Mapping Marine Oil Spill Concentrations From SAR Images Using a Co-Polarization Difference-Based Method

Accurate mapping of oil concentrations is essential for effective response to oil spill emergencies. The complexity of microwave scattering over oil-contaminated sea surfaces poses substantial challenges for synthetic aperture radar (SAR) applications, primarily due to the limited understanding of non-Bragg scattering mechanisms. This knowledge gap restricts the development of robust retrieval algorithms for quantifying oil spill concentrations. To address this issue, a novel retrieval approach is proposed based on the co-polarization difference (PD), which is independent of non-Bragg scattering. The influence of oil on the sea surface is attributed to two dominant factors: suppression of short gravity-capillary waves and reduction in the effective dielectric constant. By analyzing SAR imagery of oil spills with varying concentrations, it is found that the damping effect of oil spills on small-scale waves can be predicted using the Marangoni damping model. Once the contribution of wave suppression to PD reduction is isolated, the residual PD variation is attributed to changes in the dielectric constant. Oil concentration is then retrieved by comparing the PD of each pixel within the contaminated area to a theoretical PD lookup table. The proposed method is validated using simulated SAR datasets representing different oil concentrations and subsequently applied to SAR data acquired during the Deepwater Horizon oil spill in the Gulf of Mexico.

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Publication Details

Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3593435
Citations
1
Primary Topic
Oil Spill Detection and Mitigation
Type
article
Field-Weighted Citation Impact
0.50

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article

Mapping Marine Oil Spill Concentrations From SAR Images Using a Co-Polarization Difference-Based Method

Weimin Huang, Peng Ren, Yunhua Wang, Honglei Zheng
1 citations
IEEE Transactions on Geoscience and Remote Sensing
Oil Spill Detection and Mitigation
0.50
article

Mapping Marine Oil Spill Concentrations From SAR Images Using a Co-Polarization Difference-Based Method

Weimin Huang, Peng Ren, Yunhua Wang, Honglei Zheng
article en
1 citations

Abstract

Accurate mapping of oil concentrations is essential for effective response to oil spill emergencies. The complexity of microwave scattering over oil-contaminated sea surfaces poses substantial challenges for synthetic aperture radar (SAR) applications, primarily due to the limited understanding of non-Bragg scattering mechanisms. This knowledge gap restricts the development of robust retrieval algorithms for quantifying oil spill concentrations. To address this issue, a novel retrieval approach is proposed based on the co-polarization difference (PD), which is independent of non-Bragg scattering. The influence of oil on the sea surface is attributed to two dominant factors: suppression of short gravity-capillary waves and reduction in the effective dielectric constant. By analyzing SAR imagery of oil spills with varying concentrations, it is found that the damping effect of oil spills on small-scale waves can be predicted using the Marangoni damping model. Once the contribution of wave suppression to PD reduction is isolated, the residual PD variation is attributed to changes in the dielectric constant. Oil concentration is then retrieved by comparing the PD of each pixel within the contaminated area to a theoretical PD lookup table. The proposed method is validated using simulated SAR datasets representing different oil concentrations and subsequently applied to SAR data acquired during the Deepwater Horizon oil spill in the Gulf of Mexico.

IEEE Transactions on Geoscience and Remote SensingVol. 63
Memorial University of Newfoundland (CA), China University of Petroleum, East China (CN), Ocean University of China (CN)
National Natural Science Foundation of China, China Scholarship Council, Natural Science Foundation of Shandong Province
Life below water
Openalex Percentile: Top 37%
Oil Spill Detection and Mitigation
0.50
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