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Research Article

Geochemical analysis of SAR backscattering (Sentinel-1) on global ocean oil spill cases

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Article: 2256959 | Received 10 Dec 2022, Accepted 05 Sep 2023, Published online: 21 Sep 2023

References

  • Ajadi, O. A., Meyer, F. J., Tello, M., & Ruello, G. (2018). Oil spill detection in synthetic aperture radar images using Lipschitz-regularity and multiscale techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7), 2389–13. https://doi.org/10.1109/JSTARS.2018.2827996
  • Alpers, W., Holtb, B., & Zzengc, K. (2017). Oil spill detection by imaging radars: Challenges and pitfalls. Remote Sensing of Environment, 201, 133–147. https://doi.org/10.1016/j.rse.2017.09.002
  • Alpers, W., & Hühnerfuss, H. (1989). The damping of ocean waves by surface films: A new look at an old problem. Journal of Geophysical Research, 94(C5), 6251–6265. https://doi.org/10.1029/JC094iC05p06251
  • Benelli, G., & Garzelli, A. (1999). Oil-spills detection in SAR images by fractal dimension estimation. In Geoscience and Remote Sensing Symposium, 28 June 1999–02 July 1999, Online Publication (Vol. 1, pp. 218–220). IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).
  • Beyer, J., Trannum, H. C., Bakke, T., Hodson, P. V., & Collier, T. K. (2016). Environmental effects of the Deepwater Horizon oil spill: A review. Marine Pollution Bulletin, 110(1), 28–51. https://doi.org/10.1016/j.marpolbul.2016.06.027
  • Chaturvedi, S. K., Banerjee, S., & Lele, S. (2020). An assessment of oil spill detection using Sentinel 1 SAR-C images. Journal of Ocean Engineering and Science, 5(2), 166–135. https://doi.org/10.1016/j.joes.2019.09.004
  • Chedi, I. Y., Amitrano, D., & Guida, R.2021 Influence on radar back-scatter of oil spreading and evaporation in marine oil spills. In EUSAR 2021; 13th European Conference on Synthetic Aperture Radar, 29 March 2021–01 April 2021, Online Conference (pp. 1–6). VDE.
  • Chung, S., Loh, A., Jennings, C. M., Sosnowski, K., Ha, S. Y., Yim, U. H., & Yoon, J. Y. (2023). Capillary flow velocity profile analysis on paper-based microfluidic chips for screening oil types using machine learning. Journal of Hazardous Materials, 447, 130806. https://doi.org/10.1016/j.jhazmat.2023.130806
  • Conceição, M. R. A., Mendonça, L. F. F., Lentini, C. A. D., Lima, A. T. C., Lopes, J. M., Vasconcelos, R. N., Gouveia, M. B., & Porsani, M. J. (2021). SAR oil spill detection System through random forest classifiers. Remote Sensing, 13(11), 2044. https://doi.org/10.3390/rs13112044
  • Ermakov, S. A., Sergievskaya, I. A., Da Silva, J. C., Kapustin, I. A., Shomina, O. V., Kupaev, A. V., & Molkov, A. A. (2018). Remote sensing of organic films on the water surface using dual co-polarized ship-based X-/C-/S-band radar and TerraSAR-X. Remote Sensing, 10(7), 1097. https://doi.org/10.3390/rs10071097
  • 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. (2011). Introduction to oil chemistry and properties. In M. FINGAS. (ed.), Oil spill science and technology: Prevention, response, and cleanup (1st ed., Vol. 3, pp. 51–59). Elsevier. https://doi.org/10.1016/B978-1-85617-943-0.10003-6
  • Fingas, M. (2018). The challenges of remotely measuring oil slick thickness. Remote Sensing, 10(2), 319. https://doi.org/10.3390/rs10020319
  • Gade, M., Alpers, W., Hühnerfuss, H., Masuko, H., & Kobayashi, T. (1998). Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/multipolarization SIR‐C/X‐SAR. Journal of Geophysical Research: Oceans, 103(C9), 18851–18866. https://doi.org/10.1029/97JC01915
  • Gong, Y., Zhao, X., Cai, Z., O’reilly, S. E., Hao, X., & Zhao, D. (2014). A review of oil, dispersed oil and sediment interactions in the aquatic environment: Influence on the fate, transport and remediation of oil spills. Marine Pollution Bulletin, 79(1–2), 16–33. https://doi.org/10.1016/j.marpolbul.2013.12.024
  • Guzmán-Osorio, F. J., Adams, R. H., Domínguez-Rodríguez, V. I., Lobato-García, C. E., Guerrero-Peña, A., Barajas-Hernández, J. R., & Baltierra-Trejo, E. (2020). Alternative method for determining API degrees of petroleum in contaminated soil by FTIR. Egyptian Journal of Petroleum, 29(1), 39–44. https://doi.org/10.1016/j.ejpe.2019.10.002
  • Hersbach, H. (2010). Comparison of C-band scatterometer CMOD5. Equivalent neutral winds with ECMWF. Journal of Atmospheric and Oceanic Technology, 27(4), 4, 721–736. https://doi.org/10.1175/2009JTECHO698.1
  • Huang, X., Zhang, B., Perrie, W., Lu, Y., & Wang, C. (2022). A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery. Marine Pollution Bulletin, 179, 113666. https://doi.org/10.1016/j.marpolbul.2022.113666
  • Jayasinghe, A., Elliott, S., Gibson, G. A., & Vandemark, D. (2022). The role of phytoplankton biomacromolecules in controlling ocean surface roughness. Atmosphere, 13(12), 2101. https://doi.org/10.3390/atmos13122101
  • Jha, M. N., Levy, J., & Gao, Y. (2008). Advances in Remote Sensing for oil spill disaster management: State-of-the-art sensors Technology for oil spill surveillance. Sensors, 8(1), 236–255. https://doi.org/10.3390/s8010236
  • Johnson, J. W., & Croswell, W. F. (1982). Characteristics of 13.9 GHz radar scattering from oil films on the sea surface. Radio Science, 17(3), 611–617. https://doi.org/10.1029/RS017i003p00611
  • Keramea, P., Spanoudaki, K., Zodiatis, G., Gikas, G., & Sylaios, G. (2021). Oil spill modeling: A critical review on current trends, perspectives, and challenges. Journal of Marine Science and Engineering, 9(2), 181. https://doi.org/10.3390/jmse9020181
  • Latini, D., Del Frate, E., & Jones, C. F. (2016). Multi-frequency and polarimetric quantitative analysis of the Gulf of Mexico oil spill event comparing different SAR systems. Remote Sensing of Environment, 183, 26–42. https://doi.org/10.1016/j.rse.2016.05.014
  • Lentini, C. A. D., Mendonça, L. F. F., Conceição, M. R. A., Lima, A. T., Vasconcelos, R. N., & Porsani, M. J. (2022). Comparison between oil spill images and look-alikes: An evaluation of SAR-derived observations of the 2019 oil spill incident along Brazilian waters. Anais da Academia Brasileira de Ciências, 94(suppl 2), 94. https://doi.org/10.1590/0001-3765202220211207
  • Li, P., Cai, Q., Lin, W., Chen, B., & Zhang, B. (2016). Offshore oil spill response practices and emerging challenges. Marine Pollution Bulletin, 110(1), 6–27. https://doi.org/10.1016/j.marpolbul.2016.06.020
  • Liubartsevaa, S., Smaouib, M., Coppinic, G., Gonzales, G., Leccic, R., Cretìc, S., & Federico, I. (2020). Model-based reconstruction of the Ulysse-Virginia oil spill, October–November 2018. Marine Pollution Bulletin, 154, 154. https://doi.org/10.1016/j.marpolbul.2020.111002
  • Marta-Almeida, M., Ruiz-Villarreal, M., Pereira, J., Otero, P., Cirano, M., Zhang, X., & Hetland, R. D. (2013). Hetland efficient tools for marine operational forecast and oil spill tracking. Marine Pollution Bulletin, 71(1–2), 139–151. https://doi.org/10.1016/j.marpolbul.2013.03.022
  • Meng, T., Yang, X., Chen, K. S., Nunziata, F., Xie, D., & Buono, A. (2021). Radar backscattering over sea surface oil emulsions: Simulation and observation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14. https://doi.org/10.1109/TGRS.2021.3073369
  • Migliaccio, M., Nunziata, F., Montuori, A., Li, X., & Pichel, W. G. (2011). Multi-frequency polarimetric SAR processing chain to observe oil fields in the Gulf of Mexico. IEEE- Transactions on Geoscience and Remote Sensing, 44(12), 4729–4737. https://doi.org/10.1109/TGRS.2011.2158828
  • 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 Transactions on Geoscience & Remote Sensing, 50(10), 3812–3830. https://doi.org/10.1109/TGRS.2012.2185804
  • Mohammadiun, S., Hu, G., Gharahbagh, A. A., Li, J., Hewage, K., & Sadiq, R. (2021). Intelligent computational techniques in marine oil spill management: A critical review. Journal of Hazardous Materials, 419, 126425. https://doi.org/10.1016/j.jhazmat.2021.126425
  • Naz, S., Iqbal, M. F., Mahmood, I., & Allam, M. (2021). Marine oil spill detection using synthetic aperture radar over indian ocean. Marine Pollution Bulletin, 162, 111921. https://doi.org/10.1016/j.marpolbul.2020.111921
  • Nissanka, I. D., & Yapa, P. D. (2018). Calculation of oil droplet size distribution in ocean oil spills: A review. Marine Pollution Bulletin, 135, 723–734. https://doi.org/10.1016/j.marpolbul.2018.07.048
  • Nunziata, F., Buono, A., & Migliaccio, M. (2018). COSMO-SkyMed synthetic aperture radar data to observe the deepwater horizon oil spill. Sustainability, 10(10), 3599.
  • Pabón, R. E. C., & de Souza Filho, C. R. (2019). Crude oil spectral signatures and empirical models to derive API gravity. Fuel, 237, 1119–1131. https://doi.org/10.1016/j.fuel.2018.09.098
  • Quigley, C., Brekke, C., & Eltoft, T. (2020). Retrieval of marine surface slick dielectric properties from radarsat-2 data via a polarimetric two-Scale model. IEEE Transactions on Geoscience and Remote Sensing, 58(7), 5162–5178. https://doi.org/10.1109/TGRS.2020.2973724
  • Rix, G. J., Lai, C. G., & Spang, A. W., Jr. (2000). In situ measurement of damping ratio using surface waves. Journal of Geotechnical and Geoenvironmental Engineering, 126(5), 472–480. https://doi.org/10.1061/(ASCE)1090-0241(2000)126:5(472)
  • Sato, T., Suzuki, Y., Kashiwagi, H., Nanjo, M., & Kakui, Y. (1978). Laser radar for remote detection of oil spills. Applied Optics, 17(23), 3798–3803. https://doi.org/10.1364/AO.17.003798
  • 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
  • Shen, H., Perrie, W., & Wu, Y. (2019). Wind drag in oil spilled ocean surface and its impact on wind-driven circulation. Anthropocene Coasts, 2(1), 244–260. https://doi.org/10.1139/anc-2018-0019
  • Shinga, S., Vespe, M., & Trieschmann, O. (2013). Automatic synthetic aperture radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark. Marine Pollution Bulletin, 73(1), 199–209. https://doi.org/10.1016/j.marpolbul.2013.05.022
  • Shuming, L., Ziwei, L. I., Xiaofeng, Y., William, P. G., Yang, Y., Zheng, Q., & Xiaofeng, L. I. (2010). Atmospheric frontal gravity waves observed in satellite SAR images of the Bohai sea and Huanghai sea. Acta Oceanologica Sinica, 29(5), 35–43. https://doi.org/10.1007/s13131-010-0061-8
  • Skruners, S., Brekke, C., Eltoft, T., & Kudryavtsev, V. (2014). Comparing near-coincident C-and X-band SAR acquisitions of marine oil spills. IEEE Transactions on Geoscience and Remote Sensing, 53(4), 1958–1975. https://doi.org/10.1109/TGRS.2014.2351417
  • Skruners, S., Brekke, C., Eltoft, T., & Kudryavtsev, V. (2015). Comparing near-coincident C- and X-Band SAR Acquisitions of marine oil spills. IEEE Transactions on Geoscience and Remote Sensing, 53(4), 1958–1975. https://doi.org/10.1109/TGRS.2014.2351417
  • Ülker, D., Burak, S., Balas, L., & Çağlar, N. (2022). Mathematical modelling of oil spill weathering processes for contingency planning in Izmit Bay. Regional Studies in Marine Science, 50, 102155. https://doi.org/10.1016/j.rsma.2021.102155
  • Vafaie, A., & Kivi, I. R. (2020). An investigation on the effect of thermal maturity and rock composition on the mechanical behavior of carbonaceous shale formations. Marine and Petroleum Geology, 116, 104315. https://doi.org/10.1016/j.marpetgeo.2020.104315
  • Wang, Z., An, C., Lee, K., Owens, E., Chen, Z., Boufadel, M., Taylor, E., & Feng, Q. (2021). Factors influencing the fate of oil spilled on shorelines: A review. Environmental Chemistry Letters, 19(2), 1611–1628. https://doi.org/10.1007/s10311-020-01097-4
  • Wang, Q., Leonce, B., Meredith, E. S., Adegboyega, N. F., Lu, K., Hockday, W. C., & Liu, W. (2020). Elucidating the formation pathway of photo-generated asphaltenes from light Louisiana sweet crude oil after exposure to natural sunlight in the Gulf of Mexico. Organic Geochemistry, 150. https://doi.org/10.1016/j.orggeochem.2020.104126
  • Ward, C. P., Sharpless, C. M., Valentine, D. L., French McCay, D. P., Aeppli, C., White, H. K., Rodgers, R. P., Gosselin, K. M., Nelson, R. K., & Reddy, C. M. (2018). Partial photochemical oxidation was a dominant fate of Deepwater Horizon surface oil. Environmental Science & Technology, 52(4), 1797–1805. https://doi.org/10.1021/acs.est.7b05948
  • Wismann, V., Gade, M., Alpers, W., & Hühnerfuss, W. 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
  • Xu, J., Wang, H., Cui, C., Zhao, B., & Li, B. (2020). Oil spill monitoring of shipborne radar image features using SVM and local adaptive threshold. Algorithms, 13(3), 69. https://doi.org/10.3390/a13030069
  • Yekeen, S. T., Balogun, A. L., & Yusof, K. B. W. (2020). A novel deep learning instance segmentation model for automated marine oil spill detection. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 190–200. https://doi.org/10.1016/j.isprsjprs.2020.07.011
  • Yim, U. H., Ha, S. Y., An, J. G., Won, J. H., Han, G. M., Hong, S. H., Kim, M., Jung, J. H., & Shim, W. J. (2011). Fingerprint and weathering characteristics of stranded oils after the Hebei Spirit oil spill. Journal of Hazardous Materials, 197, 60–69. https://doi.org/10.1016/j.jhazmat.2011.09.055
  • Zhang, K., Huang, J., Mansaray, L. R., Guo, Q., & Wang, X. (2018). Developing a subswath-based wind speed retrieval model for Sentinel-1 VH-polarized SAR data over the ocean surface. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1561–1572. https://doi.org/10.1109/TGRS.2018.2867438
  • Zhang F. F., and Li, S. L. (2011). A study on oil spill identification based on backscattering of SAR data. 5th International Conference on Bioinformatics and Biomedical Engineering, 10–12 May 2011, Wuhan (Vol.1, pp. 4). https://doi.org/10.1109/icbbe.2011.5780769
  • Zhang, Y., Li, Y., Liang, X. S., & Tsou, J. (2017). Comparison of oil spill classifications using fully and compact polarimetric SAR images. Applied Sciences, 7(2), 193. https://doi.org/10.3390/app7020193