The 7th Int. Conf. on Hydroscience and Engineering (ICHE-2006), Sep 10 –Sep 13, Philadelphia, USA

OPTIMUM HYPERSPECTRAL MODELS FOR ESTIMATION OF SUSPENDED
SEDIMENTS IN SURFACE WATER
Asif Mumtaz Bhatti 1 , Seigo Nasu 2 and Masataka Takagi 3

ABSTRACTThe purpose of the research was to estimate the suspended sediment concentration by means ofremote sensing. Experiments were conducted to investigate the spectral response with different
concentrations and types of suspended sediments in the water. In this research five cases were
examined: clayey red soil, silty red soil, kaolin, silty red soil (50%) and kaolin (50%), and very finesand. Fifty concentrations of suspended sediments ranging from 20 to 1000 mg/l were used is eachcase. A hyperspectral Field SpecPro FR Spectroradiometer attached to a telescope was used tomeasure reflectance from the water. A series of reflectance measurements in the spectral range
between 400 and 900 nm was carried out in a batch. The band ratioing technique and regression
analysis were applied to develop band ratio models.
Correlation coefficients (r) were computed to describe the relationship between suspended
sediment concentration (SSC) and the reflectance. The ratios of reflectance values in the sub-bands
centered at Rrs848/Rrs548, Rrs803/Rrs708, Rrs793/Rrs713, Rrs783/Rrs673, Rrs773/Rrs663, andRrs773/Rrs658 were found to be optimum for estimation of suspended sediment concentration. The
red and near-infrared (NIR) wavelength is a useful range for determining the amount of suspended
sediments. However, the wavelength range 525-655 provides information about sediments type.

1.

INTRODUCTION

Water resources and environmental management is essential for sustaining quality of life onearth. Soil erosion is a widespread problem causing the deterioration of both land and water
resources. Globally the erosion rate has been estimated by many authors ranging between 0.06
mm/year and 0.16 mm/year (White, 2005). Sediment discharge assessment in the river basinprovides 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 sediment concentration and theirrelations 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. High concentration of suspended sediments in lakes and reservoirs accelerates the siltation
process and thus shortens their life and efficiency. The loss of storage capacity in reservoirs in the1

PhD Student, Department of Infrastructure Systems Engineering,, Kochi University of Technology, Tosayamada-cho,Kami-City, Kochi, 782-8502,Japan (asif_engr@yahoo.com)
2,3
Professor, Department of Infrastructure Systems Engineering, Kochi University of Technology, Tosayamada-cho,Kami-City, Kochi, 782-8502,Japan

United States cost $ 100 million annually in dredging and related mitigation efforts (Julien, 1995).
Monitoring and modeling of suspended sediment is a difficult task because of many influencing
factors. Suspended sediment monitoring stations are generally fairly sparse. Thus any extensive
spatial resolution cannot be obtained. Some ad-hoc spatial monitoring campaigns do take place,
however their expense and logistical difficulty prohibits extensive use.
Remote sensing holds potential for monitoring and estimating suspended sediments in surface
water. Traditional methods of sampling and analysis are time consuming, labor intensive andprovide only point data where measurements at any location on one lake, or even on many lakes, aregenerally necessary (Lodhi, 1997). The strength of remote sensing techniques lies in their ability toprovide both spatial and temporal views of surface water quality parameters that is typically notpossible from in situ measurements. Remote sensing makes it possible to monitor the water bodies
effectively and efficiently and, identifying areas with significant water quality problems. Remotelysensed multispectral and hyperspectral data is widely touted by practitioners as providing solutions
to the data problem. Remote sensing has been used extensively for the delineation of the surface
water bodies. Remote sensing has an important role in water quality evaluation and management
strategy. Monitoring large areas on a frequent basis can only be achieved economically with remotesensing (Engam 1991). Numerous researchers have developed algorithms for the relationshipbetween the concentration of suspended sediments and radiance or reflectance. A few studies havetaken the next step and used these algorithms to estimate suspended sediments for another time orplace (Ritchie, 1988).
The suspended sediment concentration and reflectance relation depends on many factors
including physical and optical properties of sediment type and sensor zenith angle (Kuo and Cheng
1978, Chen et al. 1991). The absorption and scattering properties of sediment type affect waterreflectance (Novo et al. 1989). The presence of sediment in water changes the backscatteringcharacteristics of the water dramatically. The synoptic view provided by remote sensing gives
resource managers different type of data owing to large area coverage, timeliness and repetitive
nature of satellite image, which is not possible from traditional surface data collection campaigns.
The spectral reflectance of suspended sediment in surface water is a function of both the quantity
and characteristics (particle size, texture, absorption) of sediments. It is imperative to determine thequantity and type of suspended sediments and optimal wavelength to enhance the reliability andapplicability of remotely sensed data for water quality monitoring. Controlled experiments wereconducted in order to investigate the effects of varying concentrations and types of suspended
sediments on the spectral signature of the surface water. The purpose of the study was to examine
correlations between remotely detected hyperspectral reflectivity and suspended sedimentconcentration in surface water.

2.

MATERIAL AND METHOD

The research is based on the experiments conducted indoor at Kochi University of Technology,
Kochi, Japan. The purpose of this research was to estimate the suspended sediment concentration bymeans of remote sensing. In this research work five cases were examined: clayey red soil, silty redsoil, kaolin, silty red soil (50%) and kaolin (50%) and very fine sand. The red soil samples werecollected from the central area of Okinawa Island (coordinates: 26º23´N and 127º73´E). Subsurface
geology is diverse in this area with limestone, sandstone, phyllite, and gravels (Kubotera, 2006). The
physical properties of the collected red soil were expected to be various, reflecting the diversity ofparent materials. The percentage of clay was higher in the clayey soil. Kaolin and very fine sand wasordered commercially. The soil samples were air dried, pulverized and manually sieved to ensure a
uniform particle size. To minimize extraneous reflectance, the experiments were performed in blackroom designed for experimental purpose using black coated tank. Two 500 watt tungsten halogen

lamps uniformly illuminated from 1 meter above water surface with a beam inclination of 30o were
the sources of illumination during the experiment. The water tank is also black coated to reduce theinfluence of extraneous reflectance.
A hyperspectral Field SpecPro FR Spectroradiometer attached to a telescope was used tomeasure reflectance from the water. This portable spectrometer combines three spectrometers tocover the wavelength range from 350 to 2500 nm. A photodiode array spectrometer is used to coverthe 350 to 1000 nm spectral range (UV/VNIR), while two fast-scanning (0.1 sec) spectrometersprovide coverage for the two wavelength ranges from 1001 to 1800 nm and 1801 to 2500 nm (SWIR
1 and SWIR 2). For the purpose of this study, data from 400 to 900 nm (353 instrument channels)
were used because of low signal-to-noise ratio problem at wavelengths outside this spectral range.
The sensor was positioned perpendicular to the water surface at the height of 1 meter above thesample, yielding an instantaneous field of view 1.75 cm. The effects of atmospheric dispersion andabsorption of light were considered negligible as the experiment was performed in black room undercontrolled condition and the distance of the sensor from the water surface was kept fixed during all
the experiments.
Radiation input to the ASD Field Spec FR spectrometers is through a fiber optic, nominally 5
meters in length. The spectroradiometer was linked to a laptop, which stores the scans for eachsample. The experiment was consisted of two parts. In the first part, the radiance of selected dry soil
samples and in the second part radiance of water surface with varying concentration and types ofsuspended sediments was performed. Radiation of dry soil was measured indoor in black coatedroom. The soil samples were placed in black containers (15 cm in diameter and 6 cm in height). In
case of dry soil samples contact probe was used to measure the radiation. However, in case of waterwith varying suspended sediment concentration 1 degree sensor was used.
The sieved soil samples were weighted to produce fifty concentrations of suspended sedimentsin each case ranging from 20 to 1000 mg/l. Initial reflectance readings were taken for the water tankprior to the addition of any suspended sediments and subsequently after sediments were mixed into
the tank. The addition of each soil sediment produces suspended sediment concentration of level of
20 mg/l. The depth of water column was 60 am and was kept constant for all experiments. The soil
sediments were kept in suspension by means of a mechanical diffuser and manually stirring at
regular intervals. A stopwatch was used to monitor sampling intervals to ensure equal mixing times
for each sediment addition and scanned time in order to minimize settling of input sediments. To
improve the signal to noise ratio mean of ten scans were used in the analysis. All variables andprocedure are kept identical for each case. Turbidity was measured by using PARTECH turbidity
meter in mg/l in order to verify the homogeneity of suspended sediments and for comparison with
SSC levels (M.A. Lodhi, 1997). Homogeneity of the suspended sediments in the water is imperative
for accurate estimation of concentration.
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 from the 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.

3.

RESULTS And DISCUSSION

Many authors including Streck et al. 2003 have delineated the spectral signature of different type ofsoils. Spectral signatures of different dry soils sample are unique. It is possible to differentiate
between different dry soils samples with varying texture and colors owing to distinct spectral patternof reflectance as shown in Fig 1.
1.2

a

Reflectance

1

b

0.8

e
c
d

0.6
0.4
0.2
0
400

500

600
700
Wavelength (nm)

800

900

Figure 1 Spectral Reflectance of dry soils(a) Kaolin (b) Kaolin 50% and Silty red soil 50% (c) Silty red soil (d) Clayey red soil (e) Find sandAll types of soils show a consistent characteristic signature. The reflectance value increase
with the increase in wavelength in the visible and NIR domain. Kaolin (Curve a) has the highest
reflectance, with about 25 % more reflectance than the mixture kaolin and silty red soil 50-50%. Dry
fine sand showed higher reflectance as compared to clayey red soil and silty red soil. The higher
reflectance of sand is because it is composed of quartz, which has higher reflectance VIS, NIR, andMIR compared to agricultural soils (Streck, 2003). Owing to soil characteristics and dark colour ofclayey soil the reflectance value of silty red soil was found to be more than clayey red soil. The
organic matter content in the dry soils samples was not measured as it was out of the scope ofpresent study. However, previous studies have shown that organic matter content has a strong
influence on the soil reflectance.
It is desirable to examine the spectral characteristic of the suspended sediments in thelaboratory controlled environment. This could be useful to provide methodology to monitor the type,amount and spatial distribution of suspended sediments in water bodies. In order to investigate theconcentration and type of suspended sediments in surface water, series of reflectance measurementsin the spectral range between 400 and 900 nm was carried out in a black coated tank. Reflectanceincrease uniformly with the increase in suspended sediment concentration at all wavelengths
between 400nm and 900nm as depicted in figure 2. An asymptote exists for sediment loading insurface waters, and the asymptote is different for various types of soil. Therefore, amount ofsuspended material and type are important factors in volume reflectance (Lodhi, 1997). Figure 2
shows the spectral reflectance of water with varying types of suspended sediments at SSC of 1000
mg/l. The reflectance value of kaolin as suspended sediments is higher as compared to other types ofsoils. Fine sand as suspended sediments has low value of reflectance. The find sand settle in thewater and affect the accurate estimation of suspended sediment concentration. The reflectance ofsilty red soil as suspended sediments is more than the clayey red soil, which conforms with theresults of numerous researchers.

0.4

a

0.35

SSC (1000 mg/l)

Reflectance

0.3
0.25

0.15

b
c

0.1

d

0.05

e

0.2

0
400

500

600

700

800

900

Wavelength (nm)Figure 2 Spectral reflectance of water with different type of suspended sediments(a) Kaolin (b) Kaolin 50% and Silty red soil 50% (c) Silty red soil (d) Clayey red soil (e) Find sand
Graphs were plotted to describe the variation of spectral reflectance with wavelength for different
suspended sediment concentration (SSC) caused by different types of sediments (Figure 3).
0.4
0.35

0.3

0.2

Reflectance

600mg/l0.25400mg/l

0.15
0.1

0
400

500

0.25
0.2

1000mg/l

0.15500mg/l

0.1200mg/l
100mg/lClean water

0.05

Kaolin 50% and Silty red soil 50%

0.35

800mg/l

0.3
Reflectance

0.4

Kaolin1000mg/l

200mg/lClean water

0.05
0

600

700

800

400

900

500

0.4

0.4

Silty red soil

0.35

700

800

900

Wavelength (nm)Wavelength (nm)Clayey red soil

0.35
0.3
Reflectance

0.3
Reflectance

600

0.25
0.2

1000mg/l

0.15500mg/l

0.1

400

500

600

0.15

1000mg/l
500mg/l
200mg/lClean water

0.05

Clean water

0

0.2

0.1

200mg/l

0.05

0.25

0
700

Wavelength (nm)

800

900

400

500

600

700

Wavelength (nm)Figure 3 Spectral reflectance of water with varying concentration of SSC (mg/l)

800

900

The comparison of the graphs showed that optical signature trends are almost similar in caseof kaolin, kaolin 50% and silty red soil 50%, clayey red soil, and silty red soil. A consistentincreasing trend in reflectance was observed with the increase in SSC. Ritchie et al. (1976) using insitu studies concluded that wavelengths between 700 and 800 nm were most useful for determining
suspended sediments in surface waters. It was observed that the red and near-infrared (NIR)
wavelength 700-900 is a useful range for determining the amount of suspended sediments. However,
the wavelength range 525-665 provides information about sediments type.
Suspended sediment in water has greater absorption of radiation at longer wavelength (Chen etal. 1991). It was observed that within the visible domain suspended sediments have no characteristic
absorption. Fischer and Kronfeld (1990) suggested that scattering depends strongly on the complex
refractive index and the size distribution of the particles. The mean refractive index of clay and nonclay minerals is higher than the refractive index of water (1.335). The mean refractive index of theclay minerals (1.540) is higher than the non-clay minerals (1.523) and the effect of variation in therefractive index is expected on the relation between the suspended sediment concentration and thereflectance.
The results obtained from the laboratory experiment indicated that the visible region weremore useful than the longer near-infrared wavelengths for the information about type of the soil. The
darker soil produced lower reflectance value than the light-coloured soil. In dry condition and assuspended sediments different soils can easily be distinguished from each other in the visible
domain of 525-655 nm. It was observed from the figure 3 that the peak reflectance for each level
and type of suspended sediment occurred in the visible domain and shifted towards longer
wavelength with the increase in suspended sediment concentration. The peak is closely related to theamount of SSC. However, change in shift not only depends on the amount of SSC, but also on thetype of the suspended material. The NIR wavelengths found to be more useful for estimation ofsuspended sediments.
In cases of find sand significant increase in reflectance was not observed with an increase in
sediments concentration (Figure 4). This is because of texture and variation of grain size in fine sand.
Different spectral reflectances were obtained for the different soil types of the same concentration
because of the difference between the properties and characteristics of various soil types.

0.04
0.035

R e fle c ta nc e

0.03
0.025
0.02
1000 mg/l

0.015
0.01

100 mg/l

0.005
0
400

500

600

700

800

900

Wavelength (nm)Figure 4 Spectral reflectance of water with varying concentration of fine sand (mg/l)

3.1

BAND RATIO MODEL

1

0.9

0.9

0.8

0.8

0.7

0.7

Rrs 773 / Rrs 658

Rrs 848 / Rrs 548

The band ratioing technique and regression analysis were applied to develop band ratio models. The
spectral data were partitioned into narrow bands each having 5nm width. In total 100 centerednarrow bands were generated, including 25 bands in blue domain, 16 bands in green domain, 10
bands in yellow domain, 19 bands in red domain, and 30 bands in NIR domain. Each narrow band isreferred to, by its central wavelength.

0.6
0.5
0.4

2

0.3

y = -3E-07x + 0.001x + 0.2886

0.2

R = 0.9893

2

0.1

0.6
0.5
0.4
2

0.3

y = -3E-07x + 0.0006x + 0.5084

0.2

2

R = 0.9509

0.1

0

0

0

100

200

300

400

500

600

700

800

900

0

100

200

300

0.9

0.9

0.8

0.8

0.7

0.7

0.6
0.5
0.4
2

0.3

y = -3E-07x + 0.0006x + 0.5154

0.2

2

R = 0.9499

0.1

600

700

800

900

0.6
0.5
0.4
2

0.3

y = -3E-07x + 0.0006x + 0.5347

0.2

2

R = 0.9419

0.1

0

0
0

100

200

300

400

500

600

700

800

900

0

100

200

Suspended Sediment (mg/l)

1

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6
0.5
0.4
2

0.3

y = -2E-07x + 0.0004x + 0.6473

0.2

400

500

600

700

800

900

0.6
0.5
0.4
2

0.3

y = -2E-07x + 0.0004x + 0.6618

0.2

2

R = 0.9437

0.1

300

Suspended Sediment (mg/l)

Rrs 793 / Rrs 713

Rrs 803 / Rrs 708

500

Suspended Sediment (mg/l)

Rrs 783 / Rrs 673

Rrs 773 / Rrs 663

Suspended Sediment (mg/l)

400

2

R = 0.9256

0.1

0

0

0

100

200

300

400

500

600

Suspended Sediment (mg/l)

700

800

900

0

100

200

300

400

500

600

Suspended Sediment (mg/l)

Figure 5 Band ratio models (Ratio of reflectance plotted against SSC)

700

800

900

Table 1 Development of Narrow Bands
Ratio (Bi/Gj)

RrsBi/ RrsGj

i=1 to 25

j= 1 to 16

Ratio (Bi/Yj)

RrsBi/ RrsYj

i=1 to 25

j= 1 to 10

Ratio (Bi/Rj)

RrsBi/ RrsRj

i=1 to 25

j= 1 to 19

Ratio (Bi/NIRj)

RrsBi/ RrsNIRj

i=1 to 25

j= 1 to 30

Ratio (Gi/Yj)

RrsGi/ RrsYj

i=1 to 16

j= 1 to 10

Ratio (Gi/Rj)

RrsGi/ RrsRj

i=1 to 16

j= 1 to 19

Ratio (Gi/NIRj)

RrsGi/ RrsNIRj

i=1 to 16

j= 1 to 30

Ratio (Yi/Rj)

RrsYi/ RrsRj

i=1 to 10

j= 1 to 19

Ratio (Yi/NIRj)

RrsYi/ RrsNIRj

i=1 to 10

j= 1 to 30

Ratio (Ri/NIRj)

RrsRi/ RrsNIRj

i=1 to 19

j= 1 to 30

Figure 5 shows the relationship between ratios of spectral reflectance and SSC. This
relationship is based on simple reflectance (Rrs) ratio between visible and near-infrared wavelengths
plotted against suspended sediment concentration. The ratios of reflectance values in the sub-bands
centered at Rrs848/Rrs548, Rrs803/Rrs708, Rrs793/Rrs713, Rrs783/Rrs673, Rrs773/Rrs663, andRrs773/Rrs658 were found to be optimum for estimation of suspended sediment. The regression
models were developed and correlation coefficients (r) between ratio of spectral reflectance and SSC
were computed to describe the relationship between suspended sediment concentration and thereflectance. The above spectral reflectance ratios and suspended sediments were found to be wellcorrelated for clayey red soil, silty red soil, kaolin, and kaolin (50%) and silty red soil (50%). The
computed r value was greater than 0.90 in all cases. However, in case of fine sand the computed r
value range was 0.55-0.70. The rational is because the fine sand texture and grain size affect the
spectral reflectance.

4.

CONCLUSSIONS

On the basis of experimental results obtained from the present study it can be concluded that theintensity and spectral distribution of reflectance varied with type, texture, grain size andconcentration of suspended sediments in surface water. Spectral reflectance for different types andconcentration of SSC are uniqie. It was observed that the reflectance in the red and near- infrared(NIR) wavelength 700-900 is a useful range for determining the amount of suspended sediments.
However, the wavelength range 525-665 provides information about sediments type.
Band ration models allow the accurate quantification of suspended sediments. The relationshipis based on the simple reflectance ratio between near-infrared and visible domains. The ratios ofreflectance values in the sub-bands centered at 848/548, 803/708, 793/713, 773/663, 783/673, and773/658 were found to be optimum for estimation of suspended sediment in surface water. The
results of the estimation of suspended sediment by using hyperspectral band ration models areencouraging. In this paper only the effect of suspended sediment amount and type on spectralreflectance was addressed. To develop more generalized model for predicting suspended sedimentconcentration, similar studies are needed to express and quantify the integrated effect of all suchparameters on spectral reflectance.

ACKNOWLEDGEMENTS
The authors wish to acknowledge the financial assistance provided by Kochi University ofTechnology (KUT), Japan. The authors gratefully acknowledge the assistance and cooperation of all
the members of Nasu lab, KUT.
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