Scattering Model-Based Oil-Slick-Related Parameters Estimation From Radar Remote Sensing: Feasibility and Simulation Results

In this study, the potential of electromagnetic scattering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial intelligence (AI)-based approach. The backscattering coefficient of a slick-covered sea surface is predicted using the advanced integral equatio...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 62; S. 1 - 12
Hauptverfasser: Meng, Tingyu, Nunziata, Ferdinando, Yang, Xiaofeng, Buono, Andrea, Chen, Kun-Shan, Migliaccio, Maurizio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial intelligence
Artificial neural network (ANN)
Damping
Damping ratio
Deep water
DeepWater Horizon (DWH)
Dielectric constant
Electromagnetic scattering
Emulsions
Integral equations
Mathematical models
Neural networks
Oil slicks
oil spill
Oil spills
Oils
Parameter estimation
parameter inversion
Parameters
Predictive models
Radar
Radar imaging
Remote sensing
Rough surfaces
SAR (radar)
Scattering
scattering model
Sea surface
Seawater
Surface roughness
Surface waves
Synthetic aperture radar
synthetic aperture radar (SAR)
Thickness
ISSN:0196-2892, 1558-0644
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Abstract In this study, the potential of electromagnetic scattering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial intelligence (AI)-based approach. The backscattering coefficient of a slick-covered sea surface is predicted using the advanced integral equation model augmented with the model of local balance (MLB), an effective dielectric constant model, and a composite medium model to include the effect of an oil slick. Damping ratios (DRs), predicted for different oil parameters (namely, the oil thickness and seawater volume fraction), are used to train and test a four-layer neural network. Once successfully tested, the neural network is applied to an uninhabited aerial vehicle synthetic aperture radar (UAVSAR) image collected during the DeepWater Horizon (DWH) oil spill accident to retrieve the oil slick thickness and volume fraction of seawater in the oil layer. The inversion results show that the thicker (i.e., 2-4 mm) emulsions are located in the south and west of the slick and they are surrounded by thinner (i.e., < 1 mm) oil films. In addition, the seawater volume fraction in the oil slick is found to be about 20%-30%. Results are contrasted with optical data and previous studies of the same accidental oil spill, showing qualitatively good agreement.
AbstractList In this study, the potential of electromagnetic scattering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial intelligence (AI)-based approach. The backscattering coefficient of a slick-covered sea surface is predicted using the advanced integral equation model augmented with the model of local balance (MLB), an effective dielectric constant model, and a composite medium model to include the effect of an oil slick. Damping ratios (DRs), predicted for different oil parameters (namely, the oil thickness and seawater volume fraction), are used to train and test a four-layer neural network. Once successfully tested, the neural network is applied to an uninhabited aerial vehicle synthetic aperture radar (UAVSAR) image collected during the DeepWater Horizon (DWH) oil spill accident to retrieve the oil slick thickness and volume fraction of seawater in the oil layer. The inversion results show that the thicker (i.e., 2–4 mm) emulsions are located in the south and west of the slick and they are surrounded by thinner (i.e.,
In this study, the potential of electromagnetic scattering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial intelligence (AI)-based approach. The backscattering coefficient of a slick-covered sea surface is predicted using the advanced integral equation model augmented with the model of local balance (MLB), an effective dielectric constant model, and a composite medium model to include the effect of an oil slick. Damping ratios (DRs), predicted for different oil parameters (namely, the oil thickness and seawater volume fraction), are used to train and test a four-layer neural network. Once successfully tested, the neural network is applied to an uninhabited aerial vehicle synthetic aperture radar (UAVSAR) image collected during the DeepWater Horizon (DWH) oil spill accident to retrieve the oil slick thickness and volume fraction of seawater in the oil layer. The inversion results show that the thicker (i.e., 2-4 mm) emulsions are located in the south and west of the slick and they are surrounded by thinner (i.e., < 1 mm) oil films. In addition, the seawater volume fraction in the oil slick is found to be about 20%-30%. Results are contrasted with optical data and previous studies of the same accidental oil spill, showing qualitatively good agreement.
Author Meng, Tingyu
Yang, Xiaofeng
Chen, Kun-Shan
Nunziata, Ferdinando
Buono, Andrea
Migliaccio, Maurizio
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10.1029/JC094iC05p06251
10.1109/TGRS.2021.3137654
10.1016/S0034-4257(98)00034-0
10.1080/01431161.2014.905730
10.1016/j.marpolbul.2019.06.018
10.3390/s18020641
10.1109/TGRS.2013.2287916
10.1049/PBEW047E
10.1109/JSTARS.2020.3003686
10.1016/j.marpolbul.2016.06.066
10.1109/MGRS.2022.3145854
10.1016/j.marpolbul.2016.06.027
10.1002/2015jc011062
10.3390/rs13061183
10.1109/TGRS.2019.2942280
10.1080/01431160010008564
10.1109/TGRS.2018.2872080
10.1109/JSTARS.2019.2961563
10.5670/oceanog.2013.38
10.1109/TGRS.2017.2661325
10.1109/36.134085
10.1109/TGRS.2013.2259498
10.3390/rs10020319
10.1002/9781394180042
10.1109/JSTARS.2023.3285145
10.1109/TGRS.2014.2351417
10.1007/3-540-33271-5
10.3998/0472119356
10.1029/2012gl052304
10.1016/j.marpolbul.2023.114952
10.1016/S1353-2561(98)00023-1
10.1080/014311698213849
10.1109/IGARSS52108.2023.10282557
10.1109/JSTARS.2013.2244061
10.3390/s18010091
10.3390/rs11232882
10.1016/j.rse.2017.09.002
10.3390/rs12111744
10.1109/JSTARS.2016.2605151
10.1109/TGRS.2021.3073369
10.3390/rs11020160
10.1029/97JC00467
10.1029/97JC01915
10.3390/rs10071012
10.1109/TGRS.2002.807587
10.1109/TGRS.2012.2185804
10.1109/TGRS.2019.2905558
10.1063/1.869240
10.3390/w12113223
10.3390/rs15164112
10.1016/j.rse.2019.111421
10.1016/j.rse.2020.111716
10.1016/j.rse.2016.05.014
10.1016/j.ecss.2010.05.015
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References ref13
ref57
ref12
ref56
ref15
ref14
ref53
ref52
ref11
ref55
ref10
ref54
ref17
ref16
ref19
ref18
ref51
ref50
ref46
ref45
ref48
ref47
ref42
ref41
Fingas (ref6)
ref44
ref43
ref49
ref8
ref7
ref4
ref3
ref5
ref40
ref35
ref34
ref37
ref36
ref31
ref30
ref33
ref32
Secretariat (ref9) 2017
ref2
ref1
ref39
ref38
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
References_xml – ident: ref19
  doi: 10.1016/j.rse.2018.05.001
– ident: ref44
  doi: 10.1029/JC094iC05p06251
– ident: ref12
  doi: 10.1109/TGRS.2021.3137654
– ident: ref14
  doi: 10.1016/S0034-4257(98)00034-0
– ident: ref55
  doi: 10.1080/01431161.2014.905730
– ident: ref46
  doi: 10.1016/j.marpolbul.2019.06.018
– ident: ref7
  doi: 10.3390/s18020641
– ident: ref17
  doi: 10.1109/TGRS.2013.2287916
– ident: ref47
  doi: 10.1049/PBEW047E
– ident: ref11
  doi: 10.1109/JSTARS.2020.3003686
– ident: ref10
  doi: 10.1016/j.marpolbul.2016.06.066
– ident: ref27
  doi: 10.1109/MGRS.2022.3145854
– ident: ref52
  doi: 10.1016/j.marpolbul.2016.06.027
– ident: ref25
  doi: 10.1002/2015jc011062
– ident: ref42
  doi: 10.3390/rs13061183
– ident: ref29
  doi: 10.1109/TGRS.2019.2942280
– ident: ref21
  doi: 10.1080/01431160010008564
– ident: ref3
  doi: 10.1109/TGRS.2018.2872080
– ident: ref56
  doi: 10.1109/JSTARS.2019.2961563
– ident: ref57
  doi: 10.5670/oceanog.2013.38
– ident: ref54
  doi: 10.1109/TGRS.2017.2661325
– start-page: 617
  volume-title: Proc. 35th Arctic Mar. Oil Spill Program Tech. Seminar
  ident: ref6
  article-title: How to measure slick thickness (or not)
– ident: ref38
  doi: 10.1109/36.134085
– ident: ref50
  doi: 10.1109/TGRS.2013.2259498
– ident: ref2
  doi: 10.3390/rs10020319
– ident: ref40
  doi: 10.1002/9781394180042
– ident: ref23
  doi: 10.1109/JSTARS.2023.3285145
– ident: ref18
  doi: 10.1109/TGRS.2014.2351417
– ident: ref4
  doi: 10.1007/3-540-33271-5
– ident: ref48
  doi: 10.3998/0472119356
– year: 2017
  ident: ref9
  publication-title: Bonn Agreement Aerial Operations Handbook
– ident: ref15
  doi: 10.1029/2012gl052304
– ident: ref24
  doi: 10.1016/j.marpolbul.2023.114952
– ident: ref43
  doi: 10.1016/S1353-2561(98)00023-1
– ident: ref20
  doi: 10.1080/014311698213849
– ident: ref33
  doi: 10.1109/IGARSS52108.2023.10282557
– ident: ref1
  doi: 10.1109/JSTARS.2013.2244061
– ident: ref5
  doi: 10.3390/s18010091
– ident: ref22
  doi: 10.3390/rs11232882
– ident: ref8
  doi: 10.1016/j.rse.2017.09.002
– ident: ref30
  doi: 10.3390/rs12111744
– ident: ref49
  doi: 10.1109/JSTARS.2016.2605151
– ident: ref34
  doi: 10.1109/TGRS.2021.3073369
– ident: ref36
  doi: 10.3390/rs11020160
– ident: ref39
  doi: 10.1029/97JC00467
– ident: ref13
  doi: 10.1029/97JC01915
– ident: ref26
  doi: 10.3390/rs10071012
– ident: ref37
  doi: 10.1109/TGRS.2002.807587
– ident: ref16
  doi: 10.1109/TGRS.2012.2185804
– ident: ref35
  doi: 10.1109/TGRS.2019.2905558
– ident: ref45
  doi: 10.1063/1.869240
– ident: ref28
  doi: 10.3390/w12113223
– ident: ref31
  doi: 10.3390/rs15164112
– ident: ref41
  doi: 10.1016/j.rse.2019.111421
– ident: ref32
  doi: 10.1016/j.rse.2020.111716
– ident: ref53
  doi: 10.1016/j.rse.2016.05.014
– ident: ref51
  doi: 10.1016/j.ecss.2010.05.015
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Snippet In this study, the potential of electromagnetic scattering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial...
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SubjectTerms Artificial intelligence
Artificial neural network (ANN)
Damping
Damping ratio
Deep water
DeepWater Horizon (DWH)
Dielectric constant
Electromagnetic scattering
Emulsions
Integral equations
Mathematical models
Neural networks
Oil slicks
oil spill
Oil spills
Oils
Parameter estimation
parameter inversion
Parameters
Predictive models
Radar
Radar imaging
Remote sensing
Rough surfaces
SAR (radar)
Scattering
scattering model
Sea surface
Seawater
Surface roughness
Surface waves
Synthetic aperture radar
synthetic aperture radar (SAR)
Thickness
Title Scattering Model-Based Oil-Slick-Related Parameters Estimation From Radar Remote Sensing: Feasibility and Simulation Results
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