16-Monitoring of Total Suspended Solids and Sea Surface Temperature Using NOAA AVHRR Data in Malaysia[1]

MASAUM Journal of Basic and Applied Sciences, Vol. 1, No. 3, October 2009

395

Monitoring of Total Suspended Solids and

Sea Surface Temperature using NOAA

AVHRR Data in Malaysia

H. S. Lim

1, M. Z. MatJafri

1, K. Abdullah

1, K. C. Tan

1, F. Tan

1, N. Mohd. Saleh

1, Z. Yasin

2 and A. L. Abdullah

3

Abstract—Water pollution exists since long time ago and is

coeval with the appearance of humans. It becomes one of the

major menaces in human pollution. Furthermore, the problem

worsens with the increasing of time, whether in developed or

developing countries. The main contribution for water

pollution problem is caused by the sediment. There are few

methods to detect water pollution. Usually, this can be done

through measurement by using ground instruments, such as

turbidity meters for water measurements. In this study, the

National Oceanic and Atmospheric Administration Multi-

Channel Sea Surface Temperature (SST) (NOAA MCSST)

algorithm was used to determine the sea surface temperature

(SST) at South China Sea by suing NOAA-AVHRR data. This

study included remote sensing of total suspended solids (TSS)

on the surface of water. In AVHRR radiometric correction and

calibration, the DN values were converted into radiance and

reflectance values. The reflectance values acquired from the

ground truth sample locations were extracted from all the

images. In order to minimize the atmospheric effects within

multi-temporal data, the correction was performed between

the scenes. The results revealed that the data set produced

higher correlation coefficient and lower RMS value for window

size of 3 by 3, in terms of reflectance values. Therefore, it was

used in this study. Finally, an automatic geocoding technique

from PCI Geomatica 9.1.8 - AVHRR Automated Geometric

Correction was applied in this study to geocode the SST and

TSS maps.

1. INTRODUCTION

Advanced Very High Resolution Radiometer (AVHRR)

data from National Oceanic and Atmospheric

Administration (NOAA) is well-known as an effective

sensor to capture image and have been widely use in

oceanographic applications such as sea surface temperature

(SST) studies, water quality mapping and other oceanic

phenomena. In addition, AVHRR provides useful data for

monitoring oceanic phenomena due to its high repetitive

coverage.

1School of Physics, Universiti Sains Malaysia, 11800, Penang, Malaysia.

+604-6533888, Fax: +604-6579150,[email protected], [email protected],

[email protected], [email protected] 2School of Biological Sciences, Universiti Sains Malaysia,11800, Penang,

Malaysia.+604-6533888, Fax: +604-6579150,[email protected] 3School of Humanities,Universiti Sains Malaysia, Minden 11800, Penang,

Malaysia.+604-6533888, Fax: +604-6579150, [email protected]

Since a long time ago, sea surface temperature and water

quality measurements have been carried out by using

conventional shipboard since this technique produces

accurate readings. However, this technique involved a huge

expense and it is very time-consuming. Thus, an alternative

reliable method can be chosen through satellite

measurements. For example, accurate retrieval of SST and

total suspended solids (TSS) using AVHRR data can easily

be computed using the established algorithm.

Usually, SST algorithms were determined by two

basic techniques: i) theoretical atmospheric transmittance

model with a set of representative vertical profiles of

atmospheric temperature and absorbing constituents, and ii)

regression analysis between coincident measurements of

brightness temperatures and the satellite brightness

temperatures [1]. Many studies have been focusing on SST

algorithms and various algorithms for performing this task

have been developed, such as multichannel and multi-angle

method, and the single infrared channel. Recently, NOAA

has developed the multichannel sea surface temperature

(MCSST) algorithm for AVHRR data.

Implementation of regression techniquewill lead to

difficulty since it requires a lot of coincident in-situ data

with the remotely sensed data. In addition, the desired data

are not easily acquired in equatorial region due to the

obstacle from cloud cover in the study area. Thus in this

study, the NOAA developed multichannel sea surface

temperature (MCSST) algorithm was used to measure SST

using NOAA-AVHRR satellite data.

2. STUDY AREA

The study area is located is the South China Sea which

covers Peninsular Malaysia and South East Asia. It is one of

the world’s largest seas. This area is chosen due to the

fluctuations in SST. There are strong relationships between

fluctuations in SST and rainfall variability in the equatorial

Pacific.

The data was acquired from image with level 1B

scene of AVHRR sensor from NOAA satellite. Data

selection was made based on availability of cloud-free

pixels. The images were captured daytime on 29/06/2004,

30/06/2004, 03/07/2004, 04/07/2004, 05/07/2004,

08/07/2004, 13/07/2004 and 16/7/2004. In this study,


396

NOAA AVHRR 17 data were used to retrieve SST [2].

Figure 1:The location of the study area

3. CLOUD MASKING TECHNIQUES

The atmospheric effects such as cloud cover, water vapor,

reflected sky and the presence of other gases will cause the

error in satellite measurements in SST. Normally, the errors

were dominated by the cloud contaminated pixels of SST

satellite data. Hence, the existence of cloud becomes major

obstacle for the application of SST, especially in the

equatorial region. Application of remote sensing data for

oceanographic studies such as sea surface temperature

measurements normally depends on the accuracy of the

cloud masking techniques used to eliminate cloud-

contaminated pixels. Therefore, few cloud masking

techniques should be applied to successfully determine the

SST in the study area.

In this study, two cloud masking techniques were

implemented, (a) the infrared threshold test or gross cloud

check, and (b) the channel difference. For each image data,

small sub-scenes ( channels 1, 2, 4, 5) containing clouds and

cloud free water pixels (channels 4 and 5) were extracted for

a detailed study using each technique mentioned above. The

combination of cloud masks were finally obtained after the

data have been filtered through a series of cloud masking

techniques described earlier.

3.1 The infrared threshold test or gross cloud check

This technique uses 12 µm (channel 5) brightness

temperatures test. Cloud at channel 5 tends to have greater

optical thickness as compared with other channel and it is

suitable to be used for cloud masking. Pixels in satellite data

defined as cloudy if the calculated temperature is less than a

pre-defined threshold value. The thresholds need to be

generated from the histogram of the brightness temperature

generated image. This test is applicable for both day and

night scenes. This method cannot used to detect a warm low

cloud above the sea.

3.2 The channel difference

The temperature difference between T4 and T5 is suitable to

be implemented to both day time and night time. Besides

that, this test is effective in detecting thin cirrus cloud and

the edges of thicker cloud. This technique is applicable to

detect most types of clouds except for uniform low cloud.

Usually, over the cloud the brightness temperature

differences are higher than over the clear sea water.

However, for clear sky radiances, the differences are less

(often < 1K) and fluctuate with total column water content

and satellite zenith angle. All pixels with T4-T5>Tdiff

(threshold for temperature difference) are then identified as

cloud contaminated.

4. ALGORITHM MODEL

4.1 SST Model

The computation of SST becomes more prominent with the

AVHRR, an instrument included with three infrared (IR)

channels [3]. With the aid of three AVHRR thermal bands

(channels 3, 4, and 5) and the AVHRR sensor calibration

information, SST was computed in this study. The proposed

module based on the McClain method for atmospheric

correction. Firstly, SST module used to calculate the

temperature, in ° C for every single pixel in an image,

included the land and water area. However, this module is

suitable for SST computation in ocean precisely.

Temperature values for land, cloud, or inland water bodies

can be used as rough indicators if necessary, but in general

should be ignored.

Four algorithms are available, one for daytime data

and three for nighttime data: Day MCSST Split; Night

MCSST Split; Night MCSST Dual; and Night MCSST

Triple. All of these algorithms differ with the bands, which

are utilized for the atmospheric correction, Split-window

uses bands 4 and 5, Dual-window uses bands 3 and 4, and

Triple-window uses bands 3, 4 and 5. All the coefficients

used for the NOAA 15, 16, 17 satellites are based on values

provided in the NOAA KLM User’s Guide. In this study,

Day MCSST Split algorithm was used to retrieve SST from

NOAA AVHRR.

Mostly of the retrieval algorithms for the SST

computation using AVHRR are mainly based on the

MCSST algorithm [4], which can be written as:

SST=A 1+A2T4 +A3(T4 - T5)+A4(T4 - T5)(secө-1) (1)

where A1, A2, A3 and A4 are algorithm coefficient, T4 and T5

are brightness temperatures as derived from channels 4 and

5 and ө is zenith angle.

4.2 TSS Model

For water quality studies, the effective way to analyze

remotely sensed data comes from the implementation of

appropriate technique. For example, a physical model

relating radiance from the water column and the

concentrations of the water quality provide the most

effective way to analyze the data involved in water quality


397

studies. Reflectance is particularly dependent on inherent

optical properties: the absorption coefficient and the

backscattering coefficient. The irradiance reflectance just

below the water surface, R(λ), is given by [5]

R(λ) = 0.33b(λ)/a(λ) (2)

where

λ = the spectral wavelength

b = the backscattering coefficient

a = the absorption coefficient

The inherent optical properties are determined by

the contents of the water. The contributions of the individual

components to the overall properties are strictly additive [6].

For a case involving water quality component,

chlorophyll, C, the equation given by other studies [7] can

be expressed as

))1(*

)1((

))1(*

)1(5.0(33.01)1R(

Cawa

Cbc

bbw

b

R

cλλ

λλλ

+

+

== (3)

where

bbw ( iλ ) =water backscattering coefficient

bbc *(

iλ ) =chlorophyll specific backscattering coefficient

aw ( iλ ) =pure water absorption coefficient

ac * =chlorophyll specific absorption

coefficient

C =chlorophyll concentration

Chlorophyll concentration can be obtained by solving

Equation (3) to Equation (4) that is given as

)(165.0165.0

33.0

165.0

)(1

**

λ

λ

Rb

a

b

b

b

Ra

C

bw

c

bw

bc

bw

w

+−

−

= (4)

We have to know two parameters (the backscattering

and absorption coefficients) to solve Equation 4. However,

both of these parameters were not available for this study.

Therefore, the regression analysis technique was chosen to

solve this equation. From equation 4, the regression model

can be simplified to obtain the equation for chlorophyll.

)(

)(1

21

0

λ

λ

Raa

RaC

+

+= (5)

where a0, a1 and a2 are the algorithm coefficients that can be

solved empirically.

5. DATA ANALYSIS AND RESULTS

5.1 SST in South China Sea

For further study, the cloud free area of sea water pixels

(after cloud masking process) for bands 4 and 5 were

extracted from the satellite image. All the related analysis

was carried out by using PCI Geomatica version 9.1.8 image

processing software. The NOAA AVHRR (the South China

Sea and the Straits of Malacca) data used in this study were

captured on 29/06/2004, 30/06/2004, 03/07/2004,

04/07/2004, 05/07/2004, 08/07/2004, 13/07/2004 and

16/7/2004. Bands 10, 11, and 12 were used for SST retrieval

and cloud free sea water pixels were extracted. AVHRR

Radiometric and Calibration was carried out to convert DN

values to radiance values and later to brightness

temperatures using PCI Geomatica 9.1.8. An automatic

geocoding technique from PCI Geomatica 9.1.8 (AVHRR

Automated Geometric Correction) was applied in this study

to geocode the SST maps. Finally, the SST maps were

generated and colour-coded for visual interpretation. Figure

2 shows the SST map using NOAA AVHRR satellite

imageries.

(a) (b)

(c) (d)


398

(e) (f)

(g) (h)

Legend for SST

SST < 24 °C

SST = (24-26) °C

SST = (26-28) °C

SST = (28-30) °C

SST = (30-32) °C

SST > 32 °C

Figure 2: SST maps using the NOAA algorithm,

(a)29/06/2004, (b)30/06/2004, (c)03/07/2004,

(d)04/07/2004, (e)05/07/2004, (f)08/07/2004, (g)

13/07/2004 and (h)16/7/2004.)

5.2 Water quality in South China Sea

This study included remote sensing of total suspended solids

(TSS) on the surface of water. NOAA AVHRR satellite data

(channel 1 and channel 2) were used for this purpose. At the

same time, the ground truth measurements of TSS were

conducted during Research on the Sea and Islands of

Malaysia (ROSES)’s project. The NOAA AVHRR data

used earlier in SST analysis were again used for water

quality mapping. First, AVHRR Radiometric Correction and

Calibration was carried out for converting DN values to

radiance values and then reflectance values. The reflectance

values corresponding to the ground truth sample locations

were extracted from all the images. Window size of 3 by 3

was used in this study because the data set produced higher

correlation coefficient and lower RMS value. The regression

analysis generates all the coefficients of Equation 5). The

proposed algorithm produced high accuracy between

measured TSS and estimated TSS (Figure 3). Then, the TSS

map was generated using the proposed algorithm. After that,

AVHRR Automated Geometric Correction was applied to

all the generated TSS maps through an automatic geocoding

technique to geocode these maps. Finally, the TSS maps

were colour-coded for visual interpretation.

(a) (b)

(c) (d)

(e) (f)


399

(g) (h)

Legend for TSS

TSS < 20 mg/l

TSS = (21-40) mg/l

TSS = (41- 60) mg/l

TSS > 60 mg/l

Figure 3: TSS maps using the proposed algorithm,

(a)29/06/2004, (b)30/06/2004, (c)03/07/2004,

(d)04/07/2004, (e)05/07/2004, (f)08/07/2004, (g)

13/07/2004 and (h)16/7/2004.)

6. CONCLUSION

Environmental pollution such as water pollution can be

successfully indentified through the implementation of

appropriate remote sensing technique. In this study, NOAA

AVHRR imagery effectively provides useful data for TSS

and SST mapping in the South China Sea. The SST values

in this study region were in the range from 24°C to 32°C.

For further research, more coincident SST values should be

collected for more accurate verification.

7. ACKNOWLEDGEMENTS

We would like to thank the technical staff who participated

in this project. Thanks are also extended to USM for support

and encouragement.

REFERENCES

[1] Barton, I. J., 1995. Satellite derived sea surface

temperatures: Current status. Journal of Geophysical

Research, 100: 8777-8790.

[2] NOAA Polar Orbital Data user’s Guide Section 3.0, 1998,

Available Online:

http://www2.ncdc.noaa.gov/docs/podug/html/c3/sec3-

0.htm.

[3] Schwalb, A., 1978. The TIROS-N/NOAA A-G Satellite

Series; NOAA Technical Memorandum NESS 95,

Washington, D.C.

[4] Kidwell, K. B., comp. and ed., 1995, NOAA-14 Polar

Orbiter Data (TIROS-N, NOAA-14-6, NOAA-14-7,

NOAA-14-8, NOAA-14-9, NOAA-14-10, NOAA-14-11,

NOAA-14-12, and NOAA-14) Users Guide: Washington,

D.C., NOAA-14/NESDIS.

[5] Kirk, J. T. O. (1984), Dependence of relationship between

inherent and apparent optical properties of water on solar

altitude. Limnology and Oceanography, 29, 350−356.

[6] Gallegos, C.L. and Correl, D.L. (1990), Modeling spectral

diffuse attenuation, absorption and scattering coefficients

in a turbid estuary. Limnology and Oceanography, 35,

1486−1502.

[7] Gallie, E.A. and Murtha, P.A. (1992), Specific absorption

and backscattering spectra for suspended minerals and

chlorophyll-a in Chilko Lake, British Columbia. Remote

Sensing of Environment, 39, 103−118.

BIOGRAPHY

Lim Hwee San is a lecturer at the school of physics, Universiti Sains

Malaysia. He obtained his B.Sc. from USM in Geophysics in 2001 and

M.Sc. from USM in remote sensing in 2003. He obtained his Ph.D

in environmental remote sensing from USM in 2006. His research

interests are remote sensing applications for water quality monitoring, air

quality monitoring, land surface properties and digital images

classification. He is a Member of the International Society for Optical

Engineering, US.

Mohd. Zubir Mat Jafri is a lecturer at the school of physics, Universiti

Sains Malaysia. He obtained his B.Sc. in Physics (1984) from Universiti

Kebangsaan Malaysia, his M.Sc. in microprocessor technology and

application (1991) from Brighton Polytechnic, UK, and his Ph.D. from

University College of Swansea, Wales (1996) in research area of algorithm

development for detecting curve from digital images. He has more than 20

years teaching experience in the area of physics, optical communication,

digital and analogue electronic and also microprocessors. He is also active

in research work on current-based system, automation visual inspection

system, digital image processing, and remote sensing. He has published

more than 100 articles in these area. He is a Life Member of Malaysian

Institute of Physics and Member of the International Society for Optical

Engineering, US.

Khiruddin Abdullah received his B.Sc. (Physics) in 1982 from Bedford

College, University of London, and M.Sc. (Geophysics) from Imperial

College, London, in 1984, and his Ph.D. in remote sensing from the

University of Dundee, Scotland in 1994. he is currently a faculty member

at the School of Physics, Universiti Sains Malaysia teaching courses in

geophysics and remote sensing. His research interests include remote

sensing applications in marine and coastal environments. Presently, he is

working on remote sensing algorithm for retrievals of water quality

parameters and sea surface temperature.

Tan Kok Chooi is a MSc student at the school of physics, Universiti Sains

Malaysia. He obtained his B.Sc. from USM in Medical Physics in 2008.

His research interests are remote sensing applications for water quality

monitoring, air quality monitoring, land surface properties and digital

images classification.

–


400

Tan Fuyi is a MSc student at the school of physics, Universiti Sains

Malaysia. He obtained his B.Sc. from USM in Geo Physics in 2009. His

research interests are remote sensing applications for water quality

monitoring, air quality monitoring, land surface properties and digital

images classification.

Nasirun Mohd. Saleh received his B.Sc. (Geophysics) in 1982 from

(USM), M.Sc. (Meteorology) from Reading, UK, in 1985, and M.Phil.

(Boundary Layer Meteorology) from East Anglia University, UK in 1997.

He is currently a Director of USM's Islamic Centre and Coordinator of

Astronomy and Atmospheric Science Research Unit, Universiti Sains

Malaysia. He is currently a faculty member in the School of Physics,

Universiti Sains Malaysia, teaching courses in geophysics, meteorology

and remote sensing. His research interests include meteorology

applications.

Zulfigar Yassin is a lecturer at the school of physics, Universiti Sains

Malaysia. His research has been focused on the marine and coral reef

ecology, particularly the invertebrates, since 1982. Has successfully

worked on the biology and reproduction of sea cucumbers for the

pharmaceutical and food industries. Has been actively involved in

conservation studies on the Malaysian reefs as well as the reefs around the

region, focussing on the molluscs and echinoderm groups. Had vast

experience in the study of corals and other marine benthic organisms. Has

been a member of the Natio nal Advisory Council of Malaysian Marine

Park since 1990. Had led a team of scientists from the university to conduct

a project on marine science in Antarctica for three months in 2002/2003,

the longest marine science voyage to the Antarctica.

Anisah Lee Abdullah is a lecturer at the school of physics, Universiti

Sains Malaysia. Her research has been focused on the Environmental

Remote Sensing; Integrated Coastal Zone Management; Coral Reef

Studies.


401

Documents

16-Monitoring of Total Suspended Solids and Sea Surface Temperature Using NOAA AVHRR Data in Malaysia[1]