APPLICATIONS OF MICROWAVE REMOTE SENSING
IN COASTAL OCEANOGRAPHY
Mohd Ibrahim Seeni Mohd and Samsudin Ahmad
Department of Remote Sensing
Faculty of Geoinformation Science and Engineering
Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor
Tel.: 07 – 5530800 Fax.: 07-5566163 http://www.fksg.utm.my
ABSTRACT
The use of radar remote sensing in the microwave region of the
electromagnetic spectrum has many advantages in comparison with optical remote
sensing in the visible and infrared wavelengths. By far the most important factor is
the virtual insensitivity of radar to atmospheric conditions. This allows the regular
collection of site observations independent of cloud cover or time of overpass. On the
other hand, the interpretation of radar imagery over land and ocean is not as
straightforward as the more commonly used visible and infrared remote sensors.
Usually special image processing techniques must be applied on the radar imagery to
make it more readily interpretable. Furthermore, the interpretation of the
backscattering process that underlies the radar image formation must be well
understood with respect to the physical characteristics of the targets under
observation and the specifics of the radar instrument.
This paper reports on the results of some studies that have been carried out on
coastal oceanography applications using passive and active microwave remote
sensing, (i) sea surface temperature from Tropical Rainfall Measuring Mission
(TRMM) TMI satellite data, (ii) oil spills/slicks from Radarsat SAR data, (iii) ocean
wavelength and direction, shallow water bathymetry and water fronts from ERS SAR
data.
1.
Introduction
Satellite remote sensing using the visible and the infrared wavelengths has been used
successfully in various applications related to earth resources studies and monitoring of the
environment. This is also the case for applications related to the oceanographic environment. For
instance, useful results have been obtained in sea surface temperature, suspended sediment
concentration, ocean colour and bathymetry studies. However, in some applications, there are
limitations in the use of the optical wavelengths such as in studies related to oil spills/slicks, sea
bottom topography in turbid waters and ocean waves. One of the major limitations of operating in
these wavelengths is the cloud cover problem over the tropical areas. In these areas, data acquired in
the microwave region of the electromagnetic spectrum would be free from cloud cover since
microwave penetrates through clouds. Interpretation of microwave imagery over land and ocean are
not as straightforward as that of the visible and infrared remote sensing imageries. Usually, special
image processing techniques must be applied on the microwave imagery to make it more readily
interpretable.
In this paper, some coastal oceanography applications, i.e. sea surface temperature, oils
spills/slicks, ocean wavelength and direction, shallow water bathymetry and water fronts from various
satellite data are presented.
Paper presented at the 3rd National Microwave Remote Sensing Seminar, organised by Malaysian
Centre for Remote Sensing (MACRES), 28 September 2004, Kuala Lumpur.
2.0
Sea Surface Temperature from Tropical Rainfall Measuring Mission (TRMM) TMI
Satellite Data
TRMM is a joint mission between the National Aeronautics and Space Administration
(NASA) of the United States and the National Space Development Agency (NASDA) of Japan
(NASA Facts 1997). TMI is a nine-channel 10.7 GHz passive microwave radiometer with horizontal
and vertical polarizations. TMI is 65 kg in weight and requires 50 W of main power to obtain data in
8.5 kbps (Kummerow and Barnes, 1998). The characteristics of TMI are summarized in Table 1.
Table 1.
Channel Number
TMI characteristics of the 9 channels.
1
2
3
4
5
6
7
8
9
10.65
10.65
19.35
19.35
21.3
37.0
37.0
85.5
85.5
V
H
V
H
V
V
H
V
H
Beam Width (degree)
3.68
3.75
1.90
1.88
1.70
1.00
1.00
0.42
0.43
IFOV-DT (km)
59.0
60.1
30.5
30.1
27.2
16.0
16.0
6.7
6.9
IFOV-CT (km)
35.7
36.4
18.4
18.2
16.5
9.7
9.7
4.1
4.2
Integrated time (ms)/sample
6.60
6.60
6.60
6.60
6.60
6.60
6.60
3.30
3.30
EFOV-CT (km)
9.1
9.1
9.1
9.1
9.1
9.1
9.1
4.6
4.6
EFOV-DT (km)
63.2
63.2
30.4
30.4
22.6
16.0
16.0
7.2
7.2
Number of EFOVs per scan
104
104
104
104
104
104
104
208
208
4
4
2
2
2
1
1
1
1
63x37
63x37
30x18
30x18
23x18
16x9
16x9
7x5
7x5
26
26
52
52
52
104
104
208
208
Center Frequency (GHz)
Polarization
Number of Samples (N)/beam
width
Beam EFOV (km x km)
Number of Beam EFOVs per
scan
The instantaneous field of view (IFOV) is the footprint resulting from the intersection of
antenna beamwidth and the earth’s surface. Due to the shape of antenna and incident angle, the
footprint can be described by an ellipse. The ellipse’s major diameter is in the down-track direction
called IFOV-DT and minor diameter in cross-track direction called IFOV-CT. The EFOV is the
position of the antenna beam at the midpoint of the integration period. Figure 1 shows the IFOV and
EFOV of TMI footprint characteristics.
Sea Surface Temperature (SST) is one of the geophysical parameters which are required by
researchers for various applications. In this study, TRMM Microwave Imager (TMI) which is one of
the payloads of TRMM satellite was used to retrieve SST in the South China Sea. The TMI data are
suitable in tropical areas for obtaining SST values without cloud cover problems.
The study was conducted in South China Sea located between 2o N to 7o N and 100o E to
107 E which includes Malaysia, Indonesia, Thailand, Taiwan and Philippines (Figure 2). This area
undergoes climate changes during the monsoon and inter-monsoon periods. The SST values also vary
during these periods.
o
2
85.5 GHz IFOV at
integration start
85.5 GHz IFOV at stop
85.5 GHz EFOV
single sample EFOVCT of 85.5 GHz
37 GHz EFOV
19.5 GHz IFOV at
integration stop
19.5 GHz IFOV
at integration
start
19.35 GHz EFOV
EFOVx of 37.0, 19.35,
21.3 and 10.65 GHz : all
are 9.1 km in CT
direction
10.65 GHz EFOV
10.65 GHz IFOV at
integration stop
10.65 GHz
IFOV at
integration
start
Figure 1.
TRMM Microwave Imager footprint characteristics.
(Source: Kummerow and Barnes 1998)
Figure 2: Study area.
2.1
TMI Data Processing
Daily binary data file of TMI are available at ftp site, consisting of fourteen 0.25 x 0.25
degree grids of 1440 x 320 byte maps. Seven ascending maps include SST and six other geophysical
parameters. The center of the first cell of the 1440 column and 320-row map is located at 0.125 E and
–39.875 N latitude while the center of the second cell is at 0.375 E longitude, -39.875 N latitude. All
the data values fall between 0 and 255. Specific values have been reserved as follows:
255
254
253
252
251
0 to 250
=
=
=
=
=
=
land mass
no TMI observations
TMI observations exist, but are bad
‘data set not used’
missing wind speed due to rain, or missing vapour due to heavy rain
valid geophysical data.
3
For the TMI data, the 3-day, weekly and monthly binary files are similar to the daily TMI
binary files. All the data consists of six maps with grid size of 0.25o by 0.25o and each file can be read
as a 1440, 320, 6 array. For the data processing, the data values between 0 and 250 need to be scaled
to obtain meaningful geophysical data. In order to obtain the SST from the binary data, the data have
to be multiplied by the scale factors as expressed below,
SST = (DATA * 0.15) – 3.0 to obtain SST between –3oC and 34.5oC
This algorithm is based on a model for the brightness temperature (TB) of the ocean and
intervening atmosphere. The model and algorithm are precisely calibrated using a very large in-situ
database containing SSMI observations. FORTRAN Programming Language is used to evaluate the
SST values in each of the binary data starting from January 1998 until December 2002 during the
north east and south west monsoon period.
2.2
Results
The SST values derived from TMI data using FORTRAN Programming Language for some
dates during the north east and south west monsoon periods are tabulated in Table 2. The TMI
scanning system causes striping that contains 0 or invalid data. Samples were taken at locations where
data are available (no striping). Regression analysis was carried out using the SST from TMI data and
in-situ data obtained from the Meteorological Department of Malaysia. Table 2 also shows the in-situ
SST values during the north east and south west monsoon periods. SST values were derived from
TMI data at 1031 points and compared with the corresponding in-situ SST values. The twodimensional scatter plot between TMI data and in-situ data gives a R2 value of 0.92 (Figure 3) and
RMSE of 0.3oC.
Regression analysis was also carried out during the north east (November to March) and
south west (June to August) monsoon seasons for the year 1998, 1999, 2000, 2001 and 2002. The
results are tabulated in Table 3.
33
32
TMI SST (oC)
31
y = 0.9664x + 0.9592
R2 = 0.9197
30
29
28
27
26
25
24
24.0
25.0
26.0
27.0
28.0
29.0
30.0
31.0
32.0
33.0
Insitu SST (oC)
Figure 3 : Regression of TMI SST versus in-situ SST from January 1998 to December 2002.
In order to study the variations of SST, the TMI binary data were generated in image form.
These generated images show the distribution of the SST during the two monsoon seasons. Figures
4(a) to 4(j) show the distribution of SST during the north east and south west monsoons from 1998 to
2002.
4
Table 2 : SST values derived from TMI data and corresponding in-situ SST values
for a part of study area.
Year
1998
1998
1998
1998
1998
1998
1998
1998
1999
1999
1999
1999
1999
1999
1999
1999
2000
2000
2000
2000
2000
2000
2000
2000
2001
2001
2001
2001
2001
2001
2001
2001
2002
2002
2002
2002
2002
2002
2002
2002
Key :
Date of data
Month
1
2
3
6
7
8
11
12
1
2
3
6
7
8
11
12
1
2
3
6
7
8
11
12
1
2
3
6
7
8
11
12
1
2
3
6
7
8
11
12
Day
15
20
19
15
9
15
25
14
28
19
11
14
14
19
15
16
14
19
24
16
16
19
25
14
10
10
15
1
7
14
18
26
3
10
15
9
8
20
10
8
Location (degrees)
Longitude
Latitude
4.6
106.1
1.4
105.4
5.4
106.8
1.8
106.8
4.0
105.8
5.0
105.2
4.3
105.8
3.6
105.6
5.1
106.5
5.3
103.9
3.9
105.9
5.0
099.2
5.6
103.6
3.8
105.9
2.0
104.8
4.5
106.2
2.3
104.9
1.1
105.3
5.9
105.9
5.2
106.6
5.2
106.9
2.3
106.7
5.1
106.6
2.9
105.2
4.0
106.0
2.4
106.9
4.5
106.3
14.0
118.8
13.6
110.7
2.9
105.2
14.0
118.8
5.1
106.6
3.8
105.9
2.3
106.7
1.7
105.4
13.3
118.8
5.2
106.6
2.3
106.7
3.8
105.9
4.1
106.7
north east monsoon period
5
SST Data (oC)
TMI
In-situ
27.675
28.0
28.95
29
28.875
29
29.625
29.3
30
30
29.1
29.0
28.8
28.4
27.9
28.0
27.45
27.0
29.1
29.0
29.7
30.0
30.9
30.8
30
30.0
30.15
30.0
28.8
29.0
27.15
27.2
28.05
27.6
26.85
26.8
25.8
25.7
29.25
29.4
28.95
28.8
28.5
28.5
28.05
28.0
28.8
29.0
27.45
27.0
27.15
27.0
27.45
27.4
29.85
30.0
27.75
28.0
28.45
28.4
29.85
29.7
29.15
29.0
28.95
29.0
28.5
28.5
27.85
27.9
28.15
28.0
29.25
29.3
29.5
29.4
29.5
29.8
28.5
28.3
south west monsoon period
Figure 4(a) : SST distribution during north east
monsoon from Jan 1998 to Mar 1998.
Figure 4(b) : SST distribution during south west
monsoon from June 1998 to Aug 1998.
6
Figure 4(c) : SST distribution during north east
monsoon from Nov 1998 to Jan 1999.
Figure 4(d) : SST distribution during south west
monsoon from June 1999 to Aug 1999.
7
Figure 4(f) : SST distribution during south west
monsoon from June 2000 to Aug 2000.
Figure 4(e) : SST distribution during north east
monsoon from Nov 1999 to Jan 2000.
8
Figure 4(g) : SST distribution during north east
monsoon from Nov 2000 to Jan 2001.
Figure 4(h) : SST distribution during south west
monsoon from June 2001 to Aug 2001.
9
Figure 4(i) : SST distribution during north east monsoon
from Nov 2001 to Jan 2002.
10
Figure 4(j) : SST distribution during south west
monsoon from June 2002 to Aug 2002.
Table 3 : Regression coefficient and RMSE of TMI SST and in-situ SST during monsoon period.
Year
3.
North East Monsoon
South West Monsoon
No. of samples
R²
RMSE
No. of samples
R²
RMSE
1998
132
0.8748
0.5078
80
0.6120
0.5403
1999
142
0.8896
0.5002
72
0.5481
0.5982
2000
115
0.8989
0.421
64
0.5890
0.5816
2001
123
0.9015
0.382
105
0.7456
0.4982
2002
117
0.9198
0.398
81
0.8051
0.4150
Detection of Oil Spills/Slicks from Radarsat SAR Data
Oil spills at sea pollute the marine environment to a varying degree during large oil tanker
accidents, especially when they occur close to the coast. On the other hand, oil release from ships in
transit, e.g. when cleaning tanks, may prove to be a much worse pollution when taking into account
how often such spills occur during regular ship operation. The SAR data from satellites can be
employed to help detect and monitor such spills. However, limitations exist and it is necessary to
have better knowledge of these limitations before SAR data can be utilized in an oil spill monitoring
system. The problem in detecting oil spills/slicks on the ocean surface using SAR images, lies in
distinguishing oil from other phenomena that dampen out the short waves and create dark patches.
Oil spills/slicks look-alikes may include natural film, threshold wind speed areas, wind sheltering by
land, rain cells, current shear zones, internal waves and upwelling (Hovland et al., 1994).
This oil spill study was carried out near Batu Pahat in the Straits of Malacca between
Sumatera and west coast of Johor. The satellite data used is the Radarsat SAR data (Figure 3)
acquired on 26 October 1997 which shows an oil spill from a collision between two ships, i.e. MT
Orapin Global and MV Evoikos in Singapore. The collision caused 25,000 tones of crude oil to be
spilled into the sea.
Figure 3 : Radarsat image of the study area.
11
The satellite data was geometrically corrected and the detection of oil spills/slicks was
enhanced by carrying out image processing that includes filtering and fuzzy classification.
3.1
Filtering
Speckle or noise that are contained within a SAR image is the main cause of difficulty in
interpreting the data. In this study various filters have been used to enhance oil spills/slicks areas.
The window size range from 3x3 to 11x11. Although the window size can be increased to 33x33 the
results are not satisfactory because of information loss. Qualitative and quantitative evaluation was
carried out on the filters. The qualitative evaluation is based on the preservation and enhancement of
edges, texture and retention of original image. For this purpose, visual interpretation was made on
every window size of each filter. The quantitative evaluation of the filters is primarily based on
variation coefficient values which are obtained by dividing the mean digital number by the standard
deviation. Preservation of the mean value of the original image is expected after filtering with
minimum value of standard deviation.
The filters used in this study are Lee, enhanced Lee, Frost, enhanced Frost, Kuan, Gamma
and Gaussian Sigma. Figure 4 shows the geometrically corrected image and subscene image used for
filtering. The filters used and the related statistical information are given in Table 4. The Kuan filter
has been found to be the most suitable filter for enhancing oil spill/slick areas.
(b)
(a)
Figure 4 : (a) Geometrically corrected image (b) Subscene image showing oil spill.
3.2 Fuzzy Classification
As mentioned earlier speckle or noise is the main problem in SAR image interpretation, even
a zone that is homogeneous on the ground has a granular aspect and a statistical distribution with a
large standard deviation. Therefore, the first step in the fuzzy classification technique is filtering
operation. The filter used for this operation is Kuan filter. Classification was made based on
algorithm developed by Bezdek and Trivedi (1986) which is a fuzzy extension of the least square
criterion.
n
c
J m (U ,V ) = ∑∑ (u ik ) m X k − Vi
k =1 i =1
where X = {x1, x2,…., xn} is the data set
V = {v1, v2,….., vc} is the set of prototype
uik = membership value of point X k in cluster Vi
m = the fuzziness of clusters controller.
12
Table 4 : Filters used with statistical information.
Type of Filter
Raw Image
Lee
Enhanced Lee
Frost
Enhanced
Frost
Kuan
Gamma
Gaussian
Sigma
Window Size
3x3
5x5
7x7
9x9
11 x 11
3x3
5x5
7x7
9x9
11 x 11
3x3
5x5
7x7
9x9
11 x 11
3x3
5x5
7x7
9x9
11 x 11
3x3
5x5
7x7
9x9
11 x 11
3x3
5x5
7x7
9x9
11 x 11
3x3
5x5
7x7
9x9
11 x 11
Mean DN
292.68
292.77
293.75
295.41
297.50
299.75
296.16
298.70
301.02
303.09
305.02
292.68
293.67
295.24
297.10
299.08
296.38
299.02
301.23
303.13
304.78
292.77
293.75
295.42
297.51
299.79
295.46
297.34
299.23
300.72
302.24
294.11
295.87
297.51
299.04
300.48
13
Std. Dev.
68.42
50.25
38.83
32.74
29.50
27.81
50.55
39.76
33.89
30.67
29.43
50.34
39.01
32.94
29.50
27.58
50.36
39.55
33.79
30.62
29.35
50.25
38.84
32.76
29.52
27.88
51.10
40.75
35.09
32.28
31.49
37.52
31.90
29.47
28.22
27.64
Variation Coeff.
4.28
5.83
7.56
9.02
10.09
10.78
5.86
7.51
8.88
9.88
10.36
5.81
7.53
8.96
10.07
10.85
5.89
7.56
8.92
9.90
10.38
5.83
7.56
9.02
10.08
10.75
5.78
7.30
8.53
9.32
9.60
7.84
9.28
10.10
10.60
10.87
In remote sensing images, a pixel corresponds on the ground to a cell of tens of square meters
which quite often contain mixture of surface-cover classes; in addition, boundaries among regions are
hardly ever sharp. Fuzzy set theory provides useful concepts and tools to cope with this sort of
problems. Here, a membership function is assigned to each pixel belonging to the set. The
memberships can be classified as full membership, no membership or partial membership (Barni et
al., 1995).
Once the fuzzy classification is obtained, a clustering map is produced by assigning each
pixel to the cluster which it mostly belongs. A final operation involves post-filtering of the map to
obtain more homogeneous regions. Figure 5 shows the result of fuzzy classification of the oil spill
carried out on the Radarsat image.
Sea water
(a)
Oil spill
(b)
Figure 5 : (a) Kuan filtered image (b) Result from fuzzy classification.
4.
Bathymetry Study from ERS SAR Data
In this study, the radar bathymetry model implemented at TNO was used to assess a number
of maritime features in the images potentially caused by topography of the sea bottom. This model is
based on the action balance equation, weak hydrodynamic interaction theory and Bragg scattering
(Vogelzang, 1989). The model which is suitable for shallow waters was run using estimated
bathymetric profiles based on available hydrographic charts, and wind and current velocities and
directions at the time of ERS imaging.
The contrast profiles produced by the model were compared to profiles extracted from the
ERS images. The results are shown in figures 6(a), 6(b) and 6(c).
14
Figure 6(a) : Sea bottom profile and marine
information in the study area.
Figure 6(b) : Radar contrast derived from the
model (wind and current velocities are 3.0 m/s and
1.0 m/s respectively during data acquisition).
Figure 6(c) : Profile from ERS data.
5.
Ocean Wavelength and Direction from ERS SAR Data
ERS SAR data has been demonstrated to provide ocean wave information in terms of
wavelengths and directions. Image processing was carried out by using single SAR image frames
comprising of 512 x 512 image pixels. Since each pixel in ERS SAR data represents a 12.5 m x 12.5
m area, the entire image frame corresponds to a 6.4 km x 6.4 km patch on the ocean surface. The
frame size provides a sufficiently large area that at least 10 cycles of very long surface waves, up to
640 m in length, can be included in a single image frame. They are also small enough that the ocean
can be reasonably assumed homogeneous within a frame (Monaldo, 1991).
The ocean wavelength and direction were derived from ocean wave spectra by using a 2dimensional Fast Fourier Transform, with smoothing in the spectral domain and spatially averaging
for noise reduction, and a correction for the stationary instrumental response function. The results
are shown in figure 7. This process was repeated for other frames in the image.
15
200m
100m
50m
Figure 7 : Wave spectra derived from single frame on ERS data. Circle and arrow represent ocean
wavelength and direction respectively.
6.
Rain Cells
Microwave data is also useful to determine rain cells. When heavy rain falls on the ocean
surface, it creates turbulence which may dampen out the waves. The result is an area of low radar
return where the heavy rain falls and an increased return surrounding this area. The areas have
characteristic forms due to the strong wind squalls that carry the cold descending air away from the
cell center (Hovland et al., 1994). This phenomenon is illustrated in Figures 8 and 9.
Figure 8 : A sketch of a rain cell (Source: Hovland et al., 1994).
16
Pu lau
K ap as
Figure 9 : Image of Rain Cell in Kuala Terengganu area.
7.
Water Fronts
Water fronts appear where two water masses meet, that is, sea water with river water, turbid
water with less turbid water and water with different temperatures. Studies on water fronts can be
related to fishing, shipping and tourism industry. Figure 10 illustrates the water front in the Kuala
Terengganu study area.
Figure 10 : Water fronts in Kuala Terengganu area.
17
8.
Internal Waves
An internal wave is a propagating oscillation within water that has a vertical density profile
(lighter water lying over heavier water). The differences in density are caused by differences in
temperature and/or salinity. Internal waves do not give rise to significant water surface elevations, but
they do produce surface currents (Figure 11). In areas where internal waves are present, nutrients
needed for fish breeding are usually found.
Figure 11 : Internal wave on ERS SAR image in Terengganu study area.
9.
Conclusions
The SST study from TRMM TMI data shows that SST values can be derived accurately from
this data. Comparisons with in-situ measurements indicate an accuracy of about ± 0.3oC. The R2 value
between in-situ and TMI SST is about 0.92. The SST during the north east monsoon period was
slightly lower than the SST during the south west monsoon.
The study of oil spills/slicks using filtering and fuzzy clustering techniques of the Radarsat
SAR data indicate that these areas can be detected and delineated clearly. The suitable filters for
enhancing oil spill/slick areas are Frost, Kuan and Gaussian Sigma using windows of size 9 x 9 or 11
x 11. Fuzzy clustering also improves the delineation of oil spill/slick areas. One should be careful so
as not to misinterprete calm water areas as oil spill/slick areas since they look quite similar.
For the bathymetry study, the radar contrast derived from the bathymetric signature model
and the ERS SAR data was quite consistent with the shape of the sea bottom profile obtained from
hydrographic charts.
For the ocean wave spectra analysis, the wavelength and direction of the ocean wave
determined from the SAR images were consistent with the expected results.
The dominant
wavelength is about 200 m.
Acknowledgements
The authors would like to thank Prof. T. Yanagi, Mr. Tsutomu Tokeshi of Kyushu University,
Japan, and Mohd Nazri Reba of UTM for their contributions in the TRMM TMI study. We also thank
Anuar Salleh and Khor Soong Wei for their contributions in the Radarsat and ERS studies. The
authors also acknowledge their thanks to the Meteorological Department of Malaysia for providing insitu SST data.
18
References
Barni, M., Betti, M., and Mecocci, A. (1995). A Fuzzy Approach to Oil Spill Detection on SAR
Images. IEEE Transactions 0-7803-2567-2/95.
Bezdek, J.C. & Trivedi, M.M. (1986). Low-Level Segmentation of Aerial Images with Fuzzy
Clustering. International Electronic and Electrical Engineering of Transactions on
Systems, Man & Cybernetics, 16(4), 589-598.
Hovland, H.A., Johannessen, J.A. and Digranes, G. (1994). Slick Detection in SAR Images. IEEE
Transactions 0-7803-1497-2/94.
Kummerow, C. and Barnes, W. (1998). The Tropical Rainfall Measuring Mission (TRMM) sensor
package,
Available
in
http://rain.atmos.colostate.edu/papers/kummerow/
kummerow1998.pdf
Monaldo, F.M. (1991). The consequences of sampling variability on the estimation of wave number
and propagation direction from spaceborne SAR image spectra, IEEE Transactions
on Geoscience and Remote Sensing, Vol.29, No.1, pp. 113-119.
NASA Facts (1997). Tropical Rainfall Measuring Mission’s (TRMM) Microwave Imager, Available
in http://trmm.gsfc.nasa.gov
Vogelzang, J. (1989). The mapping of bottom topography with imaging radar : A comparison of the
hydrodynamic modulation in some existing models, Int. J. Remote Sensing, Vol.10,
No.9, pp. 1503-1518.
19