ASSESSING THE POTENTIAL OF REMOTELY SENSED DATA FOR WATERQUALITY MONITORING OF COASTAL AND INLAND WATERS
Asif Mumtaz Bhatti 1, Donald C. Rundquist 2, Seigo Nasu 3 and Masataka Takagi 4
1
Research Associate, COE, Kochi University of Technology, Kochi, Japan.
2
Professor, CALMIT, University of Nebraska-Lincoln
3,4Professor, Kochi University of Technology, Kochi,
ABSTRACT: The objective of this research study was to assess the potential of remotely sensed data for
estimation of water quality parameters in water bodies. The assessment was based on the field measurements,
laboratory examination and data analysis of the collected hyperspectral reflectance data. The factors
influencing the qualitative and quantitative nature of the spectra were also analyzed. The hyperspectral data
is integrated into ALOS/AVNIR-2 band widths. Examination and correlation of simulated ALOS data
reveals that multiplicative two band approach by using Band 4 (NIR) and Band 3 (Red) is best predictor ofsuspended sediments in water bodies. The ratio of ALOS/AVNIR-2 Band 3 and Band 1 was most closely
correlated with Chl-a concentration. For estimation of total suspended sediments (TSS), a higher accuracy
was achieved using the Band 4 /Band 2 ratios. Color dissolved organic matters (CDOM) were found to bewell correlated with Band 1/Band 2. Regression models were developed and the applicability of developedmodels was tested. The correlation coefficients with R2 >0.70 elucidate the effectiveness of developed
remote models for water quality monitoring. The developed algorithms have potential for estimation of water
quality parameters in inland and coastal waters. The research work demonstrates an example for thefeasibility of remotely sensed data for effective and efficient monitoring of suspended sediments, totalsuspended sediments, chlorophyll and (CDOM) in water bodies.
KEY WORDS: Water quality parameters, Assessment, Remotely sensed datacharacteristics. Effectiveness of remotely sensed data
in water quality assessment of different lakes andreservoirs has been examined by numerous
researchers [2] [3] [4] [5].

1. INTRODUCTION
Synoptic information on water quality isdifficult to obtain from a routine in situ monitoring
network. The conventional point sampling
campaigns do not give either the spatial or temporalview of water quality needed for accurate assessmentand management of water bodies. Therefore, thedifficulty of overall and successive water quality
sampling becomes a barrier in water quality
monitoring, forecasting and management. Remotesensing makes it possible to monitor the water
bodies effectively, efficiently, and identifying areas
with significant water quality problems. Remotesensing is a tool and technique that provides aplatform for large-scale synoptic observations andcontinuous monitoring of water bodies.

From the remote sensing perspective, waters
can generally be divided into two classes: case-I andcase-II waters [6]. Case-I waters are those dominatedby phytoplankton (e.g. open oceans) whereas case-II
waters containing not only phytoplankton, but also
suspended sediments, dissolved organic matter, andanthropogenic substances for example some coastal
and inland waters [7]. Major factors affecting water
quality in water bodies are suspended sediments,
algae (i.e., chlorophylls), chemicals (i.e., nutrients,
pesticides, metals), and colored dissolved organic
matter (CDOM).
The prime objective of the paper is to develop
relationships between water quality parameters(WQP) and remotely sensed data (RSD). The
criterion that has been taken in to consideration is
the simplicity of the water quality model, accuracy,
overcome influencing factors, and the potential
application of the developed model at different timeand place.

Remote sensing techniques for monitoring
coastal and inland waters have been underdevelopment since the early 1980's. The advantagesof remote sensing are wider coverage of a satellite
image, high and medium spatial resolution up to submeter, multi-spectral imagery, sometimes stereomode, cyclic monitoring etc. On the other hand, thedisadvantages are invisible in case of cloud coverageand night time for optical sensor, fixed time and date
for acquisition and normally higher cost of the data[1]. The remotely sensed techniques for accuratemonitoring of water quality parameters depend on
the substance being measured, its concentration,
influencing environmental factors and the sensor

2. STUDY AREA
The research work to assess the potential of
remotely sensed data for water quality monitoring
was performed on three different rivers; The

1

Monobe River, Kochi, Japan, Altamaha River,
Georgia, USA, and the St. Marys River, Georgia,
USA. The Monobe River is 68.12 km long and thebasin area is 508.2 km2, in which, mountain forest is461.8 km2, flat land is 38.2 km2 and water course is
8.2 km2. The Monobe River has three dams and twohead works, namely Nagase Dam, Yoshino Dam,
Suita Dam, Godo Weir and Togo Weir fromupstream to downstream [8]. The Altamaha River inGeorgia, USA drains one of the largest basins on theeast coast. The watershed area of the river including
its tributaries is 36,260 km2. There is no dam on theriver. The St. Marys River is in the southeastern
United States. The river is approximately 144 km
long. The research work at Altamaha River and St.
Marys River was carried out as a joint research
project with Center for Advanced Land Management
Information Technologies (CALMIT), School of
Natural Resources, University of Nebraska-Lincoln,USA.

optical fiber and a cosine collector was pointed
upward to simultaneously measure the incident
irradiance Einc(λ). To match their transfer functions,the inter-calibration of the radiometers was
accomplished by measuring the upwelling radiance(Lcal) of a white Spectralon reflectance standard,simultaneously with incident irradiance (Ecal). An
average of 10 consecutive scans was used to collect
the reflectance spectra at each of the sampling points.
Percentage spectral reflectance R(λ) was computedas:%R(λ) = [L(λ)u / E(λ)inc]×[E(λ)cal / L(λ)cal]× R(λ)cal ×100
R(λ)cal is the reflectance of the Spectralon
panel linearly interpolated to match the band centersof each radiometer.
4. RESULT & DISCUSSION
Field spectroscopy is a useful tool tounderstand the process and application of remotelysensed data. However, satellite and airborne opticalsensors can provide the high spatial and temporal
resolution data and have actual potential role forassessment of water quality parameters in surface
waters. Satellite-based remote sensing provides one
option for relatively low-cost assessment ofsuspended sediment concentration in surface waters[9]. Numerous researchers have applied remotesensing as a tool to measure water quality parametersand developed some algorithms in this endeavour.
The band ratioing technique has proven to beadvantageous because it tends to allow compensationfor variations from atmospheric influences. The
decision involving which bands to use is not always
straightforward [10]. The four bands which aremostly correlated with water quality parameters are
the blue, green, red and NIR. In order to investigate
the filed applicability and feasibility of remotelysensed data to measure precisely the water quality
parameters, the reflectance spectra of Monobe River,
Altamaha River and St. Marys River was acquired.
The acquired spectral signatures at the three rivers
were integrated into ALOS/AVNIR-2 sensor band
widths.

3. METHODOLOGY
3.1 In situ measurements
The in situ water quality data was collected forthe three rivers. In case of Monobe River theconcentration of suspended sediments (mg/l) was
prime parameter and only the suspended sediment(mg/l) data was collected, however, for other two
rivers duplicate measurements were made for Chl-a,
total suspended solids (TSS), salinity and CDOM.
The spectral reflectance data for the same sampling
points were was also collected.
3.2 Hyperspectral reflectance data
The spectral reflectance data of Monobe River,
Kochi, Japan was collected by using Field SpecProHyperspectral FR Spectroradiometer. Reflectancewas calculated as a simple ratio between upwellingradiance from the water surface and upwellingradiance of reference panel. The sensor was
positioned perpendicular to the water surface at theheight of 1 meter above the sample, yielding an
instantaneous field of view 1.75 cm.

500

Band 1 = ∫

420
600

% R(λ ) = [L(λ)t / L(λ )cal ] × R(λ)cal ×100
In case of Altamaha River and St. Marys
River the spectral reflectance measurements werecollected by a dual-fiber system, two inter-calibrated
Ocean Optics USB2000 Spectroradiometers in the
spectral range of 400nm to 900nm. Radiometer # 1,
equipped with a 25o field off view optical fiber was
kept just below the water surface (~3 cm) in thenadir direction to collect the subsurface upwellingradiance Lu(λ). Radiometer # 2, equipped with an

R ( λ )d λBand 2 = ∫R ( λ )d λBand 3 = ∫R ( λ )d λ

520
690

610
890

Band 4 = ∫760

R ( λ )d λThe collected data was analyzed to develop
the relationship between water quality parameters
and remotely sensed data. Regression models were2

developed between the simulated ALOS/AVNIR-2
data and water quality parameters.

The rational is that TSS, Chl-a and turbidity
is significantly high at the Altamaha River. The
spectra were quite similar in shape with diverged
magnitude for different sampling points. In case ofboth the river the reflectance was highly variable inthe visible domain. The reflectance peak in green
domain was 4.2% and 3.6% for Altamaha River andSt. Marys River respectively and less than 1% in red
domain for both rivers. The models were developed
to assess TSS, Chl-a, CDOM, and secchi depth at
Altamaha River and St. Marys River. The
relationship between CDOM and salinity was also
developed. Secchi depth is optical property of waterand is strongly related with water constituents
present in the water bodies.

30

Monobe River

Reflectance, %

25
20
15
10
5
0
400

500

600

700

800

900

Wavelength (nm)Figure 1: Spectral reflectance of Monobe River
water acquired during field campaigns

R e fle c ta nc e , %The reflectance spectra of Monobe River
were acquired during different field campaigns undervarying environmental conditions. Figure 1 depicts
that there is a variation in the spectral magnitude andshape with maximum spectral reflectance of 24%.
For brevity, the spectral data with high suspended
sediment concentration is demonstrated. The spectral
shape shows increasing trend with the increase insuspended sediment concentration. It is observedthat the spectral shape of Monobe River water isalmost same although the signals were acquired at
different times of the year. The reason is that eachfield campaign is followed by heavy rain. The major
constituent in the water is suspended sediments. The
fine sediments brought by the feeding streams affectthe spectral magnitude; however, it is observed thatthe type of sediments is almost same.

St. Marys
River

5
4
3
2
1
0
400

500

600

700

800

900

Wavelength (nm)Figure 3: Subsurface spectral signature of St.
Marys River water
The amount of TSS present in the water
body defines the water category. Chl-a is commonlymeasured in water quality monitoring campaigns forcostal and inland water bodies [11]. The advantageof the developed regression model is that the samemodel can be applied on both the rivers.

The acquired reflectance spectra at
Altamaha River and St. Marys River demonstratethat the spectral signals are high at Altamaha Riveras compared to St. Marys River.

70
60

7
6
5
4
3
2
1
0

Altamaha River50

TSS (mg/l)

R e fle cta nce , %

7
6

40

2

R = 0.08

30
20
2

10

R = 0.07

0
0

3

6

9

12

Chlorophyll-a (µg/l)

400

500

600
700
Wavelength (nm)

800

900

Figure 4: Relationship between TSS (mg/l) and
chlorophyII-a (μg/l)

Figure 2: Subsurface spectral signature of
Altamaha River water

Both the rivers fall under the category ofcase-II waters. Chl-a concentration and TSS were
not related (Figure 4) with the determination3

coefficient of linear relationship R2 < 0.08 & 0.07. It
depicts that Chl-a was not the only characteristic
controlling water quality, confirming that the waters
belonged to typical case-II water group. The ability
to monitor water quality parameters in Case 2 waters
requires high resolution remotely sensed data,
quantitative and qualitative change in reflectancespectra, and the diverse nature of optical constituents
present in the water body.

The simulated ALOS/AVNIR-2 bands, in
combination, were used with regression techniques
to define the relationship between reflectance and
SSC to provide solution for the field application of
remotely sensed data for assessment of suspended
sediments. As illustrated in the figure 5, thecorrelation coefficient of 0.90 was obtained byapplying the combination of two bands approach.
The developed model is of the following form;

4.1

SSC(mg/ l) =m[(B4 + B3) /(B3 / B4 )] + n

Suspended Sediments

The study of suspended sediments in water bymeans of remote sensing is complex and involves
many factors that must be carefully taken into
account. The magnitude and spectral shape varies
with sediments type, texture, grain size andconcentration of suspended sediments in surface
water. The review of intensive literature demonstratethat the red and NIR region is best predictor ofsuspended sediment concentration. However, after
the years of research the scientists and researchers
are unsuccessful to develop global remote sensing
model for estimation of SSC. Based on the dataacquired during experimental work and massive field
campaigns, the multiplicative 2 band algorithm(B4+B3)/(B3/B4) is strongly advocated for theprediction of SSC in water bodies. The Band 3
represents concentration of different suspended
sediments ranging from very fine to medium size.
However, the Band 4 characterizes the sediments
near water surface representing very fine texture.
The addition of Band 3 and Band 4 provides the
spectrum features of low and high concentrations of
the suspended sediments and provides solution for
the estimation of different sizes of sediments within
the water body. However, the influence ofchlorophyll-a concentration on red domain (Band 3)
is well documented in literature. To minimize theinfluence of chlorophyll on reflectance data, theresultant Band (B4+B3) is divided by the Band 3.
This approach helps to minimize the influence of thechlorophyll on SSC. More precisely, by adopting
this approach the influence of chlorophyll is
minimized, however, the spectral signals disturb.
The actual representative of suspended sediment inwater bodies is Band 4. To develop model moreaccurately, the best approach is to multiply with theBand 4. The developed model for estimation ofsuspended sediments in water body is of followingform;(B 4 + B 3 ) (B 3 /B 4 )(B 4 + B 3 )× B4
B3

Where m and n are coefficient with values
19.5 and 69 respectively.

600

S S C (m g /l)

500
400

2

R = 0.90

300
200
100
0
0

5

10
15
20
25
Reflectance, (B 4+B 3)/(B 3/B 4), %30

Figure 5: Relationship between developed model &SSC (mg/l)
4.2 Total Suspended Sediments (TSS)
Total suspended sediments, inorganic andorganic detritus contribute mainly to scattering oflight. In general, the absorption of the totalsuspended particles is very low and is negligible forthe inorganic particles. Inorganic particles are due tosuspended sediments brought by the feeding stream.
Total suspended sediments shows good correlation
with NIR/Green (Band 4/Band 2) band ratio. The
NIR domain (750~900nm) absorbs in water and any
reflectance in this range is because of suspended
sediments present in the water body. However, thereflectance peak in green domain represents theamount of TSS present in the water body. The
reflectance ratio of ALOS/AVNIR-2 B4/B2 shows
correlation coefficient r2 = 0.73 for Altamaha Riverand 0.74 for St. Marys River.

TSS(mg/ l) = m(B4 / B2 )2 − n(B4 / B2 ) + l
Value of empirical coefficients for
Althamaha River is m = 34.8, n = 127, l = 44, andfor St. Marys River is m = 6.5, n = 17.4, l = 13

⎡B4⎤
+ 1⎥ × B 4
⎢
⎣B3
⎦

4

30

6

25

5
4
2

3

R = 0.75

AltamahaRiver

St. Marys
River

20

TSS (mg/l)

C h l-a(µ g/l)

7

2

2

R = 0.71

15
10
2

1

2

R = 0.82

0

R = 0.77

5
0

0

4

8

12

16

20

0

24

0.5

18

10

15

8
2

R = 0.82

962

R = 0.87

3

R = 0.91

6
4
2

R = 0.97

2

0

2

2

-1

12

CDOM (m )Secchi Depth (m )Reflectance, B3 /B1

1
1.5
Reflectance, B4 /B2

0

0

0.5

1

1.5

2

2.5

0

3

Reflectance, B3 /B2

0.1

0.2
0.3
0.4
Reflectance, B1 /B2

0.5

0.6

Figure 6: Relationship between water quality parameters and simulated AVNIR-2 band ratios
4.3 ChlorophyII-a

Chl− a(μg / l) = m(B3 / B1)2 + n(B3 / B1) + l

Phytoplankton cells are strong absorbers ofvisible light and therefore play a major role indetermining the absorption properties of natural
waters. The concentration of the main phytoplankton
pigment, chlorophyll-a, is often taken as an index ofphytoplankton biomass, although two major colour
phytoplankton groups – green and brown – also
contain chlorophylls-b and -c, respectively [12]. The
chlorophyll-a is an indicator of the abundance ofphytoplankton in the water. The pronounced
scattering/absorption features of chlorophyll-a are:strong absorption between 400–500nm (blue) and at
680nm (red), and reflectance maximums at 550 nm(green) and 700nm (NIR) [13]. The scattering andabsorption characteristics of chlorophyll-a can bestudied when more than one band is used (Dekker etal. 1991). Keeping in view the absorption andscattering relationship, the reflectance ratio of
R700/R670 is applied. The correlation coefficient R2
was 0.88 for Altamaha River and 0.71 for St. Marys
River. A basic principle of using band ratios
technique is to select two spectral bands that are
representative of absorption/scattering features ofchlorophyll-a (Gin et al. 2002).

Value of empirical coefficients for
Althamaha River is m = -0.12, n = 0.67, l = 4.5, andfor St. Marys River is m = -0.02, n = 0.41, l = 3.2.
4.4 CDOM
Both fresh and saline waters contain varying
concentrations of colored dissolved organic matters.
These compounds are generally brown in color, andin sufficient concentrations can color the water
yellowish brown. For this reason the compounds are
generically referred to as yellow matter or CDOM,
colored dissolved organic matter. Other common
names are gelbstoff and gilvin. The presence ofchlorophyll, TSS and CDOM usually dominate thewater colour. The increase in the CDOM
concentration affects the reflectance values in theblue and green region of the spectrum. CDOM
absorption is characterized by a strong absorption in
the blue wavelength region, exponentially declining
towards longer wavelengths. To achieve asatisfactory degree of CDOM concentration retrieval
the relationship between absorption by CDOM at5

water bodies. Remote sensing coupled with in sitemeasurements is effective technique for monitoringwater quality parameters. Remote sensing is a power
tool to monitor the water quality potentially up to
several times per year and offer valuable data on theseasonal variability of water quality. The ability tomonitor water quality parameters in Case-II waters
requires high resolution remotely sensed data,
elucidation of quantitative and qualitative change inreflectance spectra and the diverse nature of opticalconstituents present in the water body.

440 nm and band ratio (B1/B2) showed goodcorrelation.

CDOM (m −1 ) = m( B1 / B2 ) 2 − n( B1 / B2 ) + l
Value of empirical coefficients for
Althamaha River is m = 64.7, n = 59.3, l =13.5 andfor St. Marys River is m = 67.36, n = 58.7, l = 12.9
The relationship between CDOM and Salinity wasdeveloped with high degree of correlation.

Salinity ( ppt ) = a ln(CDOM , m −1 ) + b
Value of empirical coefficients for Althamaha Riveris a = -10.2, b=12.7 and for St. Marys River:a = -10.3, b=23.4

The study of suspended sediments in waterby means of remote sensing is complex and involves
many factors that must be carefully considered. The
multiplicative two band approach is effective forassessment of suspended sediments in water bodies.
By adopting this approach, the influence of otherparameters on suspended sediment assessment isminimized. It was found that the red domain andblue domain is well correlated with chlorophyll
concentration and the ratio of simulated
ALOS/AVNIR-2 Band 3/Band1 is well correlatedwith chlorophyll-a concentration. The ratio of NIR
and green domain (Band 4/Band 2) is best predictorof TSS. Color dissolved organic matters (CDOM)
were found to be correlated with Band 1/Band 2. It
was noticed that CDOM and salinity are strong
correlated and the salinity may estimated indirectly
by applying developed CDOM and salinity
relationship. The optical depth depends on the Chl-aand TSS concentration. The secchi depth and theratio of Band3/Band 2 is well correlated and the ratio
Band3/Band2 is found to be representative of opticaldepth in water bodies. The developed algorithms canbe successfully applied for water quality monitoring.
Empirical and semi-empirical algorithms are easy touse, however, the coefficients used in empiricalalgorithms are derived from data sets that do notnecessarily represent all natural variations. The
performance of such algorithms is always subject tocompatibility between the waters under study andthe waters from which data were obtained foralgorithm development. The clear vision andunderstanding of relationship between water quality
parameters and optical measurements made bymeans of remote sensing techniques is imperative formonitoring water bodies. This study demonstrates
that the remotely sensed data can be a useful tool formonitoring the distributions of water quality
parameters in coastal and inland waters. The water
quality and quantity “Q & Q” connection” is vital forsustainable water resources development &management. Water should be recognized as a toolfor community development and peace building. In
the future, it will be important to incorporate water
quality assessment as an integral part of water
resources and environmental planning andSalinity (ppt)

40
32
24

2

R = 0.93

16
8

2

R = 0.95

0
0

2

4
6
CDOM (m-1)

8

10Figure 7: Relationship between salinity & CDOM
4.5 Secchi depth
Secchi disk is a subjective visual technique.
and defines the optical transparency of the water.
The secchi depth is inversely correlated with theamount of TSS present in the water bodies. Thismethod relies on human visual perception and muchtime and effort is required. The ratio of red andgreen domain B3/B2 provides information about thesecchi depth.

SD ( m) = m( B3 / B2 ) 2 − n( B3 / B2 ) + l
Value of empirical coefficients for
Althamaha River is m = 4.2, n = 5.7, l=7.9, and for
St. Marys River is m = 5.8, n = 20.65, l=25.
Concluding Remarks
Remotely sensed data is effective to monitor
water quality parameters and to develop a better
understanding of light/water/substance interactions.
Hyperspectral remote sensing allows accurate andpotential use of entire range of electromagnetic
spectrum recorded in extremely narrow wavebands
for monitoring water quality on multiple sites in6[10] Jensen, J.R., 2005, “Introductory Digital
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derivative of reflectance in estimating algalchlorophyll concentration: a case study in aturbid reservoir”, Remote Sensing ofEnvironment, 62, pp. 253–261.

management. Without this approach the problems of
the water & environmental sector are going tocompound in the future.
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