Ocean Remote Sensing Data Predicts Trajectory of Oil Spill

An Analytical Model for SAR Polarimetric Scattering Matrix

Bo Wang

, Bertrand Chapron

and René Garello

Department of Computing and Communication, China University of Petroleum (Huadong), Qingdao, China

LOS, IFREMER, Plouzané, France

Department ITI, Telecom Bretagne, Brest, France

Keywords: Ocean Remote Sensing, Polarimetric SAR, Oil Trajectory.

Abstract: The ocean surface is part of the upper ocean which directly interacts with the overlying atmosphere and sea

ice. Once oil spill happened due to an accident such as the oil rig pipe leaking and exploring, it would be

unimaginable disaster to the oceanic environment, especially in the coastal area. If we can predict the

direction along which the oil films floats over the marginal sea surface, the damage would be controlled

within a pre-knowledge level. Under these knowledge, we analysed the polarimetric SAR (Synthetic

Aperture Radar) data with an analytical model to separate backscattered contributions by different sea

surface scatterers. Furthermore, it provides a possible prediction of the local wind direction by using the

separated backscattered signal. With this direction, it is ready to predict the direction of oil film’s floating.

1 INTRODUCTION

The ocean environment plays an important role in

the global climate change. Ocean remote sensing

offers satellite observations as a data collection

which could be assimilated into ocean models. Such

assimilations as SAR acquisition used in a flood

model (García-Pintado et al., 2013), the ozone

analyses based on Envisat data (Geer et al., 2006;

Lahoz et al., 2007), the cloud and precipitation

observations effort in a numerical weather prediction

model (Ohring et al., 2005), have all achived

benefits from the satellite data. The ocean surface is

part of the upper ocean which directly interacts with

the overlying atmosphere and sea ice. Surface films

isolate the air from the ocean surface, they also

damp the surface wave vibration (Gade et al., 2006),

leaving a dark area or dark textures in the SAR

imagery. Based on the above understandings, we

analysed multi-polarization data by SAR onboard of

the Radarsat-2 satellite, with the first result of wind

wave direction estimated by a Bragg wave

component. This estimation of local wind direction

can help to predict the trajectory of the surrounding

oil films, by applying this parameter into an ocean

wave prediction model, such as the numerical model

Wavewatch III (Feng et al., 2006).

2 SAR POLARIMETRY

SAR measurements or derivatives have recently

been assimilated into models, including hydraulic

(Giustarini et al., 2011), snowpack (Phan et al.,

2013). The efforts to ocean wave modelling trace

back to the 1990s when a large number of researches

focused on the wave retrieval from the SAR imagery

(De las Heras et al., 1995; Olsen et al., 1995;

Hasselmann et al., 1997; Breivik et al., 1998 ),

which have seldom updates recently. Polarimetric

SAR refocused the researches on ocean wave

retrieval, however, it had rarely been considered that

the sea surface is composed by different scattering

mechanisms. A theoretical model concern different

polarization properties in the radar backscattering

coefficient expressed the NRCS (Normalized Radar

Cross-Section) as the sum of polarization dependent

Bragg and polarization independent Scalar

contributions (Quilfen et al., 1999). Following this

theoretical model, we proposed an analytical model

for polarimetric SAR scattering matrix. In our

model, the Bragg contribution was expressed in

scattering matrix by the tilt Bragg model, while the

Scalar contribution concerned the Rayleigh

scattering from foams introduced by sea surface

wave breaking, the specular reflection by the steep

wave, and double-bounced metallic sea surface

822

wang B., Chapron B. and Garello R..

Ocean Remote Sensing Data Predicts Trajectory of Oil Spill - An Analytical Model for SAR Polarimetric Scattering Matrix.

DOI: 10.5220/0005125308220827

In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (MSCCEC-2014), pages

822-827

ISBN: 978-989-758-038-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

target. The interaction between i.i.d. (independent

and identically distributed) scatterers has been

considered within each pixel area using the random

walk model, with the assumption that the Bragg

contribution is the vector sum of the random walk by

all the Bragg scatterings. This analytical model was

iteratively resolved by a former estimate (WANG et

al., 2011). In this iteration algorithm, a Bragg related

polarization ratio, β, was determined iteratively to

separate the Bragg contribution from the non-Bragg

contribution. This model is indeed the theoretical

model of Quilfen et al. when the cross product of

Bragg and Scalar contributions is zero. But in real

case, the latter could not be ignored. Thus the sea

surface depolarization and polarization can be

separated better by complex scattering matrix than

by NRCS. Wind seas were also retained from

separated scattering contributions, indicating a finer

resolution than traditional retrieval method.

3 WIND DIRECTION

3.1 Polarization Wind Wave

Parameters

Since 1990s, global measurements of the two-

dimensional wave spectrum by space-borne SAR

sensors on-board of satellites such as ERS-1/2,

Radarsat-1/2 has been investigated. Missions of

ocean wave investigated mapping of ocean wave

spectrum from the SAR image spectrum

(Hasselmann and Hasselmann, 1991), the unification

of the directional spectrum (Elfouhaily et al., 1997),

and the effect of the longer wave and swell (Chen et

al., 2000; Plant, 2003; Ardhuin et al., 2007).

Furthermore, the bound wave / free wave model

(Plant et al., 2010) consists with sea surface slope

spectrum measured in 1950s (Cox, 1958). However,

all the algorithms were limited within the range of

wavelength longer than a cut-off wavelength, till the

full polarization radar imagery being available. At a

specially selected orientation angle, the modulation

of the wave slope on radar measured intensity could

be enhanced, better than any of that from the

standard linear polarization HH, VV, HV or VH

(Boerner et al., 1992; Schuler et al., 1995). It is

possible to find this special orientation angle to

solve optimal polarization problems. This principle

is called polarization modulation transfer function

(MTF) (Schuler et al., 1995). A combined

polarization MTF with an eigenvector α angle

modulation (Cloude and Pottier, 1996) could also

enhance the wave slope in the azimuth direction

(Schuler et al., 2004). However, Schuler's method

was questioned for applying to the sea surface by not

considering the non-linear velocity bunching effect

(Alpers, 1983).

The α parameter of the entropy-anisotropy-α

decomposition theorem (Cloude and Pottier, 1996) is

roll-invariant in the azimuth direction. It has high

sensitivity to wave-induced change of local

incidence angle. Decomposition theorems consider

polarimetric radar backscattered signal matrices as

the summation of different scattering mechanisms

coherently or incoherently. According to Cloude-

Pottier's theorem, the mean scattering matrix has an

eigenvector

k

√

e







e











(1)

in which λ, α is the mean target power (span) and

roll-invariant mean scattering respectively, the rest

three parameters β, δ, γ are orientation angle related,

and rotation variant, could be used to define the

target polarization orientation angle. The five

parameters are connected with the radar

measurements by









∙



∗

∙

∗

(2)

in which 



is the covariance matrix, and





































(3)

is the '3-D Pauli feature vector' (Lee and Pottier,

2009), where S



,P,Q∈H,V is the components

of Sinclair scattering matrix.

From Eq. (1) and Eq. (3), the approximation of

the α parameter from polarimetric SAR

measurements should be as

tanα

















(4)

On the other hand, the relationship between α

angle and incidence angle θ



could be find as Eq.

(5),

tanα







(5)

using the SMP model and considering only

incidence angle, for the water dielectric constant

ϵ→∞.

OceanRemoteSensingDataPredictsTrajectoryofOilSpill-AnAnalyticalModelforSARPolarimetricScatteringMatrix

823

So far, the range slope can be derived from the

local incidence angle corresponding to α angle

subtracted by the incidence angle according to the

radar geometry as







(6)

3.2 Wind Wave Direction Retrieval

Based on the above analysis, the local incidence

angle is reasonable to be iteratively estimated by the

polarimetric SAR measurements, hence possible to

assign each scattering contribution due to different

polarization characteristics (Wang, 2013). The

Bragg scattering of wind seas is considered to be

sensitive to the radar polarization, and the other

scattering mechanisms introduced by steep waves or

breakers are insensitive to the polarization, being

scalar contributions. Separated scattering

contributions correspond to different range of the

ocean wave spectra. As the primary results, we

showed the capability of wind wave direction

retrieval from the Bragg scattering component.

3.3 First Results

In-situ platforms collocated with Radarsat-2 satellite

data set are offered by the Observing System

Monitoring Center (OSMC) programme funded by

NOAA/OCO, providing both the near real and

historical status of meteorological and

oceanographic data collection systems. These

platforms include both buoys and voluntary ships,

returning one record per hour, generally. Records in

the nearest distance were interpolated in time for

satellite acquisitions in August and September.

Other records following up are supplied by

meteorological moored buoys located exactly inside

of the satellite image scene. The result does not

always support our assumption that a special part of

the wave spectra could be assigned for the Bragg

scattering and scalar contribution separately. To the

contrary, scalar contribution retains a wind wave

direction much more consist with the in-situ data

than the Bragg contribution. We are not ready to

give an explanation in this paper, however, the

reginal currents should be considered more carefully

since all the contradiction data located in the

Mediterranean Sea, where the swell is not so

significant to domain in the scalar component as the

polarization insensitive contribution, both in summer

and in winter. Nevertheless, the results from the data

located in the North Sea and North West Atlantic

Ocean indicates consisted wind derived short wave

direction on the Bragg scattering contributions.

3.3.1 Case Study

We showed in Fig. 1 the location of the SAR

imagery acquired on 2011 December 5

, to the south

west of Scotland and northwest of Bretagne, with the

satellite LOS of 45.11 deg. in the center of image.

The local wind speed is 10.29m/s, blowing to the

direction of 290 deg. departing from the North, as

shown in Table 1.

Figure 1: The location of the SAR imagery 20111205 (red

square) and the local wind direction (blue arrow).

Table 1: The in-situ measurements to be counterpart with

the SAR imagery of 20111205.

Wind speed

(m/s)

Wind direction

(degree)

Wave height

(m)

10.29 290 4.5

3.3.2 The Direction

The cross spectrum of Bragg component in VV

polarization indicated a peak direction consist with

the in-situ 290 deg. from the North, shown in Fig. 2.

Figure 2: Cross spectrum of Bragg component in VV

polarization. The peak direction (thick line in black)

retains the local wind direction of 290 deg. from the

North.

SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand

Applications

824

It is clear from the comparison of Figure 2 to 3

that the separation of scattering mechanisms such as

Bragg contribution helped to tell the local wind

direction, which was impossible by using the single

polarization data. The direct SAR measurement is

definitely a combination of different scattering

contributions. This study supported the theoretical

model of NRCS concerning the polarization

(Quilfen et al., 1999).

Figure 3: Cross spectrum of SAR imagery in VV

polarization. There is hard to find the local wind direction.

4 OIL FILMS

Surface films could not only be a natural sea slick

generated by plankton, but also the floating oil

introduced by an oil spill. Oil spill is easily to

accompany with an accident such as the oil rig pipe

leaking and exploring. Oil spill ruins the local sea,

depresses the air/sea interaction, and is even worse

to the environment than the natural plankton films.

Figure 4: Bragg scattering component in HH polarization.

Figure 5: Direct SAR measurement in HH polarization.

In Fig. 4 and 5 is the Bragg scattering component

and the direct SAR measurement, both in HH polarization.

The improvement from original measurement to the

separated single scattering mechanism could be indicated

by the expansion of the range of data value from [-32dB,

22dB] to [-40dB, -22dB], although the image quality has

not been much improved. On the other hand, the slick

texture domains the minimum values, i.e. around -40dB

for Bragg component. It helped the accuracy of local wind

direction estimated from the cross spectrum of Bragg

contributions.

5 CONCLUSIONS

The polarimetric SAR data have been analysed in

this paper, introducing an estimation of short wave

directions, for the wind seas with a wavelength less

than 50 meters. This kind of short wave is

considered to be the wind wave, the propagation

direction of which accords to the local wind. The

local wind direction is critical to predict the

trajectory of oil spills. However, the oil films float

over the sea surface not only along the local wind,

but also tracing the longer wave and current. This is

the same problem that encountered by the

contradiction data of Mediterranean Sea in section 3.

For example, the predictions for significant wave

height by the numerical model such as NCEP

Wavewatch III have been identified to have

substantial differences when include and exclude

ocean surface currents.

ACKNOWLEDGEMENTS

This work was partially supported by a grant from

the European Union, the Brittany Region and the

OceanRemoteSensingDataPredictsTrajectoryofOilSpill-AnAnalyticalModelforSARPolarimetricScatteringMatrix

825

Brest Metropole Oceane to the VIGISAT project, in

the framework of a FEDER grant Presage # 32635,

and by “the Fundamental Research Funds for the

Central Universities” No. 14CX02135A. The

Radarsat-2 data was supported by CLS under the

contracts #08 GET 13M and 09 GET 11M.

REFERENCES

García-Pintado, J., Neal, J. C., Mason, D. C., Dance, S. L.,

Bates, P. D., Scheduling satellite-based SAR

acquisition for sequential assimilation of water level

observations into flood modelling, Journal of

Hydrology 495 (2013) 252-266.

Geer, A. J., Lahoz, W. A., Bekki, S., Bormann, N., Errera,

Q., Eskes, H. J., Fonteyn, D., Jackson, D. R., Juckes,

M. N., Massart, S., Peuch, V. H., Rharmili, S., Segers,

A., The ASSET intercomparison of ozone analyses:

method and first results, Atmos. Chem. Phys., 6, 5445-

5474, 2006.

Lahoz, W., Geer, A., Bekki, S., Bormann, N., Errera, Q.,

Eskes, H. J., Fonteyn, D., Jackson, D. R., Juckes, M.

N., Massart, S., Peuch, V. H., Rharmili, S., Segers, A.,

Evaluation of MIPAS and SCIAMACHY data using

data assimilation, In Proceedings of the Third

Workshop on the Atmospheric Chemistry Validation of

Envisat (ACVE-3), 4-7 December 2006, ESA SP-642,

February 2007.

Ohring, G., Weng, F., Errico, R., Mahfouf, J. Turk, J.,

Bauer, P., Campana, K, Ferrier, B., Assimilation of

satellite cloud and precipitation observations in NWP

models: Report of a workshop, In Proceedings of

ITSC-14, Beijing, May 25-31, 2005.

Gade, M., Hühnerfuss, H., Korenowski, G., 2006, Marine

Surface Films, Springer-Verlag Berline Heidelberg,

The Netherlands.

Feng, H., Vandemark, D., Quilfen, Y., Chapron, B.,

Beckley, B., “Assessment of wind-forcing impact on a

global wind-wave model using the TOPEX altimeter”,

Ocean Engineering, Vol. 33, Issues 11-12, August

2006, P. 1431-1461

Giustarini, L., Matgen, P., Hostache, R., Montanari, M.,

Plaza, D., Pauwels, V. R. N., De Lannoy, G. J. M., De

Keyser, R., Pfister, L., Hoffmann, L., Savenije, H. H.

G., Assimilating SAR-derived water level data into a

hydraulic model: a case study, Hydrol. Earth Syst. Sci.

Discuss., 8, 2103-2144, 2011)

Phan, X. V., Ferro-Famil, L., Gay, M., Durand, Y.,

Dumont, M., Morin, S., Allain, S., D’Urso, G., Girard,

A., 3D-VAR multilayer assimilation of X-band SAR

data into a detailed snowpack model, The Cryosphere

Discuss., 7, 4881-4912, 2013

De las Heras, M. M., Burgers, G., Janssen, P. A. E. M.,

Wave data assimilation in the WAM wave model,

Journal of Marine Systems 6 (1995) 77-85

Olsen, R. B., Dunlap, E. M., De Margerie, S., Wilson, L.

J., Assimilation of SAR image spectra in a spectral

ocean wave model, In Proceeding of Geoscience and

Remote Sensing Symposium, 1995

Hasselmann, S., Lionello, P., Hasselmann, K., An optimal

interpolation scheme for the assimilation of spectral

wave data, Journal of Geophysical Research, Vol. 102,

No. C7, Pages 15823-15836, 1997

Breivik, L. A., Reistad, M., Schyberg, H., Sunde, J.,

Krogstad, H. E., Johnsen, H., Assimilation of ERS

SAR wave spectra in an operational wave model,

Journal of Geophysical Research, Vol. 103, No. C4,

Pages 7887-7900, 1998

Quilfen, Y., Chapron, B., Bentamy, A., Gourrion, J.,

Elfouhaily, T., Vandemark, D., Global ERS 1 and 2

and NSCAT observations: Upwind / crosswind and

upwind / downwind measurements, J. Geophys. Res.,

vol. 104, C5, no. 11,459-11,469, May 15, 1999.

Wang, B., Chapron, B., Mouche, A., Mercier, G., Garello,

R., He, M., Depolarization model - a polarimetry

approach, presented at the CFOSAT meeting, 2011.

Hasselmann, K., Hasselmann, S., On the nonlinear

mapping of an ocean wave spectrum into a synthetic

aperture radar image spectrum and its inversion, J.

Geophysical Research, vol. VOL. 96, No. C6, no.

P.10713-10729, June 15, 1991.

Elfouhaily, T., Chapron, B., Katsaros, K., Vandemark, D.,

A unified directional spectrum for long and short

wind-driven waves, J. Geophys. Res., vol. Vol. 1O2

(C7), no. P.15781-15796, July 15, 1997.

Chen, G., Belcher, S. E., Effects of long waves on wind-

generated waves, Journal of Physical Oceanography,

Vol.30, September 2000.

Plant, W. J., Bound waves and sea-surface slope, In

Proceedings of OCEANS2003.San Diego, CA, USA,

vol. Vol.4, no. 1825-1828, 22-26 Sept. 2003.

Ardhuin, F., Herbers, T. H. C., Vledder, G. P., Watts, K.

P., Jensen, R., Graber, H. C., Swell and slanting-fetch

effects on wind wave growth, Journal of Physical

Oceanography, Vol.37, no. pp.908-931, April 2007.

Plant, W., Keller, W. C., Hayes, K., Chatham, G.,

Normalized radar cross section of the sea for

backscatter: 1. mean levels, J. Geophys. Res., vol. 115,

C09032, 2010.

Cox, C. Measurements of slopes of high frequency waves,

J. Mar. Res., vol. 16, no. 199-225, 1958.

Boerner, W. M., Liu, C. L., Zhang, X., Comparison of

optimization procedures for 2x2 sinclair, 2x2 graves,

3x3 covariance and 4x4 mueller (symmetric) matrices

in coherent radar polarimetry and its application to

target versus background discrimination in microwave

remote sensing and imaging, International Journal of

Advances in Remote Sensing (IJARS), Vol. 2, No.1-1,

no. pp. 55-82, January 1993.

Schuler, D. L., Lee, J. S., A microwave technique to

improve the measurement of directional ocean wave

spectra, Int. J. Remote Sensing, Vol. 16, No. 2, no.

199-215, 1995.

Cloude, S. R., Pottier, E., A review of target

decomposition theorems in radar polarimetry, IEEE

Transacctions on Geoscience and Remote Sensing,

VOL.34, NO. 2, MARCH 1996.

SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand

Applications

826

Schuler, D. L., Lee, J. S., Kasilingam, D., Pottier, E.,

Measurement of ocean surface slopes and wave

spectra using polarimetric sar image data, Remote

Sensing of Environment, vol. 91, no. 198 - 211, 2004.

Alpers, W., Monte carlo simulations for studying the

relationship between ocean wave and synthetic

aperture radar image spectra, J. Geophys. Res., vol.

Vol. 88, NO. C3, no. p.1745-1759, February 28, 1983.

Wang, B., Synthetic aperture radar multi-polarization

ocean surface characteristics and ship detection,

Ph.D. dissertation, Télécom Bretagne, Brest,

December 2013.

OceanRemoteSensingDataPredictsTrajectoryofOilSpill-AnAnalyticalModelforSARPolarimetricScatteringMatrix

827