APPLICATION OF REMOTELY SENSED DATA FOR QUANTIFICATION OF
SUSPENDED SEDIMENT IN TURBID RIVER WATER
Asif Mumtaz Bhatti ,PhD Student, Department of Infrastructure Systems Engineering,, Kochi University of Technology, Tosayamadacho, Kami-City, Kochi, 782-8502,Japan (asif_engr@yahoo.com).
Seigo Nasu & Masataka Takagi
Professor, Department of Infrastructure Systems Engineering, Kochi University of Technology, Tosayamada-cho,Kami-City, Kochi, 782-8502,Japan.

ABSTRACT
Remote sensing is proved to be a useful tool to provide an instantaneous and synoptic view of suspended sediments
in rivers & estuaries. Erosion of the land surface yields sediments which are transported downstream by rivers.
Turbidity is not a uniform parameter, either spatially or temporally. The objective of the research study is to quantify
suspended sediments in turbid sediment-dominated waters from high spatial resolution remotely sensed data. In thisstudy, near real time satellite data from Landsat TM coupled with in situ measurements is used to establish
relationship between the remote sensing reflectance (Rrs) signal and suspended sediment concentration (SSC).
Reflectance spectra of different concentration of suspended sediments were measured 1m above the water surface inthe laboratory using hyperspectral Field SpecPro FR Spectroradiometer in the electromagnetic spectrum region of
400 nm to 900 nm. Regression model was developed to quantify the suspended sediment concentration. Results
show that a strong correlation exists between the suspended sediment concentration and red & infrared domain.
Variations of quantity and characteristics of suspended sediment affect the reflectance signal and limit the accuracyof suspended sediment measurement. However, remotely sensed data makes it possible to efficiently monitor theseasonal distribution and concentration of suspended sediment in the water bodies.

Key words: Suspended sediment monitoring, remotely sensed data, NIR, Landsat TM , SPOT

1- INTRODUCTION
Water resources & environmental management is essential for sustaining quality of life on earth. Most of the
problems in water resources sector are common in arid & semi-arid regions of the world. These are generally:
scarcity of water, inadequate storage and sedimentation in water bodies. Soil erosion is a widespread problem
causing the deterioration of both land & water resources. 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.
It is estimated that approximately 1,100 km3 of sediments have been accumulated in the world’s reservoirs, taking
up almost one fifth of the global reservoir storage capacity [1]. The sedimentation has engineering consequences
because it leads to a reduction of the storage capacity of the reservoir and, hence, of its efficiency. Most of thesediment which is eroded from the land surface is in the form of fine particles which are transported in water courses
as a suspended load. These fine sediments account for the turbidity often observed in rivers and their natural fallvelocity in water is so low that the natural turbulence maintains them in suspension. A small proportion of thesediment, perhaps around 10%, is coarser material which is transported as bed load. These sediments roll along the
bed of the river or saltate (hop) close to the bed [2].
Soil provides ecological capacity by delivering a range of functions including food and fiber production,
biodiversity, environmental services, landscape and heritage, raw materials and physical platforms for the built
environment. Erosion of the land surface yields sediments which are transported downstream by rivers. Globally theerosion rate has been estimated by many authors ranging between 0.06 mm/year and 0.16 mm/year [2]. Sediment
discharge assessment in the river basin provides a useful perspective on the intensity of erosion in upstream
watershed. For estimation of the sediment yield one of the methods is the analysis of suspended sedimentconcentration and their relations to discharge at the outlet of catchment. Suspended sediment concentration
measurement posses the advantage of providing a spatially integrated assessment of erosion rates in the upstream

area. Monitoring of suspended sediment is a difficult task because of many influencing factors. Suspended sedimentmonitoring stations are generally fairly sparse. Thus any extensive spatial resolution cannot be obtained. Some adhoc spatial monitoring campaigns do take place, however their expense and logistical difficulty prohibits extensive
use. New sources of data must be explored to complement traditional data collection techniques. Current
technological advances and scientific/engineering knowledge can analyze various scenarios with a fair degree of
accuracy. Remotely sensed data is widely touted by practitioners as providing solutions to the data problem. Data
from remote sensing can easily indicate a spatial pattern of a parameter, which is not possible though traditional
measurement campaign.
Remote sensing makes it possible to monitor the water bodies effectively & efficiently and, identifying areas withsignificant water quality problems. Remote sensing (hyperspectral, satellite and airborne platforms) is a tool that
addresses problems of a regional or global nature. Several studies have been conducted to address the impact ofsuspended sediments on the spectral profile of surface 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 and size affect spectral reflectance [3] [4 [5]. Variations of sediment type (grain size
and refractive index) and changing illumination conditions affect the reflectance signal of coastal waters and limit
the accuracy of sediment-concentration estimations from remote-sensing measurements [6]. It is believed that themain differences between the two types of waters are located in the 400 – 700 nm spectral range [7], where theturbid water has significantly larger reflectance than the clear water. This formed the basis for the detection of turbid
water and SSC estimation algorithms. In this paper spectroradiometer data and spatial information available fromsatellite is comprehensively analyzed and regression model was developed to estimate suspended sedimentconcentration (SSC) from Landsat TM and SPOT data.

2- STUDY AREA
Sediments, which fill lakes, reservoirs, and dams, are one of the most important environmental problems throughout
the world. The rivers of Pakistan carry much sediment either as bed load or in suspension. Sedimentation in the threemajor reservoirs – Tarbela, Mangla, and Chashma in Pakistan is going to deplete their storage capacities by over
25 % by the end of the year 2010 [8]. Much of the land in Pakistan is highly erodible because of sparse vegetal
cover, steep slopes, and non-resistant soils and rocks. The Indus River is one of the largest sediment producing
rivers in the world. The main source of sediment is from the glacial landscape and erosion from steep sided barren
slopes. Melting glaciers and falling rocks leave behind large quantity of sediments each year.
Table 1 : Sediment data of World Rivers
River

Catchment Area
(km2)

Annual Runoff(km3 )

Annual Sediment load
(million tons)

Average Sediment
Concentration (kg/m2)

Yellow

687,869

42.6

1,600

37.5

Ganges955,000

371

1,451

3.91

Bramaputra

666,000

384

726

1.89

Yangtze

1,700,000

933.6

490

0.52

Indus

969,000

175

470

2.68

AmazonSource : [9]5,770,000

5,710

363

0.06

The Indus rises in Tibet, in the snow-clad Kailas range of the Himalayas, is about 5,500 m above mean sea level.
The river slope from the headwaters to Attock is approximately 1/ 300; from Attock to Mithankot it is 1/4,000; andfrom Mithankot to the sea it averages 1/7,000. The total length of the Indus is about 29,00 km. The Indus River
alone provides 65% of total river flows, while the contribution of River Jhelum is 17 %.
The Tarbela Dam is the largest earth and rockfill dam of the world across the river Indus. The 169,650 km2
catchment area of river Indus above Tarbela consists of two distinct hydrological regions. Over 90 % lies between
Karokoram and Himalayan mountain ranges and about 10 % lying immediately upstream of the dam. This 10% partof the basin is subject to monsoon rainfall (July to September) casing sharp floods of short duration. The length ofTarbela dam reservoir is 97 km with an area of 260 km2.

Table 2 : Flow and sediment loads of the Indus catchment to Tarbela

Catchment Area
Annual flows
Annual sediment loads
Source : [10]Upper Catchment(Non-monsoon area) %Lower Catchment(Monsoon area) %

94
92
96

6
8
4

The river flow regime of the Indus is consistent from year to year. The sediment load varies with discharge, seasonal
differences and conditions of the watershed and river morphology. The river basin and catchment areas havesignificant effect on the grain size distribution of the suspended sediments. About 94% of the catchment area of theIndus river is located in semi-arid to hyper-arid environment. A very large portion of the total sediment load is
carried by the river during the summer time. The predicted rate of sediment inflow was 0.294 BCM (Billion Cubic
Meter) per year meaning that the dam would silt up to 90% capacity in 50 years and thereafter continue to provide
only about 1.2 BCM of live storage.

Figure 1: Schematic diagram of Indus Basin Network

350

100
90

300

River Flow

250

60

200

9

3

(10 xm )River Inflow

70

50
150

40

Sediment Load

30

Sediment Load
(Million Tons)

80

100

20
50

10
0

0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year

Figure 2: Annual sediment load computed from hydrograph survey

3- MATERIAL & METHODOLOGY
3.1 Landsat TM Image Processing
Two images were examined in this research study to quantify the suspended sediment load and its spatial andseasonal variation along the river. Among them, one Landsat TM images was acquired on 22nd June, 1994 and
SPOT 2 image was acquired on 10th May, 2005 covering the Tarbela dam reservoir and gauging station. The images
acquired in June 1994 correspond to high discharge period (June to September) and the images acquired in May
correspond with the medium discharge period. The images were geometrically corrected. In order to estimate the
SSC from the satellite images, Digital Counts (DC) are converted to at-satellite radiance and then radiance isconverted to reflectance. The equation to convert satellite DCs to at-satellite radiance is:
L = (DC+A) S
where L is the at-satellite spectral radiance for the given spectral band (Wm-2 sr-1 μm-1), DC is the digital count at agiven pixel for a given spectral band, A is the offset for a given spectral band in the unit of DC, and S is the slope
for a given spectral band in Wm-2 sr-1 μm-1/DC [11]. The offset and slope values were taken from header file.
Atmospheric correction was applied to the at-satellite spectral radiance and then converted to surface reflectance.
The suspended sediment concentration is very high ranging from 500mg/l in May to 2000mg/l in August. As
suspended sediments in the river is high, and 0% reflectance or completely dark objects are not likely to exist. The
improved dark-object subtraction technique [12][13] was adopted to remove atmospheric path radiance or haze DC
value from the images. Also, correction for atmospheric transmission coefficient due to atmospheric absorption was
carried out based on the COST model of Chavez (1996). The following equation was used to calculate reflectancefrom radiance data [11].

R=

π ( Ls − La )TAUV ( EoCos(TZ )TAU Z + Ed )where R is spectral reflectance of the water surface, La is atmospheric path radiance(Wm-2 sr-1 μm-1). TAUv is
atmospheric transmittance along the path from the ground surface to the sensor, TAUz is atmospheric transmittancealong the path from the sun to the ground surface, TZ is Solar zenith angle in degree. Eo is solar spectral irradianceon a surface perpendicular to the Sun ray’s outside the atmosphere (Wm-2 sr-1 μm-1). Ed is the downwelling spectral
irradiance at the water surface due to scattered solar flux in the atmosphere(Wm-2 sr-1 μm-1).

3.2 SPOT 2 Image Processing
The SPOT image was acquired on 10 May, 2005 during that time in situ measurement of turbidity was taken along
the river. The digital numbers (DNs) was converted the into radiance units by using the equation 1;
L= DN/Ak

where L (Wm-2 sr-1 μm-1) is the top of atmosphere (TOA) upwelling radiance and Ak (W-1m2 sr μm) is thecalibration factor for spectral band k. Lwas converted to TOA reflectance R using the following relationship [14]:R ( XSi ) = πL ( XSi )μ s E s ( XSi )Where i = 1, 2, or 3 for XS1, XS2, XS3; µs = cos(θs), θs is the solar zenith angle; Es(XSi) (Wm-2 μm) is the solar
TOA irradiance. The TOA measurements were then corrected for atmospheric effects. R is related to the above
water reflectance (Rw, unitless) by following the relationship:
R = Tg (Raer+ Rray + TdRw)
where Raer and Rray are the aerosol and Rayleigh reflectances, respectively; Tg and Td (unitless) are the gaseous anddiffuse transmittances, respectively. The atmospherically corrected reflected was calculated by using the following
equation;

Rac =

Rw
1 + SRw

Where S (unitless) is the total spherical albedo. This atmospherically corrected reflectance, divided by π steradians,
can be compared to the remote-sensing reflectance: 1/ π Rac(λ)Rrs(λ) [15].

3.3 Laboratory Experiments
Water and soil samples were collected from the study area. Soil samples were collected from the catchement area inorder to investigate the relationship between Suspended sediment concentration and reflectance from the water
surface. The experiments were conducted in a black painted room to measure the reflectance from water surface andwater with varying concentration of collected soil samples. The samples were air dried and sieved to ensure a
uniform sediment size. A hyperspectral Field SpecPro FR Spectroradiometer (Analytical Devices, Inc., Boulder,CO) was used to collect the upwelling radiance from the water surface. The depth of water column was 40 cm.
Initial reflectance readings were taken for the water tank prior to the addition of any suspended sediments andsediments 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. Data from 400 to 900 nm
was used because noise was pronounced at wavelength shorter that 400 nm and longer than 900 nm. Reflectancewas calculated as a simple ratio between target and reference panel using following equation.
%

R ( λ ) =L ( λ )× Cal
S ( λ )

( 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.

4. RESULT AND DISCUSSION
The determination of the statistical variance in the spectral reflectance is a tool for measuring the information
content in the spectra. The spectral reflectance of water with varying concentration of collected soil samples areshown in figure 3. With the increase in suspended sediment concentration the spectral reflectance increase and thepeak reflectance shifts towards longer wavelength. The spectral reflectance of suspended sediment in surface water
is a function of both the quantity and characteristics (particle size, texture, absorption) of sediments and is strongly
affected by other optically active water components. The average reflectance value of selected soil sample with the
SSC of 1000 mg/l was 6 %. The low reflectance value is caused by the black wall and bottom of the experimental
tank. Most of light is absorbed by the black bottom causing the low reflectance. The hyper
The Landsat TM was acquired almost 12 years before and the atmospheric condition at the site was not known. In
case of SPOT the sky was clear on the day of acquisition of image and the also there was no wind. We assumed thatthe factor affecting the images on two different times with long time span was not very much different. For the
purpose of this study the TM band 3 (630 - 690 nm) and band 4 (760 - 900 nm) was selected as suspended sedimentconcentration is strongly correlated with red and NIR domain. The average reflectance value of TM band 3 was
21% and SPOT X3 was 14 %. The concentration was found to be low in case of SPOT image because of dryseason. However the month of June is considered to be rainy with many flash floods. This scenario closely matcheswith the seasonal pattern of SSC in the river. The in situ suspended sediment measurements are shown in table 3.
Table 3 : In Situ Suspended Sediment Concentration Measurements
Date

Discharge (m3/s)

SSC (mg/l)

22 June, 1994
10th May, 2005

7690.7
3015

1700
473

nd

% Average
Sand

Silt

Clay

20
10.1

57
76.1

23
13.8

6

5

3
2
900

1

800

500

600

SSC

th

av

800

W

600

1000

e le

ng

700

(n m

0

)

Reflectance %

4

400

(m g

/l)

200

400

Figure 3. Spectral Reflectance of Water With Varying Concentration of Collected Soil Sample
The simulated TM bands, individually and in combination, was used with regression techniques to examine therelationship between reflectance and SSC to provide solution for the field application of remotely sensed data forassessment of suspended sediments. The correlation coefficients (R2) were computed between SSC and simulated
TM band (Table 4).

Table 4: Correlation between simulated TM bands and SSC
TM Bands
1
2
3
4

R2
0.93
0.98
0.99
0.99

The NIR region (Band 4) seems to be more useful than the visible region for measuring the SSC in water. The
reflectance of band 4 can be used to develop a linear regression model of the following form for estimation of SSC.
SSC (mg/l) = (R (Band 4) + 0.0412)/ 0.004
The spectral reflectance, in the NIR domain (Band 4), as a function of varying SSC in water is shown in figure 4.

62

R = 0.99

Reflectance

5
4
3
2
1
0
0

100

200

300

400 500 600
SSC (mg/l)

700

800

900 1000

Figure 4. Correlation between simulated TM bands and SSC
Reflectance was estimated from the pixel of different location alone the river. The SSC is higher in the upstream of
the river and lower in the reservoir on the Tarbela dam because the velocity of the river inflow containing sediments
decreases upon entering Tarbela reservoir, which reduces the sediment carrying capacity of the river water. The
coarse sediment tends to deposit in the upper reaches of the reservoir, while the finer particles travel downstream
towards the dam and settle in the reservoir. The reason for success of remote sensing in such surveys is the strong
positive relationship that exists between SSC and remotely sensed spectral reflectance. It is also easy to update
remote sensing data, which allows continuous monitoring. Many authors have developed the empirical relationshipto estimate the SSC. However no standard algorithm was developed to estimate the SSC from the reflectance.

5- CONCLUSION
Seasonal and spatial variations of reflectance for TM band 3 and 4 and SPOT image were observed along thecourses of the Indus River and compared with on-site SSC measurements. In the high discharge period (June )reflectance values are higher than during the medium discharge period (May). It is concluded that in the red and nearinfra red band’s reflectance increase with the increase in SSC. For the same concentration, the experimental
reflectance value was found to be less than the estimated reflectance of TM band 3. The rational is because theexperimental tank was black panted and it absorbs most the incident light. Also, the water quality parameter targeted
here, namely suspended sediments are strongly influenced by the environmental factors. Comparisons of satellitederived sediment concentration indicated several areas of similar relative concentrations. The study has shown that it
is possible to use hyperspectral and multispectral data to estimate the suspended sediments in the surface water ofrivers and reservoirs. The water and environmental engineers could concentrate on the water bodies and theirwatersheds with the most significant suspended sediment problems. In the future, it will be important to incorporate
sediment assessment as an integral part of water resources and environmental planning, development andmanagement. Without this approach the problems of the water & environmental sector are going to compound in the
future.

Acknowledgement
The authors wish to acknowledge the cooperation and financial assistance provided by Kochi University ofTechnology (KUT), Japan.

REFERENCES:[1]. Mahmood K. (1987),Reservoir sedimentation: impact, extent and mitigation,World Bank, Technical Paper 71.
[2]. W. R. White (2005), Review of Current Knowledge, World water storage in man-made reservoirs, Foundation
for Water Research, FR/R0012, April 2005.

[3]. Lodhi et al. (1997), The potential for remote sensing of loess soils suspended in surface waters, Journal of theAmerican Water Resources Association, 33, 111–117.
[4]. Bhargava, D. S., and Mariam, D. W. (1990), Spectral reflectance relationships to turbidity generated by different
clay materials, Photogrammetric Engineering and Remote Sensing, 56, 225–229.
[5]. Bhargava, D. S., and Mariam, D. W. (1991), Effects of suspended particle size and concentration on reflectancemeasurement, Photogrammetric Engineering and Remote Sensing, 57, 519–529.
[6]. D. Doxaran et al. 2003, Remote-Sensing Reflectance of Turbid Sediment-Dominated Waters. Reduction of
Sediment Type Variations and Changing Illumination Conditions Effects by Use of Reflectance Ratios,
Applied Optics-LP, Volume 42, Issue 15, pp. 2623-2634.
[7]. Rong-Rong Li, Yoram J. Kaufman, Bo-Cai Gao, & Curtiss O. Davis, 2003: Remote Sensing of Suspended
Sediments and Shallow Coastal Waters. IEEE Transaction on Geoscience and Remote sensing. Vol. 41, No.
3 PP. 559.
[8]. Kahlown M.A. & Majeed A. (2001), COMSATS 1st meeting on Water Resources in The South: Present
Scenario and Future Proposals, November 2001, Islamabad, Pakistan.
[9]. Tarbela second periodic inspection (1991), Volume: 2, Sec: 20.5 (b), page 20-12.
[10]. WAPDA (1998), “Tarbela Dam Sediment management Study” Volume 2, March 1998.
[11]. Chavez, P. S. Jr, 1996, Image-based atmospheric corrections - Revisited and Improved. PhotogrammetricEngineering and Remote Sensing, 62, 1025–1036.
[12]. Chavez, P. S. Jr, 1988, An improved dark-object subtraction technique for atmospheric scattering correction ofmultispectral data. Remote Sensing of Environment, 24, 459–479.
[13]. Chavez, P. S. Jr, 1989, Radiometric calibration of Landsat Thematic Mapper Multispectral images. Remote
Sensing of Environment, 55, 1285–1294.
[14]. Vermote, E. F. et al. (1997), Second simulation of the satellite signal in the solar spectrum: an overview. IEEE
Transactions on Geoscience and Remote Sensing, 35 (3), 675–686.
[15]. D. Doxaran et al. (2002), “Spectral signature of highly turbid waters Application with SPOT data to quantify
suspended particulate matter concentrations”, Remote Sensing of Environment 81 (2002) 149– 161.