Monitoring Suspended Sediment Concentrations in Surface
Waters of the Indus River, Pakistan from ALOS Data
Asif M. Bhatti (CI)1, Seigo Nasu (JAXA-PI -305)2 and Masataka Takagi(CI)3
1

Research Associate, 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.

Abstract
The objective of this research work is to monitor suspended
sediment concentrations in surface waters of the Indus
River by means of ALOS data. The heavy sediment loadbrought by feeding streams in most of the rivers andreservoirs is a major problem. The emphasis of our researchis to use the pattern of suspended sediment concentration(SSC) to compute the sediment load that is transferred fromthe upstream to the downstream. Monitoring of suspended
sediment concentrations using remotely sensed data havereceived the most attention owing to strong relationshipbetween suspended sediments and water surface reflectance.
Reflectance of ALOS band 3 and band 4 was used todevelop the algorithms for estimation of suspended
sediment concentrations. Remotely sensed data makes it
possible to monitor suspended sediment in river waters on
seasonal bases and during floods. The research work
demonstrates an example for the feasibility of ALOS data
for effective and efficient monitoring of suspended
sediments in water bodies.
Keywords: ALOS, Suspended sediments, Monitoring,
Algorithm
1.

INTRODUCTION

Soil erosion is a widespread problem causing thedeterioration of both land & water resources. Highconcentrations of suspended sediment in water are a criticalelement in the economic feasibility of a project and could
shorten the useful life of many reservoirs & dams. South
Asia is one of the regions in the world where soil erosion
by water and wind is a severe problem [1]. Assessment ofsuspended sediment volume carried by a river is vital for an
adapted water resources management strategy. Satellites
play a significant role in monitoring the sediment dynamics.
Distribution of the suspended sediment concentration in therivers is the key issues for analyzing the intensity oferosion in upstream watershed. Remote sensing provides aplatform for monitoring seasonal sediment distribution
patterns, which is not possible though traditional
measurement campaign. Numerous researchers haveestablished relationship between suspended sedimentconcentration and remotely sensed data. The rate ofscattering and backscattering from water body is a functionof size, texture characteristics of the suspended sediments
and is strongly affected by other optically active watercomponents. The main differences between the turbid andclear waters are located in the 400 – 700 nm spectral range[2], where the turbid water has significantly larger
reflectance than the clear water. The objective of present
research is: i) to quantify the suspended sediment in riverwater, ii) to develop methodology for estimation ofsuspended sediments by using ALOS data, iii) toinvestigate the feasibility and effectiveness of ALOS datafor monitoring the suspended sediment distribution.
2.

STUDY AREA

The Indus River, Pakistan is one of the largest sedimentproducing rivers in the world. Sedimentation in the threemajor reservoirs – Tarbela, Mangla, and Chashma inPakistan is going to deplete their storage capacities by over
25 % by the end of the year 2010 [3]. The total length of theIndus is about 2,900 km. The Tarbela Dam is the largest
earth and rockfill dam in the world; across the river Indus.
The length of Tarbela dam reservoir is 97 km with an areaof 260 km2. Sediment load brought by the feeding streams
is major problem in the study area. The sedimentmonitoring station is located 67 km upstream of the dam
body. However, there is dire need to adopt methodology for
effective and efficient monitoring of suspended sediments
along the river.
Table 1: Land area affected by soil erosion by water andwind in South Asia
CountryWaterWind
Total
Erosion
Erosionland Area(Mha )
(Mha)
(Mha)
India
32.8
10.8
328.8
Iran
26.4
35.4
165.3
Pakistan
7.2
10.7
79.6
Afghanistan
11.2
2.1
65.3
Source: [4]3.

METHODS3.1. Laboratory Analysis
In an effort to quantify the relationship between Suspended
sediment concentration and reflectance, water and soil
samples were collected from the study area. The suspended
sediment concentration data of the Indus River at themonitoring station on the day of satellite overpass wastaken from Water and Power Development Authority(WAPDA), Pakistan. A hyperspectral Field SpecPro FR
Spectroradiometer (Analytical Devices, Inc., Boulder, CO)
was used to measure reflectance from the water surface
with varying concentration (100 mg/l to 1000mg/l) of
collected soil sample (Figure 1). The experiment wasperformed in the laboratory and the depth of water column
was 40 cm.
643
2800

600
SSC

500400(mg/l)

200gth600avele
n1000700(n8000m)

9001WxReflectance (R)1005the Indus River. For each band the image digital numbers(DN) were converted to top of atmosphere (TOA) spectral
radiance (L). The TOA spectral radiance is converted into
TOA reflectance for each band by using the following
equation [5].

R(i) =πL(i )μ s E s (i )(1)

Where i = 1,2,3 and 4; μs = cos (θ), θ is the solar zenith
angle, Es (Wm-2µm) is the solar TOA irradiance. The TOA
solar irradiance for the band 1,2,3 and 4 is 1943.3, 1813.7,1562.3 and 1076.5 Wm-2µm respectively.
The TOA measurements are then corrected for atmosphericeffects. The atmospheric correction is the process of
retrieving the surface radiance and surface reflectance fromthe satellite radiances. The dark pixel subtraction technique
was used to remove or reduces the atmospheric influenceon water-leaving light signals. A darkest pixel subtraction
approach is simple and feasible and may apply when no
ground truth data corresponding with the image is available.
Hadjimitsis et al. [6] reported that, from an operational
point of view, the darkest pixel approach was preferred overmore sophisticated techniques that require atmospheric andmeteorological data. The dark pixel subtraction is based on
the assumption that an effective black body exists in theimage, resulting in a pixel value of zero [7]. The minimum
pixel value for that point in each band is because ofatmosphere and subtracted from the corresponding band. In
the upstream area of the Indus River small isolated cleanwater pond located in the mountains with the minimum
pixel values was considered for the purpose of atmosphericcorrection.

400Figure1. Spectral reflectance of water with varyingconcentration of collected soil samples
3.2. ALOS Image
The Advanced Land Observing Satellite (ALOS) has two
optical sensors called PRISM and AVNIR-2, and an L-band
Synthetic Aperture Radar called PALSAR. The
wavelengths covered by the Advanced Visible and Near
Infrared Radiometer type 2 (AVNIR-2) include: Band 1(0.42-0.50 μm), Band 2 (0.52-0.60 μm), Band 3 (0.61-0.69μm) and Band 4 (0.76-0.89 μm). Three ALOS images
acquired on 6 October, 2006 were analyzed in this researchstudy. These images encompass the Indus River, TarbelaDam and surrounding area in the Pakistan. The month ofOctober is the end of the high discharge and flood period in4.

SPECTRAL REFLECTANCE MODEL

The relationship between remote sensing reflectance Rrs,
inherent optical properties (IOPs) of the medium,absorption coefficient a(λ) and backscattering coefficientbb(λ) is expressed as [8]:Rrs(λ ) =b b (λ )fQ a(λ ) + b b (λ )(2)Where f is a coefficient and the commonly adopted valueof f is 0.33, which is valid for zenith sun and for large
variety of natural waters [9]. In the research study area
during the flood season (June-October), the suspended
matters in the water are mainly composed of sediments. The
presence of chlorophyll in the water is neglected. So, theabsorption and scattering by chlorophyll is not taken in toa (λ ) = a w (λ ) + a y (λ ) + a s (λ )bb (λ ) = bbw (λ ) + bb s (λ )(3)Where the subscript w, y, and s represents water, coloreddissolved organic mater, and total suspended sediments.
The value of aw and bbw are taken from [10] for the range
400-800 nm. ay is modeled as follow [11]: ay(λ) =ay(λo)exp[-S(λ−λo)], where λo is 400 nm, ay(λo) =0.3 m-1 and
S=0.014 m-1. Absorption by sediment is modeled as [12]:as(λ) = kS(λref/λ) where k = 0.01 & λref = 400 nm and S issuspended sediment concentration.
Light scattered by suspended sediment is modeled using theMie theory. bbs(λ) is written as [12]:D23S
1bbp =.
Qbb .D − 2 dD (4)∫2 ρ ln( D2 / D1 ) D1
Where S is suspended sediment concentration, ρ is thesediment density = 2600 kg/m3. Qbb is the backscattering
efficiency factor of the suspended particle of refractive
index mr. D2 and D1 represents maximum and minimum
diameter of the suspended particles.

5. RESULT AND DISCUSSION
For the purpose of this study, the combination of ALOS/AVNIR-2 bands 3 and 4 was selected to demonstrate therelationship between reflectance and suspended sedimentconcentration because SSC is strongly correlated with redand NIR domain. The developed reflectance model(Rλ4+Rλ3)/ (Rλ3/Rλ4) derived from laboratory experiments
is the best predictor of SSC. The subscripts 4 and 3
represent band 4 and band 3 of ALOS optical sensor
AVNIR-2. The term (Rλ4+Rλ3) takes account of spectrum
features of low and high concentrations of the suspended
sediment. The term of (Rλ3/Rλ4) might reduce thechlorophyll interference to the low suspended sedimentconcentration.
The in situ suspended sediment concentration at themonitoring station 67 km upstream of the dam was 255
mg/l (sand 20%, silt 55% and clay 15%). However the SSC
at other different location was unknown. In order to deal
with lack of data problem, one approach is to develop
relationship between SSC and reflectance in the laboratory
and apply that model on the image to quantify the SSC
along the river. However the lab conditions varysignificantly from the field conditions. The applicability ofthe laboratory developed model on the image may lead toinaccurate estimation of the suspended sediments. To
quantify the SSC, the model inversion technique wasadopted and the spectral reflectance curve was computedfor the station with known SSC. The computed spectral
curve was averaged into ALOS bands. The simulated model
bands were compared with ALOS spectral signals of thesame sampling station. The ay value was assumed to beconstant. The procedure consists in minimizing thedifference between modeled reflectance data and ALOS
reflectance data. At different locations along the river thedifference between the modeled and ALOS reflectance datawas minimized by varying SSC and the amount ofsuspended sediment was estimated as depicted in figure 2.
500400SSC (m g/l)

account. The absorption coefficient a(λ) and backscattering
coefficient bb(λ) is written as follow:

300200100010305070 90 110 130 150 170 190
Length of River (km)Figure 2: SSC along the river reach
It is evident from the graphical presentation that the SSC
vary along the river and is higher at about 70 km upstreamof the dam. The river catchment area in the range from60km to 80km upstream of the dam is contributing heavy
suspended sediment load to the river. The velocity of theriver inflow containing sediments decreases upon entering
Tarbela reservoir, which reduces the sediment carrying
capacity of the river water. The coarse sediment tends todeposit in the upper reaches of the reservoir, while the finerparticles travel downstream towards the dam and settle inthe reservoir. The adopted methodology proved to be
useful for quantification of SSC along the river reach.
6.

CONCLUSION

The ratio of ALOS/AVNIR-2 Band 4 and Band 3 (2 BandModel) was the best predictor of SSC. The inversion
technique is feasible to cope with lack of ground truth data
problem. The Integration ALOS/ANVIR-2 data, in situ data,
theoretical model is an effective tool for monitoring erosion,

deposition areas and sediment distribution in water bodies.
If ground data is available, the inversion technique may
apply for atmospheric correction. The study has shown thefeasibility of ALOS data to estimate the suspended
sediments in the surface water of rivers and reservoirs. It isimperative to incorporate sediment assessment as anintegral part of water resources and environmental planningfor sustainable development and management.

Acknowledgement
This research is conducted under the agreement of JAXA
Research Announcement titled ‘Monitoring of SuspendedSediment in Surface Water Using Remotely Sensed Data’(JAXA-PI305). The authors wish to acknowledge thecooperation and assistance provided by Japan Aerospace
Exploration Agency (JAXA), Japan.

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