(maximum SSC up to 2.5 g l-1).
In addition to these spectral peaks (centred at wavelengths 590, 700 and 810 nm),
three other wavelengths (centred at 510, 620 and 709 nm) were selected to analyse the
sensitivity of observed remote-sensing reflectance to SSC, mineral composition and
particle size distribution. The three other wavelengths were selected due to their
importance to ocean colour sensors, e.g. MODIS, SeaWiFs and MERIS, and to
infer water turbidity indicators (Dransfeld et al. 2005). The response of Rrs at these
wavelengths to SSC variation has two phases (see figure 6). The first phase is a rapid
linear increase in reflectance (between 0.03 and 0.05 sr-1) for SSC values between 0
and 0.15 g l-1. The second phase is the slowly logarithmic increase of reflectance
values (0.01–0.02 sr-1) corresponding to the SSC values between 0.15 and 2.5 g l-1.
The results from this experiment and related analysis show that Rrs is very sensitive to
SSC at relatively low concentrations and less sensitive (reaching a saturation level) at
high concentrations of SSM. Large errors are therefore expected from EO products of
SSC in the Yangtze River and Yellow River estuaries.
The sensitivity of Rrs to SSC variations is further assessed using a sensitivity
analysis (SA) approach (Salteli et al. 2000). Let F be a multivariable response function
with respect to the variables Pm ðm ¼ 1; 2; ::; MÞ, the differential sensitivity with l
dimension can be expressed as:
0.08
SSC (g l–1)
0.07
9
9
3.6652
8
8
1.9567
7
7
1.1083
5
6
0.5790
4
5
0.2577
4
0.0877
3
0.0560
2
0.0388
1
0.0222
0.06
6
0.05
Rrs (sr–1)
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4.1
2647
0.04
0.03
3
0.02
2
1
0.01
clear water
0
350
Figure 5.
(SSC).
450
550
650
750
Wavelength (nm)
850
950
Spectral shapes of Rrs (l) at different levels of suspended sediment concentration
2648
F. Shen et al.
0.08
Rrs (sr –1)
0.07
0.06
Wavelength
0.05
510 nm
0.04
590 nm
0.03
620 nm
700 nm
0.02
709 nm
0.01
0
810 nm
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
SSC (g l–1)
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Figure 6.
Rrs (l) at six wavelengths as a function of suspended sediment concentration (SSC).
l Sl hF j Pm i ¼ F Pl
m
(2)
Note that the gradients of F and Pm are assumed to be linear functions. Let F denote
Rrs and Pm denote SSC, and thus the sensitivity will be simplified with S ¼ Rrs =SSC.
Consequently, the sensitivities of Rrs, reflectance ratios and reflectance differences at
different wavelengths are analysed with respect to variable values of SSC (shown in
figure 7).
Figure 7 shows that the sensitivity of Rrs (l) is decreasing with increased values of
SSC. These results are consistent with the aforementioned analysis of figure 6. We can
observe, from figure 7, that the Rrs ratios at typical wavelengths are more sensitive to the
SSC variation than their differences (see figure 7(a)) and the single Rrs (see figure 7(b)).
Furthermore, figure 7 indicates that the Rrs ð700Þ=Rrs ð590Þ ratio is more sensitive than
the Rrs ð810Þ=Rrs ð700Þ ratio at low SSC levels (e.g. , 0.15 g l-1), but at a high SSC
level (e.g. . 0.15 g l-1), the Rrs ð810Þ=Rrs ð700Þ ratio is more sensitive than the
Rrs ð700Þ=Rrs ð590Þ ratio. It was concluded that the Rrs (l) ratios at typical wavelengths
could be better for EO-based retrievals of SSC, and the Rrs ð810Þ=Rrs ð700Þ ratio could
improve EO-based retrieval of SSC values in extremely turbid waters of the Yangtze
River and the Yellow River estuaries.
4.2
Effect of SSM particulate diameter on Rrs (l)
The effect of SSM particle size variations on observed reflectance is analysed by comparing Rrs of fine particles to Rrs (l) of coarse particles in the two estuaries (estimated from
tank experiments in §3). Figure 8 shows that Rrs values of fine particles are notably bigger
than those of coarse particles at corresponding wavelengths. This result is in general
agreement with laboratory experiments reported by Bale et al. (1994). The ratios of Rrs of
fine particles (Rrs_fine) to Rrs of coarse particles (Rrs_coarse) vary between 1 and 3 (see
figure 9), i.e. the Rrs_fine is pronouncedly higher than Rrs_coarse in the 400–900 nm
spectral domain. The ratios appear rather high in the 550–730 nm spectral domain at
low levels of SSC. However, at high levels of SSC (i.e. highly turbid water), the ratios
tend to be low in the visible domain but become rather high in the near-infrared domain.
The possible reason would be that the Rrs response tends to saturate in the visible
spectral domain when SSC reaches a level higher than 0.15 g l-1. It is therefore suggested
Remote sensing reflectance characteristics
2649
100
(a)
Sensitivity
10
1
810 nm/700 nm
0.1
700 nm/590 nm
810 nm – 700 nm
0.01
700 nm – 590 nm
0.001
0.0001
0.01
0.1
1
10
SSC (g l–1)
(a)
(b)
10
Sensitivity
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100
810 nm/700 nm
1
700 nm/590 nm
0.1
510 nm
620 nm
0.01
709 nm
810 nm
0.001
0.0001
0.01
0.1
1
10
SSC (g l–1)
(b)
Figure 7. Comparison between the sensitivity of Rrs values, their ratios and their difference at
different wavelengths to various levels of suspended sediment concentration (SSC). (a)
Sensitivity of Rrs ratios and Rrs differences, (b) sensitivity of Rrs ratios and Rrs .
that the particle size influences the reflectance of water at low turbidity in the visible part
of the spectrum. However, in highly turbid water, the effect of particle size is less
pronounced in the visible domain and increases abruptly in the near-infrared domain
(l . 730 nm). The influence of particle size on Rrs (l) is therefore dependent on wavelength
and on the range of SSC values. Neglecting the effect of particle size on observed
reflectance may lead to over-estimation of EO-derived SSC in low turbidity water.
4.3
Effect of SSM composition on Rrs (l)
Laboratory analysis of SSM samples (§3.1) has shown that the SSM of the Yangtze
River estuary has different mineral compositions and organic fractions than the SSM
from the Yellow River estuary. Here, we discuss whether this variability in SSM
composition affects the observed Rrs (l) at a given particle size and SSC value.
It is found, from comparing the values of Rrs (l) from the Yellow River estuary
(Rrs_yellow) to the Rrs (l) from the Yangtze River estuary (Rrs_yangtze - see figure 10(a)),
that the Rrs_yellow is pronouncedly higher than the Rrs_yangtze in the visible domain (l ¼
2650
F. Shen et al.
0.08
(a)
0.07
Rrs (sr –1)
0.06
0.05
0.04
0.03
0.02
0.01
0
0.01
0.1
1
10
SSC (g l –1)
(a)
0.05
Rrs (sr r –1)
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0.06
(b)
0.04
0.03
0.02
0.01
0
0.001
0.01
0.1
1
SSC (g l –1)
(b)
620nm
510nm
510
nm709nm
620nm
620
nm810nm
709nm
709
nm
Particle diameter <<40
40 µm
µ mat:
at:
510nm
620nm
709nm
510nm
510
nm
620nm
620
nm810nm
709nm
709
nm
Particle diameter >> 40
40µm
µ mat:
at:
810nm
810
nm
810nm
810
nm
Figure 8. Rrs responses to various suspended sediment concentration (SSC) levels at four
wavelengths with fine- and coarse-particle size, respectively. (a) Suspended sediment matter
(SSM) from the Yellow River estuary, (b) SSM from the Yangtze River estuary.
450–710 nm) especially at l ¼ 590 nm for low SSC levels (e.g. , 0.15 g l-1). Whereas the
Rrs_yellow is lower than the Rrs_yangtze, remarkably at l ¼ 700 nm for high SSC levels
(e.g. . 0.15 g l-1). However, the Rrs_yangtze always appears higher than the Rrs_yellow in the
near-infrared domain (l . 710 nm) especially at wavelength l ¼ 810 nm. The possible
explanation is that the backscattering of the SSM in the visible domain is not only related
to the composition itself but also to the concentration level. However, the backscattering
in the near-infrared domain appears to be related purely to the composition itself.
The ratio Rrs_yellow / Rrs_yangtze is calculated for the different concentration levels in
order to investigate the effect of each component (composition or particle size) on Rrs (l).
The ratio Rrs_yellow / Rrs_yangtze varies within about 0.9–1.3 in the visible domain and ,
0.9 in the near-infrared domain (see figure 10(b)). This may indicate that the effect on Rrs
(l) induced by SSM particle size is larger than that induced by SSM compositions.
Remote sensing reflectance characteristics
2651
3
Rrs_fine / Rrs_coarse
(a)
SSC (g l –1)
2.5
2
3
1.5
4
1
400
500
600
2
0.034
2
0.057
3
0.112
4 0.414
1
5 0.792
5
700
1
800
900
Wavelength (nm)
(a)
)
Rrs_fine / Rrs_coarse
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3
SSC (g l –1)
10
2.5
2
1.5
1
400
9
7
500
600
700
Wavelength (nm)
800
0.030
7
0.065
8
0.091
9
0.262
10 0.408
8
6
6
900
(b)
Figure 9. Ratios of Rrs of fine particle to that of coarse particle under equivalent suspended
sediment concentration (SSC) levels. (a) Suspended sediment matter (SSM) from the Yellow
River estuary (b) SSM from the Yangtze River estuary.
However, the main remaining difficulty is identifying the component that has the greatest effect on Rrs (l).
5.
Conclusions
An outdoor tank experiment was conducted for the highly turbid waters of the
Yangtze River and the Yellow River estuaries. Remote-sensing reflectance of variable
suspended sediment concentration, mineral composition and particle size distribution
were analysed, compared and discussed in this work.
It was found that the response of observed Rrs to SSC variation has two phases. Rrs is
very sensitive to SSC at low values so that SSC could be accurately estimated with
radiometric measurements; however at high SSC levels, the sensitivity of Rrs to SSC is
largely reduced. This has important implications on the accuracy of EO derived SSC
products in the Yangtze River and Yellow River estuaries. In addition, Rrs (l) ratios at
typical wavelengths could be more appropriate for the retrieval of SSC; in particular the
ratio Rrs ð810Þ=Rrs ð700Þ has a potential to improve SSC retrievals in very turbid waters.
2652
F. Shen et al.
0.06
SSC (g l–1)
0.05
0.2607
0.2577
Rrs (sr–1)
0.04
0.1058
0.0967
0.03
0.0868
0.02
0.0877
0.0438
0.01
0.0402
0
350
450
550
650
750
850
950
Wavelength (nm)
(a)
(b)
Rrs_yellow/Rrs_yangtze
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1.5
1.3
Mean SSC (g l–1)
1
2
1.1
1
3
2
0.1013
0.2592
4 0.2592
3
4
0.9
0.0420
0.0420
0.0873
0.0873
0.7
0.5
400
500
600
700
800
900
Wavelength (nm)
(b)
Figure 10. The characteristics of Rrs and Rrs ratios of two types of suspended sediment matter
(SSM). (a) Rrs characteristics with a pair of equivalent levels of suspended sediment concentration (SSC) from the two estuaries. Solid line represents Rrs of the Yantze River estuary, dashed
line represents Rrs of the Yellow River estuary. (b) Ratios of Rrs of the Yellow River estuary to
that of the Yangtze River estuary.
The effect of particle size on Rrs (l) is significant, with a dependency not only on
spectral wavelength but also on SSC ranges. The ratio of Rrs of fine particles to that of
coarse particles is rather high in the visible and near-infrared spectral domain in low
turbidity water. In highly turbid water, however, the ratio is lower in the visible
domain and abruptly higher in the near-infrared domain. Particle size of SSM has,
therefore, considerable effects on derived SSC values from above-water radiometric
measurements. Comparative analysis has shown that Rrs (l) is more affected by SSM
particle size than SSM composition. However, difficulty still remains in identifying
which component is the main modulator of the observed Rrs (l).
The contribution of such fundamental experiments will be helpful to establish a
thorough understanding of the relationship between Rrs (l) and SSM physical properties,
and will enable improvement of the accuracy of the forthcoming satellite SSC retrieval
models for the highly turbid waters of the Yangtze River and Yellow River estuaries.
Remote sensing reflectance characteristics
2653
Acknowledgements
This work was funded by the Key Project of Chinese Ministry of Education (no. 105076),
the National Key Basic Research Project of China (grant no. 2002CB412403) and the
National Natural Science Foundation of China (no. 40871165). The authors are very
grateful to Qingxiang Cheng, Yi Meng, Jianguo Qu and Lingxiang Wang for help
in sediment sampling and laboratory analysis. We sincerely thank Dr Chris Mannaerts,
Dr Zoltan Vekerdy and Ms Wiwin Ambarwulan for helpful discussions and Dr Curtis
D. Mobley for instructive suggestion in above-water radiometric measurements. The
comments and suggestions of anonymous reviewers and the Editor are greatly appreciated. This work was also supported by the China Scholarship Council and the
International Institute for Geo-Information Science and Earth Observation (ITC), the
Netherlands.
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