EFFECT OF SUSPENDED SEDIMENT CONCENTRATION ON REMOTE SENSING
REFLECTANCE AND LIGHT PENETRATION DEPTH
Asif Mumtaz Bhatti 1, Seigo Nasu 2 and Masataka Takagi 3

ABSTRACTThe purpose of the research was to investigate the effect of suspended sediment concentration on
remote sensing reflectance and depth of light penetration in the turbid water. Turbidity is not a uniform
parameter, either spatially or temporally. Laboratory based experiments were performed to establish
relationship between the hyperspectral remote sensing reflectance signal and suspended sedimentconcentration (SSC). The indoor spectral reflectance was measured 1m above the water surface using both
hyperspectral Field SpecPro FR Spectroradiometer and quantum sensor. The experiments were performed in
the electromagnetic spectrum region of 400 nm to 900 nm keeping all environmental parameters constant. A
relationship for PAR (Photosynthetically Active Radiation) transmittance, reflectance and absorption withvarying levels of SSC was illustrated. The reflectance and diffuse attenuation coefficient increase with theincrease in SSC. The first derivative of peak reflectance, at 827 nm, correlated strongly with SSC than thepeak reflectance itself. Model was developed to quantify the suspended sediment concentration.

Keywords: Remotely sensed data, Suspended sediment, First Derivative, NIR
1.

Introduction

Water quality of reservoirs, rivers, coastal waters and estuaries undergoes significant changes owing
to many influencing factors. Sustainable development demands a deeper knowledge of the interaction among
economy, society and water resources. One important water quality issues is suspended sediments
originating from soil erosion. Soil erosion is a widespread problem causing the deterioration of both land &water resources. Sediments, which fill lakes, reservoirs, and dams, are one of the most importantenvironmental problems throughout the world. High concentrations of suspended sediment in water are a
critical element in the economic feasibility of a project and could shorten the useful life of many reservoirs &dams. Monitoring of suspended sediments in water bodies is imperative for proper water resources andenvironmental management. Suspended sediment load carried by streams varies considerably both in quality
and quantity as it comes from a variety of sources. The major portion of suspended sediments is caused bysoil erosion. However, certain amount also originates from weathering of rocks from beds and banks ofstream. Most of the sediment which is eroded from the land surface is in the form of fine particles which aretransported in water courses as a suspended load.
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]. Identification of these sources and quantifying theireffect is a major task for environmental technologist and water resources planners [3]. This necessitates acomprehensive study of suspended sediments, quantitative relation of sediment to the water flow, and thequantity of total sediment deposited in reservoirs. Remote sensing of water quality is appealing to theresource managers because it offers not only multi-temporal spatial data but also synoptic view that is notpossible through exiting conventional, time consuming and expensive analytical data collecting systems.
Several studies have been conducted to address the impact of suspended sediments on the spectral profile ofsurface waters. Most of these studies focus on the relationships between spectral reflectance and suspended
sediment concentrations in surface waters. Few studies actually address how different sediment types andsize affect spectral reflectance [4] [5] [6].
It is imperative to determine the quantity, distribution, type of suspended sediments, and optimal wavelength
to enhance the reliability and applicability of remotely sensed data for water quality monitoring. Remotesensing makes it possible to monitor the water bodies effectively & efficiently and, identifying areas withsignificant water quality problems.
____________________________________________1

PhD Student, Department of Infrastructure Systems Engineering,, Kochi University of Technology,
Tosayamada-cho, Kami-City, Kochi, 782-8502,Japan. asifmumtaz.bhatti@kochi-tech.ac.jp
2, 3Professor, Department of Infrastructure Systems Engg., Kochi University of Technology, Kochi, Japan.

The incident energy on the water surface is reflected, absorbed, and/or transmitted. The transmitted,
absorbed and scattered energy vary with quality and quantity of suspended sediments in water bodies. In
remote sensing techniques the reflected energy is measured, carrying some information of the object fromwhich it is reflected. Reflectance is function of many influencing factors that caused the change inreflectance signals. Different channels have their own properties and show different spectral characteristics.
Rational feature selection is imperative for analysis and information extraction of hyperspectral data.
Research on processing, analysis and information extraction of hyperspectral data should be strengthened todetermine more useful information, make full use of the advantage and potential of hyperspectral remotesensing technology and promote the development of new and important technology [7].
While dealing with suspended sediments, the characteristics of the sediments including size, shapeand texture of the particles must be taken into consideration. In order to investigate the effects of varying
concentrations and composition of suspended sediments on the spectral signature of surface waters, series ofcontrolled experiments were conducted. In the present research work the hyperspectral data of different
suspended sediments, spectral characteristics, and correlation between first derivative and SSC iscomprehensively analyzed.

2.

Experimental Methodology

In this study four different types of soils were used to evaluate the effect of particle size, shape andtexture variation on remote sensing reflectance. First type of soil, silty red soil was collected from the central
area of Okinawa Island (coordinates: 26º23´N and 127º73´E). While the second type kaolin was readily
available from the market. The third type silty red soil (50%) and kaolin (50%) was prepared at thelaboratory to elucidate the effect of colour and texture of soil on the spectral reflectance. Also, vary fine sandwas used to study the effect of size and texture on reflectance spectra. All types of soil samples were air
dried and manually sieved to ensure a uniform sediment size. The experiments were conducted indoor in a
black painted room at Kochi University of Technology, 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 upwellingradiance from the water surface.
The sieved soil samples were weighted to produce fifty concentrations of suspended sediments
ranging from 20 to 1000 mg/l. Initial reflectance readings were taken for the water tank prior to the additionof any suspended sediments and sediments were added systematically to the water-filled tank to enable
spectra to be collected for a range of SSC. To study the SSC and reflectance relationship, the mean of ten
scans was used for analysis.
The soil sediments were kept in suspension by manually stirring at regular intervals. The depth ofwater column was 60 am and was kept constant for all experiments The water tank was scanned within 15
seconds of addition of sediments in order to minimize settling of input sediments. The sensor ofspectroradiometer was positioned perpendicular at the center of the tank to achieve nadir view. The distanceof 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 was used because noise
was pronounced at wavelength shorter that 400 nm and longer than 900 nm. To illuminate the tank uniformly,
two 500 watt tungsten halogen lamps illuminated from 1 meter above water surface with a beam inclinationof 30o. All sides of the tank were painted black to minimize potential bottom and side effects. Radiation
input to the ASD FieldSpec FR spectrometers is through a fiber optic bundle, 5 meters in length. The fiber
optic cable provides the ability to quickly and easily point the spectrometer field of view at different targets,
especially when using the pistol grip. Measurements pertaining to the underwater irradiance at certain depthwith 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.
L (λ )R (λ ) =× Cal(λ )S (λ )
Where L (λ) is the radiance measured from the water surface, S (λ) is the radiance from the reference
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 and calibrated to reflect natural white light with minimum absorption.

2

3.

Results and Discussion

3.1

Effect of SSC on Remote Sensing Reflectance

Reflectance is the function of quantity and characteristics of suspended sediments. There is no
standard algorithm for the estimation of suspended sediments due to the complex nature and composition of
the suspended sediments. The spectral reflectance of water with different types of suspended sediments andconcentration of 1000mg/l is shown in figure 1. The reflectance tends to increase with the increase in SSC,
with a few minor exceptions. The wavelengths of peak reflectance were plotted against SSC (Figure 2).
When SSC is low the peak reflectance occurred at short visible wavelengths, but when SSC is high the peak
0.4
0.35

KaolinW avelen gth (n m )R efle cta n ce

0.3
0.25
0.2

Kaolin 50%-Silty Red Soil 50%

0.15
0.1

Silty Red Soil

0.05

Fine Sand

0
400

500

600

700

800

700
675
650
625
600
575
550
525
500

900

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

Wavelength (nm)Figure 1: Spectral Reflectance of water withdifferent types of soil

Figure 2: Wavelength of peak reflectance withvarying SSC

reflectance shifted towards longer wavelength. Below 400 mg/l, the reflectance occurred around 575 nmwith only minor differences. Above 400 mg/l the peak reflectance shifted to 675 nm and increase slightly
with increase in sediment load. Maximum reflectance was observed in the visible domain and highabsorption is in the NIR. With the increase in the suspended sediments, spectral reflectance increase andpeak reflectance shifts towards longer wavelengths. The spectral reflectance of silty red soil is plotted against
SSC as depicted in figure 3. The qualitative nature of the spectral curves are similar in all cases, however,there is considerable variance in the magnitude of spectral reflectance. The low reflectance value is due tothe black coated inner walls of the tank, which absorb much of the incident light. At levels of 600 mg/l SSC
and above, the spectral profile becomes somewhat irregular in all cases, suggesting that the relationshipbetween reflectance and SSC was found to be nonlinear.

0.14

0.14

0.12

0.12

900

0.04

800

0.02
1000

600

500
400

SSC (
mg/l)

200

0

700

0.00

600
800

900
800

0.02

700

0.00

0.06

800

400

600
600

500
400

SSC (
mg/l)

200

400

Figure 3: Spectral reflectance of water with varying concentration of silty red soil

3

h(
nm
)

0.04

0.08

W
av

0.06

ele
ng
t

Reflectance

0.10

0.08

W
av
ele
ng
th
(n
m)

Reflectance

0.10

The first derivative of a spectral reflectance can be defined as the rate of change of reflectance withrespect to wavelength (dy/dx). The spectral data were partitioned into narrow bands each having 5nm width.
The derivatives were computed by dividing the percentage reflectance difference between last and first
percentage reflectance value of each narrow band by the wavelength interval separating them. The derivative
spectra seem visually separable below SSC concentration of 500 mg/l with peak separation occurring at 827
nm (Figure 4). However, above 500mg/l, the curves seem visually identical.

0.16
0.12
0.08
0.04
0
-0.04 400
-0.08
-0.12
-0.16

0.16

100 mg/l to 500mg/l

0.08

F ir s t D e r iv a t iv e

F ir s t D e r iv a tiv e

0.12
0.04
0
-0.04400

500

600

700

800

900

-0.08
-0.12
-0.16

600 mg/l to 1000mg/l

500

600

700

800

900

Wavelength (nm)Wavelength (nm)Figure 4: First derivative spectra with varying SSC

The derivatives tended to be positive in visible domain and shift towards negative in the NIR domain. The
regression model was developed in order to examine the relationship between first derivative and SSC(Figure 5). The inverse second order equation of the following form can be used to estimate the SSC.
SSC (mg/l) = a + b/x +c/x2
Where a, b and c are regression coefficients and x is the derivative reflectance of the selected wavelength.
1000

SSC (mg/l) = -0.186+44.4x-2917x2
R2 = 0.96

SSC (mg/l)

800

600

400

200

0
-0.16

-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

First Derivative

Figure 5: Correlation between SSC and first derivative at 827nm

3.2

Effect of SSC on Depth of Light Penetration

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 metersquared (μmols-1m2). The quantum sensors were placed first at the depth of 30 cm and then 60 cm from thewater surface, with one facing upward to measure the downwelling irradiance (Edo) beneath the surface andthe other facing downward to measure the upwelling irradiance (Eu). The optical properties of water are
functions of underwater irradiance, such as the vertical attenuation coefficient for downward irradiance or
irradiance reflectance.
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 withvariable SSC, allowed us to examine the energy budget for PAR in the water. The effect of depth and SSC

4

on upwelling irradiance (figure 6) was also examined. Eu is a complex parameter because it should be
dependent on the total radiation incident to the surface, SSC in the water column and any reflective effect of
the bottom itself [8]. When electromagnetic energy reaches the surface water body, it may be reflected,
absorbed, or transmitted. The interrelationship among these three processes can be expressed as
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 energy (R- Eu /Edo). The interrelationship involving PAR transmittance, reflectance, andabsorption (in percent) with varying SSC is shown in figure 7. Both the downwelling and upwelling
irradiance below the water surface decreased with increasing SSC.
SSC (m g/l)
600

SSC (m g/l)

400

200

Et

0

20

40

60

Ea

80

100

Transmitted, Reflected & Absorbed Energy (%)

Er

0

1000

800

600

400

200

0

b

0.0

0.2
-2 -1

800

Radiance (µmol m s )

1000

0.4

a

0.6

0.8

1.0

Figure 7: Upwelling irradiance (Eu) at 30 cm(Curve a) & 60 cm (Curve b) depth from water
surface with varying SSC

Figure 6: PAR Budget with varying SSC

The diffuse attenuation coefficient (k) is derived from the Beer-Bouger Law and can be calculatedusing the following equation.
k = [ln (Edo – Ed)] /d

Diffuse Attenuation Coefficient k (at60 cm Depth)

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 the surface.
The diffuse attenuation coefficient k quantifies the “quenching” of light as it passes from the water
surface to the quantum sensor at the depth of 60 cm. The relationship between SSC and k is importantbecause the level of turbidity of a water body can be measured by means of computing k, instead of
measuring SSC. The results of relationship between SSC and k are summarized graphically as figure 8.
Suspended sediment concentrations can be estimated by extension coefficients using a linear regression
model of the formk = 9E-05 (SSC) + 0.021
0.16
0.14
2

0.12

R = 0.9807

0.1
0.08
0.06
0.04
0.02
0
0

100 200 300 400

500 600 700 800 900 1000

SSC (mg/l)

Figure 8: Relationship between SSC and extinction
coefficient54.

Conclusions

Based on the laboratory conducted study, the following inferences are drawn: 1) The reflectanceincreased with the increase in SSC for all types of soil. Between 700 to 900 nm (NIR) the reflectanceincreases more uniformly with increase in SSC; 2) The soil type, particle shape and texture played adominant role in affecting the spectral reflectance. The shape and magnitude of the spectra reflected fromwater is strongly influenced by type and amount of suspended sediments; 3) In the visible domain, withincrease in SSC, there is small increase in reflected energy compared to a large increase in energy absorbed;
4). The first derivative proved to be effective in estimating SSC; 5) The diffuse attenuation increase with theincrease in SSC and the relationship is linear; and 6) At a depth of 60 cm, in the visible domain, there was nobottom effect when suspended sediment concentrations were above 400mg/liter.
To develop more generalize model for predicting suspended sediment concentration, effect of suspended
sediments distribution with depth and application of remotely sensed data with different environmental
condition needs evaluation. It is further concluded that sustainable water resources management in an
organized and coordinated way will be key measures for sustainable development.

Acknowledgments
The research work was financially supported by the Kochi University of Technology, Kochi Japan.
The assistance of all members of Nasu Lab is gratefully acknowledged.

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