Experimental research on oil film thickness and its microwave scattering during emulsification

Jie GUO; Chenqi XU; Genwang LIU; Xi ZHANG; Junmin MENG; Guangbo REN

doi:10.1007/s00343-021-1183-x

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Experimental research on oil film thickness and its microwave scattering during emulsification

Physics | Updated：2023-05-20

- Experimental research on oil film thickness and its microwave scattering during emulsification
- Journal of Oceanology and Limnology Vol. 40, Issue 4, Pages: 1361-1376(2022)
- Affiliations：
  
  1.Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences(CAS), CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai 264003, China
  2.Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, CAS, Yantai 264003, China
  3.Center for Ocean Mega-Science, CAS, Qingdao 266071, China
  4.University of Chinese Academy of Sciences, Beijing 100049, China
  5.First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
- Author bio：
  
  Jie GUO, E-mail: jguo@yic.ac.cn
- Funds：
- DOI：10.1007/s00343-021-1183-x
  CLC：
- Received：17 June 2021，
  
  Accepted：31 August 2021，
  
  Online First：06 September 2021，
  
  Published：2022-07
- Accepted：
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Jie GUO, Chenqi XU, Genwang LIU, et al. Experimental research on oil film thickness and its microwave scattering during emulsification[J]. Journal of Oceanology and Limnology, 2022, 40(4): 1361-1376.
DOI：

Jie GUO, Chenqi XU, Genwang LIU, et al. Experimental research on oil film thickness and its microwave scattering during emulsification[J]. Journal of Oceanology and Limnology, 2022, 40(4): 1361-1376. DOI： 10.1007/s00343-021-1183-x.

摘要

Abstract

Synthetic Aperture Radar (SAR) plays a major role in identifying oil spills on the sea surface. However

obtaining information of oil spill thickness (volume) is still a challenge. Emulsification is an important process affecting the thickness and normalized radar cross section (NRCS) of oil film. Experiments of crude oil emulsification with C-band fully-polarized scatterometer were conducted combining airborne hyperspectral imaging spectrometer and 3D laser scanner observation data

to provide experimental parameters and method to support accurate remote sensing monitoring on marine oil spill. It is further proved that through quantitative homogeneous emulsified oil spill experiments

to a certain extent

the NRCS of oil film increased during the emulsification process of crude oil. The backscattering mechanism of crude oil emulsification was explored using a semi-empirical model (SEM); the change of oil film NRCS was modulated by its dielectric constant and surface roughness

in which the dielectric constant showed a dominant effect. The relationship between thickness and NRCS of oil film was studied under two experimental conditions. The differences of NRCS between oil film and adjacent seawater (Δ

) and the damping ratio (DR) were found to have a linear relationship with oil thickness

which were best in the vertical polarization mode (VV) at 45° incident angle during the quantitative crude oil homogeneous emulsification process. In the natural emulsification process of continuous oil spill in which oil film was mixed with both crude oil and emulsified oil

an empirical equation of oil film thickness is preliminarily established. The Δ

and the empirical equation of oil film thickness were applied to the marine continuous oil spill incident on a 19-3 oil platform with spaceborne SAR image and successfully explained the distribution of the relative thickness of the oil film.

关键词

Keywords

references

Angelliaume S, Boisot O, Guérin C A. 2018. Dual-polarized l-band SAR imagery for temporal monitoring of marine oil slick concentration. Remote Sensing , 10 (7): 1012, https://doi.org/10.3390/rs10071012..

Del Frate F, Petrocchi A, Lichtenegger J, Calabresi G. 2000. Neural networks for oil spill detection using ERS-SAR data. IEEE Transactions on Geoscience and Remote Sensing , 38 (5): 2282-2287, https://doi.org/10.1109/36.868885..

Espeseth M M, Jones C E, Holt B, Brekke C, Skrunes S. 2020. Oil-spill-response-oriented information products derived from a rapid-repeat time series of SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 : 3448-3461, https://doi.org/10.1109/jstars.2020.3003686..

Espeseth M M, Skrunes S, Jones C E, Brekke C, Holt B, Doulgeris A P. 2017. Analysis of evolving oil spills in full-polarimetric and hybrid-polarity SAR. IEEE Transactions on Geoscience and Remote Sensing , 55 (7): 4190-4210, https://doi.org/10.1109/tgrs.2017.2690001..

Fingas M, Brown C E. 2018. A review of oil spill remote sensing. Sensors , 18 (1): 91, https://doi.org/10.3390/s18010091..

Fingas M, Fieldhouse B, Mullin J. 1999. Water-in-oil emulsions results of formation studies and applicability to oil spill modelling. Spill Science & Technology Bulletin , 5 (1): 81-91, https://doi.org/10.1016/S1353-2561(98)00016-4..

Fingas M, Fieldhouse B. 2003. Studies of the formation process of water-in-oil emulsions. Marine Pollution Bulletin , 47 (9-12): 369-396, https://doi.org/10.1016/s0025-326x(03)00212-1..

Fingas M. 1995. Water-in-oil emulsion formation: a review of physics and mathematical modelling. Spill Science & Technology Bulletin , 2 (1): 55-59, https://doi.org/10.1016/1353-2561(95)94483-z..

Garcia-Pineda O, MacDonald I, Hu C M, Svejkovsky J, Hess M, Dukhovskoy D, Morey S L. 2013. Detection of floating oil anomalies from the deepwater horizon oil spill with synthetic aperture radar. Oceanography , 26 (2): 124-137, https://doi.org/10.5670/oceanog.2013.38..

Garcia-Pineda O, Staples G, Jones C E, Hu C M, Holt B, Kourafalou V, Graettinger G, Dipinto L, Ramirez E, Streett D, Cho J, Swayze G A, Sun S J, Garcia D, Haces-Garcia F. 2020. Classification of oil spill by thicknesses using multiple remote sensors. Remote Sensing of Environment , 236 : 111421, https://doi.org/10.1016/j.rse.2019.111421..

Garcia-Pineda O, Zimmer B, Howard M, Pichel W, Li X F, MacDonald I R. 2009. Using SAR images to delineate ocean oil slicks with a texture-classifying neural network algorithm (TCNNA). Canadian Journal of Remote Sensing , 35 (5): 411-421, https://doi.org/10.5589/m09-035..

Guo J, Luo Y M, Ge F, Wang D H, Diao M X, Yang Q X, Liu Y, Qu Z, Zhang T L. 2019. Study on crude oil and its emulsification characteristics. In: 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Yokohama, Japan. p. 8257-8259, https://doi.org/10.1109/IGARSS.2019.8898844.

Guo J, Zhang T L, Zhang X, Liu G W. 2020. Impact of emulsification of crude oil on normalized radar cross section. Journal of Oceanology and Limnology , 38 (1): 42-54, https://doi.org/10.1007/s00343-019-8298-3..

Han H J. 2019. Study on Testing Technology of Complex Dielectric Constant of Aqueous Crude Oil. Xi'an Shiyou University, Xi'an. (in Chinese with English abstract)

Ivonin D V, Skrunes S, Brekke C, Ivanov A Y. 2016. Interpreting sea surface slicks on the basis of the normalized radar cross-section model using RADARSAT-2 copolarization dual-channel SAR images. Geophysical Research Letters , 43 (6): 2748-2757, https://doi.org/10.1002/2016gl068282..

Jones C E, Holt B. 2018. Experimental L-band airborne SAR for oil spill response at sea and in coastal waters. Sensors , 18 (2): 641, https://doi.org/10.3390/s18020641..

Leifer I, Lehr W J, Simecek-Beatty D, Bradley E, Clark R, Dennison P, Hu Y X, Matheson S, Jones C E, Holt B, Reif M, Roberts D A, Svejkovsky J, Swayze G, Wozencraft J. 2012. State of the art satellite and airborne marine oil spill remote sensing: application to the BP Deepwater Horizon oil spill. Remote Sensing of Environment , 124 : 185-209, https://doi.org/10.1016/j.rse.2012.03.024..

Li H Y, Perrie W, Wu J. 2019. Retrieval of oil-water mixture ratio at ocean surface using compact polarimetry synthetic aperture radar. Remote Sensing , 11 (7): 816, https://doi.org/10.3390/rs11070816..

Liu P, Zhao C F, Li X F, He M X, Pichel W. 2010. Identification of ocean oil spills in SAR imagery based on fuzzy logic algorithm. International Journal of Remote Sensing , 31 (17-18): 4819-4833, https://doi.org/10.1080/01431161.2010.485147..

Lu Y, Tian Q, Wang X, Zheng G, Li X. 2013. Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China. International Journal of Digital Earth , 6 (1): 76-93, https://doi.org/10.1080/17538947.2012.695404..

Lu Y C, Tian Q J, Li X. 2011. The remote sensing inversion theory of offshore oil slick thickness based on a two-beam interference model. Science China Earth Sciences , 54 (5): 678-685, https://doi.org/10.1007/s11430-010-4154-1..

Migliaccio M, Gambardella A, Tranfaglia M. 2007. SAR polarimetry to observe oil spills. IEEE Transactions on Geoscience and Remote Sensing , 45 (2): 506-511, https://doi.org/10.1109/tgrs.2006.888097..

Minchew B, Jones C E, Holt B. 2012. Polarimetric analysis of backscatter from the deepwater horizon oil spill using L-band synthetic aperture radar. IEEE Trans actions on Geoscience and Remote Sensing , 50 (10): 3812-3830, https://doi.org/10.1109/TGRS.2012.2185804..

Minchew B. 2012. Determining the mixing of oil and sea water using polarimetric synthetic aperture radar. Geophysical Research Letters , 39 : L16607, https://doi.org/10.1029/2012gl052304..

Nunziata F, Gambardella A, Migliaccio M. 2008. On the Mueller scattering matrix for SAR sea oil slick observation. IEEE Geoscience and Remote Sensing Letters , 5 (4): 691-695, https://doi.org/10.1109/lgrs.2008.2003127..

Ren G B, Guo J, Ma Y, Luo X D. 2019. Oil spill detection and slick thickness measurement via UAV hyperspectral imaging. Acta Oceanologica Sinica , 41 (5): 146-158, https://doi.org/10.3969/j.issn.0253-4193.2019.05.015. (in Chinese with English abstract).

Richards J A. 2009. Remote Sensing with Imaging Radar. Springer-Verlag, Berlin, Heidelberg.

Sergievskaya I, Ermakov S, Lazareva T, Guo J. 2019. Damping of surface waves due to crude oil/oil emulsion films on water. Marine Pollution Bulletin , 146 : 206-214, https://doi.org/10.1016/j.marpolbul.2019.06.018..

Shi J, Jiao J, Lu Y, Zhang M, Mao Z, Liu Y. 2018. Determining spectral groups to distinguish oil emulsions from sargassum over the Gulf of Mexico using an airborne imaging spectrometer. ISPRS Journal of Photogrammetry and Remote Sensing , 146 : 251-259, https://doi.org/10.1016/j.isprsjprs.2018.09.017..

Shu S J, Meng J M, Zhang X, Guo J, Liu G W. 2020. Experimental study of C-band microwave scattering characteristics during the emulsification process of oil spills. Acta Oceanologica Sinica , 39 (7): 135-145, https://doi.org/10.1007/s13131-020-1612-4..

Skrunes S, Brekke C, Eltoft T. 2014. Characterization of marine surface slicks by radarsat-2 multipolarization features. IEEE Transactions on Geoscience and Remote Sensing , 52 (9): 5302-5319, https://doi.org/10.1109/tgrs.2013.2287916..

Solberg A H S, Brekke C, Husoy P O. 2007. Oil spill detection in Radarsat and Envisat SAR images. IEEE Transactions on Geoscience and Remote Sensing , 45 (3): 746-755, https://doi.org/10.1109/tgrs.2006.887019..

Song D M, Ding Y X, Li X F, Zhang B, Xu M Y. 2017. Ocean oil spill classification with radarsat-2 SAR based on an optimized wavelet neural network. Remote Sensing , 9 (8): 799, https://doi.org/10.3390/rs9080799..

Sun S J, Hu C M. 2019. The challenges of interpreting oil-water spatial and spectral contrasts for the estimation of oil thickness: examples from satellite and airborne measurements of the deepwater horizon oil spill. IEEE Transactions on Geoscience and Remote Sensing , 57 (5): 2643-2658, https://doi.org/10.1109/tgrs.2018.2876091..

Svejkovsky J, Hess M, Muskat J, Nedwed T J, McCall J, Garcia O. 2016. Characterization of surface oil thickness distribution patterns observed during the Deepwater Horizon (MC-252) oil spill with aerial and satellite remote sensing. Marine Pollution Bulletin , 110 (1): 162-176, https://doi.org/10.1016/j.marpolbul.2016.06.066..

Wen Y S, Wang M Q, Lu Y C, Sun S J, Zhang M W, Mao Z H, Shi J, Liu Y X. 2018. An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint. International Journal of Digital Earth , 11 (9): 972-979, https://doi.org/10.1080/17538947.2018.1470687..

Wettle M, Daniel P J, Logan G A, Thankappan M. 2009. Assessing the effect of hydrocarbon oil type and thickness on a remote sensing signal: a sensitivity study based on the optical properties of two different oil types and the HYMAP and Quickbird sensors. Remote Sensing of Environment , 113 (9): 2000-2010, https://doi.org/10.1016/j.rse.2009.05.010..

Wismann V, Gade M, Alpers W, Huhnerfuss H. 1998. Radar signatures of marine mineral oil spills measured by an airborne multi-frequency radar. International Journal of Remote Sensing , 19 (18): 3607-3623, https://doi.org/10.1080/014311698213849..

Yue H S, Guo J, Mu Y K, Zhang T L. 2017. Detection of oil film roughness of 3D laser scanner. Journal of Guangxi Academy of Sciences , 33 (4): 298-302, https://doi.org/10.13657/j.cnki.gxkxyxb.20171127.002. (in Chinese with English abstract).

Zhang B, Perrie W, Li X F, Pichel W G. 2011. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophysical Research Letters , 38 : L10602, https://doi.org/10.1029/2011gl047013..

Zheng H L, Zhang Y M, Wang Y H, Zhang X, Meng J M. 2017. The polarimetric features of oil spills in full polarimetric synthetic aperture radar images. Acta Oceanologica Sinica , 36 (5): 105-114, https://doi.org/10.1007/s13131-017-1065-4..

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