Estimating Suspended Sediment Concentration in Turbid Water Using Remotely Sensed Data
Asif Mumtaz Bhatti 1, Seigo Nasu 2 and Masataka Takagi 3
1

PhD Student, Department of Infrastructure Systems Engineering,, Kochi University of Technology,
Tosayamada-cho, Kami-City, Kochi, 782-8502,Japan. asif_engr@yahoo.com
2, 3Professor, Department of Infrastructure Systems Engineering, Kochi University of Technology, Kochi,
Japan.
ABSTRACT
Remote sensing is proved to be a useful tool to provide an instantaneous and synoptic view ofsuspended sediments in rivers & estuaries. Turbidity is not a uniform parameter, either spatially or
temporally. The objective of the research study is to estimate suspended sediments in turbid sedimentdominated waters from high spatial resolution remotely sensed data. In this study, laboratory experiments
and simulated Landsat 7 ETM data is used to establish relationship between the hyperspectral remotesensing reflectance signal and suspended sediment concentration (SSC). The indoor spectral reflectance wasmeasured 1m above the water surface using hyperspectral Field SpecPro FR Spectroradiometer in theelectromagnetic spectrum region of 400 nm to 900 nm. The experiments were performed under controlled
condition keeping all environmental parameters constant. Model was developed to quantify the suspended
sediment concentration. The techniques used were band ratioing and regression modeling. Results from thestudy indicate that strong correlation exists between the suspended sediment concentration and ETM bands 3& 4. Variations of quantity and characteristics of suspended sediment affect the reflectance signal and limit
the accuracy of suspended sediment measurement. However, remotely sensed data makes it possible toefficiently monitor the seasonal distribution and concentration of suspended sediment in the water bodies.
Keywords: Remotely sensed data, Suspended sediment, Optimum range
1.

Introduction

Sustainable development demands a deeper
knowledge of the interaction among economy,society and water resources. Monitoring ofSuspended Sediment Concentration (SSC), in
reservoirs, rivers, coastal waters and estuaries isimperative for proper water resources andenvironmental management. Sediments, which filllakes, reservoirs, and dams, are one of the mostimportant environmental problems throughout theworld. Most of the sediment which is eroded fromthe land surface is in the form of fine particles which
are transported in water courses as a suspended load.
High concentrations of suspended sediment in water
is a critical element in the economic feasibility of a
project and could shorten the useful life of many
reservoirs & dams. The design and sizing of many
hydraulics structures require a pre-determinedknowledge of the quality and quantity of suspended
sediments. This necessitates a comprehensive studyof suspended sediments, quantitative relation of
sediment to the flow of water, sediment carrying
capacity of rivers, and the quantity of total sedimentdeposited in reservoirs.
The most often cited estimates of the global
suspended sediment discharge to the ocean range
between 15×109 t·yr-1 to 20×109 t·yr-1 where the best
estimate may be about 20××109 t·yr-1 [1], of which

over 25% is considered to be trapped by dam
reservoirs [2].
Remote sensing holds potential formonitoring and estimating suspended sediments insurface water. Several studies have been conductedto address the impact of suspended sediments on
the spectral profile of surface waters. Most of thesestudies focus on the relationships between spectralreflectance and suspended sediment concentrations
in surface waters. Few studies actually address how
different sediment types and size affect spectralreflectance [3] [4] [5]. The strength of remotesensing techniques lies in their ability to provide
both spatial and temporal views of surface water
quality parameters that is typically not possible fromin situ measurements.
It is imperative to determine the quantity,
distribution, type of suspended sediments, andoptimal wavelength to enhance the reliability andapplicability of remotely sensed data for water
quality monitoring. Current water quality techniques
involve in-situ measurements and/or the collectionof water samples for subsequent laboratory analyses.
Remote sensing makes it possible to monitor thewater bodies effectively & efficiently and,identifying areas with significant water quality
problems. Remote sensing (hyperspectral, satellite
and airborne platforms) is a tool that addresses

problems of a regional or global nature. It is an
economical way to monitor spatial or temporalchange in terrestrial areas and water bodies, because
it can monitor large areas in a short time on arepetitive basis. It is most successfully used as partof a multidisciplinary approach to addressing natural
resource questions.
Different channels have their own properties
and show different spectral characteristics. Rationalfeature selection is imperative for analysis andinformation extraction of hyperspectral data.
Research on processing, analysis and information
extraction of hyperspectral data should be
strengthened to determine more useful information,
make full use of the advantage and potential of
hyperspectral remote sensing technology andpromote the development of new and important
technology [6]. In order to investigate the effects ofvarying concentrations and composition ofsuspended sediments on the spectral signature ofsurface waters, series of controlled experiments wereconducted. In the present research work thehyperspectral data of different suspended sediments,
spectral characteristics, and correlation between
different bands is comprehensively analyzed. On thebasis of analysis, optimum band selection andfeature extraction from hyperspectral remote sensing
data was proposed.
2.

Methods

The experiments were conducted indoor in a
black painted room at Kochi University ofTechnology, Japan and Center for Advanced Land
Management Information Technologies (CALMIT),
University of Nebraska- Lincoln, USA. A
hyperspectral Field SpecPro FR Spectroradiometer(Analytical Devices, Inc., Boulder, CO) was used
to collect the upwelling radiance from the water
surface.
In the present research work three types ofsoils were taken into account to serve as suspended
sediments for the experiments. The first was silty redsoil collected from the central area of Okinawa
Island (coordinates: 26º23´N and 127º73´E) whichcontained about 60 percent of silt and only 10
percent sand while the second type Kaolin was
readily available from the market. The third typesilty red soil (50%) and kaolin (50%) was preparedat the laboratory to elucidate the effect of colour andtexture of soil on the spectral reflectance. The silty
red soil sample was air dried and manually sieved toensure a uniform sediment size. The sieved soil
samples were weighted to produce fifty
concentrations of suspended sediments in each caseranging from 20 to 1000 mg/l. Initial reflectancereadings were taken for the water tank prior to theaddition of any suspended sediments and sediments
were added systematically to the water-filled tank toenable spectra to be collected for a range of SSC. To
study the SSC and reflectance relationship, the meanof ten scans was used for analysis.
The soil sediments were kept in suspension
by manually stirring at regular intervals. The depthof water column was 60 am and was kept constantfor all experiments The water tank was scanned
within 15 seconds of addition of sediments in orderto minimize settling of input sediments. The sensor
of spectroradiometer was positioned perpendicular at
the center of the tank to achieve nadir view. The
distance of the sensor from the water surface was
kept fixed at the height of 1 meter above the sample,
yielding an instantaneous field of view (FOV) 1.75
cm. For this study, data from 400 to 900 nm wasused because noise was pronounced at wavelength
shorter that 400 nm and longer than 900 nm. Two
500 watt tungsten halogen lamps uniformly
illuminated from 1 meter above water surface with abeam inclination of 30o was the sources ofillumination during the experiment. The inner wallsof the water tank were painted black to minimize‘bottom’ effects and extraneous reflectance.
Radiation input to the ASD FieldSpec FR
spectrometers is through a fiber optic bundle, 5meters in length. The fiber optic cable provides theability to quickly and easily point the spectrometerfield of view at different targets, especially whenusing the pistol grip. Measurements pertaining to theunderwater irradiance at certain depth with varying
sediment loads were made using four Li-192SA
underwater quantum sensors.
Reflectance was calculated as a simple ratio
between target and reference panel using following
equation.

% R (λ ) =L (λ )× Cal ( λ )S (λ )

Where L (λ) is the radiance measured fromthe water surface, S (λ) is the radiance from thereference panel measured under the same
illumination conditions and Cal (λ) is the calibration
factor for the reference panel. The reference plaque
was a Labsphere white Spectralon of dimensions
12.5cm x 12.5cm which was specially designed andcalibrated to reflect natural white light withminimum absorption. Using this method, allparameters that are multiplicative in nature, such as
the spectral irradiance of the illumination source andthe optical throughput of the field spectrometer, andpresent in both the spectral response of a reference
sample and the target material, are mathematicallyeliminated.

In addition to study the response of spectralreflectance, irradiance and extinction coefficientwith various types and concentration of suspended
sediments, the relationship between the SSC andsimulated Landsat ETM bands were also examined.
3.

Results and discussion

3.1

Response of Reflectance, Irradiance andExtinction coefficient with Suspended
Sediments

Suspended sediments can be divided broadly
into organic and inorganic sediments. Inorganic
sediments (tripton) originate from soil erosion andland slides in the upstream catchments. There is no
standard algorithm for the estimation of suspended
sediments due to the complex nature andcomposition of the suspended sediments. The
spectral reflectance of water with different types andconcentration of suspended sediments is shown infigure 1. The reflectances tend to increase with theincrease in SSC, with a few minor exceptions. At
wavelengths shorter than 500 nm, visual separationof spectral curves is more difficult in case of silty
red soil as compared to Kaolin. At levels of 600 mg/l
SSC and above, the spectral profile becomessomewhat irregular in all cases, suggesting that therelationship between reflectance and SSC was foundto be nonlinear.
Maximum reflectance was observed in thevisible domain (Band 1, 2, and 3 of Landsat TM)

40

1000 mg/l

30

KaolinKaolin 50% and Silty
red soil 50%

20

Silty Red Soil

10

800mg/l30600mg/l25

400mg/l2015200mg/l
100mg/l105Clean water

0

0
400

450

500

550

600

650

700

750

800

850

400

900

450

500

550

600

650

700

25

25

Reflectance (%)

1000mg/l

15500mg/l

10200mg/l5

Clean water

0
400

450

500

550

600

650

800

850

900

Silty red soil

Kaolin 50% and Silty red soil 50%

20

750

Wavelength (nm)Wavelength (nm)

Reflectance (%)

Kaolin1000mg/l

35

Reflectance (%)

Reflectance (%)

40

and high absorption is in the NIR (Band 4 of Landsat
TM). With the increase in the suspended sediments
spectral reflectance increase and peak reflectanceshifts towards longer wavelengths. The qualitative
nature of the spectral curves are similar in all cases,
however, there is considerable variance in themagnitude of spectral reflectance.
The optical properties of water are functionsof underwater irradiance, such as the verticalattenuation coefficient for downward irradiance or
irradiance reflectance. The quantum sensors wereplaced first at the depth of 30 cm and then 60 cm
from the water surface, with one facing upward tomeasure the downwelling irradiance (Edo) beneaththe surface and the other facing downward tomeasure the upwelling irradiance (Eu). The quantum
instrument record amounts of photosynthetically
active radiation (PAR-400 to 700 nm) in the water,
measure photosynthetic photon flux density (PPFD)
in micromoles per second per meter squared (μmols1 2m ).
The measurements Edo, underwater downwelling
irradiance at depth (Ed) , upwelling irradiance fromthe tank bottom and water column (Eu) and PAR
reflectance form the water surface R, in conjunction
with variable SSC, allowed us to examine the energy
budget for PAR in the water. The effect of depth and
SSC on upwelling irradiance (figure 1.2) was also
examined. Eu is a complex parameter because it
should be dependent on the total radiation incident tothe surface, SSC in the water column and any
reflective effect of the bottom itself [7]. When

20
15

1000mg/l

10500mg/l
200mg/l5

Clean water

0700

Wavelength (nm)

750

800

850

900

400

450

500

550

600

650

700

750

800

850

900

Wavelength (nm)Figure 1: Spectral reflectance of water with different types and concentration of suspended sediments

100

0.8

Ea

T r a n sm i t t e d , R e f le c t e d &A b so r b e d E n e r g y ( % )R a dia nc e(µ m o l m -2 s -1 )

1

80

a

0.6

60
40

0.4

b

0.2

20

Er

0

0
0

0

100 200 300 400 500 600 700 800 900 1000
SSC (mg/l)

Et

100 200 300 400 500 600 700 800 900 1000
SSC (mg/l)

Figure 1.2: Upwelling irradiance (Eu) at 30 cm (a)& 60 cm (b) depth from water surface withvarying SSC

Figure 1.3: PAR Budget with varying SSC

electromagnetic energy reaches the surface water
body, it may be reflected, absorbed, or transmitted.
The interrelationship among these three processes
can be expressed as

The vertical extinction coefficient kquantifies the “quenching” of light as it passes fromthe water surface to the quantum sensor at the depthof 60 cm. The relationship between SSC and k isimportant because the level of turbidity of a water
body can be measured by means of computing k,instead of measuring SSC. The results ofrelationship between SSC and k are summarizedgraphically as figure 1.4. Suspended sedimentconcentrations can be estimated by extension
coefficients using a linear regression model of theform

Ei = Ea + Et + Er
Where Ei is the incident energy; Ea is absorbed
energy (100-Et-Er); Et is transmitted energy(Ed/Edo); and Er is reflected enegery (R- Eu /Edo). Theinterrelationship involving PAR transmittance,
reflectance, and absorption (in percent) with varying
SSC is shown in figure 1.3. Both the downwellingand upwelling irradiance below the water surface
decreased with increasing SSC.
The extinction coefficient (k) is derived
from the Beer-Bouger Law and can be calculatedusing the following equation.
k = [ln (Edo – Ed)] /d
Where Edo is the irradiance at the surface, Ed
is the irradiance at depth and d is the distance
between the sensor at depth and the sensor at thesurface.
Extinction Coefficien t (at 6 0 cm
Depth)

0.16
0.14
2

0.12

R = 0.9807

0.1

k = 9E-05 (SSC) + 0.021
3.2

Correlation between
Bands and SSC

simulated

ETM

Numerous researchers have analyzed therelationship between SSC and remotely sensed data,
especially Landsat multispeatral scanner (MSS) andThematic Mapper (TM). In order to investigate theapplicability of satellite data to measure precisely the
SSC, the spectroradimeter data between 400 to 900
nm were integrated to spectral width of Landsat
ETM Band 1 (450 to 520 nm), Band 2 (520 to 600nm), band 3 (630 to 690 nm) and Band 4 (760 to 900nm). The integration to the satellite bands wasapproximated by the following summation:
Band averaged reflectance (%) = Σ Ri /n

0.08
0.06
0.04
0.02
0
0

100 200 300 400 500 600 700 800 900 1000

SSC (mg/l)

Figure 1.4: Relationship between SSC andextinction coefficient

Where Ri is the reflectance in the ith
spectral band of the spectroradiometer and n is thenumber of spectroradiometer bands integrated persatellite band width.
The simulated ETM bands, individually andin combination, was used with regression techniques
to examine the relationship between reflectance and
SSC to provide solution for the field application ofremotely sensed data for assessment of suspended
sediments. The correlation coefficients (R2) werecomputed between SSC and simulated ETM band
(Table 1).

that the reflectance ratio was strongly correlated toSSC. This linear relationship is based on reflectanceratio between Band 4 (NIR) and Band 2 (Visible) as
1000

Table 1: Correlation between simulated ETM bands
and SSC
R
0.90
0.97
0.98
0.99

S S C (m g /l)

TM Bands
1
2
3
4

800

2

2

600

R = 0.9971

400
200

The NIR region (Band 4) seems to be more
useful than the visible region for measuring the SSC
in water. The spectral reflectance, in the NIR domain(Band 4), as a function of varying SSC in water isshown in figure 1.5. The selection of the best
20Band 4R e f le c t a n c e

16

0
20

30

40
50
60
70
80
Reflectance % (Band 4/Band 2)

90

100

Figure 1.6: Correlation between Band 4/Band 2
and SSC
depicted in the figure 1.6.
SSC = 1150 (Band 4 / Band 2)2+101.5 (Band4 /Band2)-112.9

122

R = 0.9981

8
4

It was observed that in case of Multispectral
remotely sensed data (Landsat ETM), the ratios ofband 4 & band 2 plotted against SSC will provide
solution for the assessment of suspended sediments.

00

100 200 300 400 500 600 700 800 900 1000
SSC (mg/l)

Figure 1.5: Correlation between simulated
TM bands and SSC
regression equation for measuring SSC is made bycomparing the correlation coefficients. The
reflectance of band 4 can be used to develop a linear
regression model of the following form forestimation of SSC.
SC (mg/l) = (R (Band 4) – 0.0102 )/ 0.0132
3.3

Development of band ratio model

The use of band ratio technique is very
common in remote sensing measurements. The
advantage of using ratios over absolute values ofradiance or reflectance is that they correct some ofthe effects of measurement geometry andatmosphere. Band ratio model was developed to
examine the relationship between suspended
sediment and ratio of reflectance. The reflectance in
NIR (Band 4) increased with increasing SSC.
However, no significant correlation was foundbetween visible (Band 2) and SSC. It was observed

4.

Conclusions

The research summarized in this paper leads
us to the following useful findings 1) In the visible
domain, with increase in SSC, there is small increase
in reflected energy compared to a large increase inenergy absorbed; 2) Between 700 to 900 nm (NIR)
the reflectance increases more uniformly withincreased SSC. Thus, the optimum wavelength formeasuring SSC is NIR (Band 4 of Landsat ETM).
However, visible wavelength (Band 2) is useful for
qualitative point of view to characterizing anddifferentiating suspended sediments. 3) The shapeand magnitude of the spectra reflected from water isstrongly influenced by type and amount ofsuspended sediments. 4) The spectroradimeter data,
integrated to into the band width of Landsat TM,
allow accurate assessment of SSC. Effect ofsuspended sediments distribution with depth andapplication of remotely sensed data with different
environmental condition needs to be explored for
future research. It is further concluded thatsustainable water resources management in an
organized and coordinated way will be key measures
to sustain the economic development. In the future,
therefore, it will be important to incorporatesediment assessment as an integral part of water
resources and environmental planning, development
and management. Without this approach the
problems of the water & environmental sector aregoing to compound in the future.
Acknowledgments
The research work was financially supportedby the Kochi University of Technology, Kochi Japan.
The assistance of all members of Nasu Lab isgratefully acknowledged.
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