volume | PIER Journals

Abstract

Airborne and spaceborne optical remote sensing is an important means formonitoring oil slicks on ocean surface. However, it is still a major challenge to determine both the category (related to a specific value of reflective index) and thickness of the marine oil slick with existing methods, particularly when the oil slick is too thin to obtain significant fluorescence signal with a laser induced fluorescence method. Sun-glint is usually harmful to optical remote sensing of an ocean target. In this work we utilize the polarized sun-glint reflection to monitor oil slicks on a rough ocean surface.The degree of linear polarization (DOLP) of the sun-glint reflection contains the characteristics information of the oil slick with different physical properties. Combiningthe polarized optical remote sensing and the inversion theory based on a thin-film optical model, weanalyze the variation trend of the DOLP with the parameters of solar zenith angle, sensor zenith angle, relative azimuth angle, refractive index and thickness of the oil slick. Different types and thicknesses of the oil slicksgive different Fresnel's reflection coefficients of polarized sun-glint reflections and consequently different Stokes parameters, which lead to different DOLP. We analyze the DOLP of the sun-glint reflection at the wavelength of 532 nm,and determine simultaneously the refractive index and thickness of marineoil slick from the DOLP values measured by a remote detector at two different zenith angles.

1. Ying, L., B.-X. Liu, and P. Chen, "Study advancement in oil spill monitoring using hyper-spectral remote sensing," Marine Environmental Science, Vol. 31, 460-464, 2012. Google Scholar

2. Alam, M. S. and P. Sidike, "Trends in oil spill detection via hyperspectral imaging," International Conference on Electrical and Computer Engineering, 858-862, IEEE, 2013. Google Scholar

3. El-Rahman, S. A. and A. H. S. Zolait, "Hyperspectral image analysis for oil spill detection: A comparative study," International Journal of Computing Science & Mathematics, Vol. 9, No. 2, 2018.
doi:10.1504/IJCSM.2018.10012786 Google Scholar

4. Salem, F., M. Kafatos, T. El-Ghazawi, R. Gomez, and R. Yang, "Hyperspectral image analysis for oil spill detection," Summaries of NASA/JPL Airborne Earth Science Workshop, 5-9, Pasadena, November 2001. Google Scholar

5. Liu, D.-L., L. Han, and J.-Q. Zhang, "Study of automatic marine oil spills detection using imaging spectroscopy," Spectroscopy and Spectral Analysis, Vol. 11, 3116-3119, 2013. Google Scholar

6. Lei, C., R. Deng, and X. Jian, "Study on monitoring offshore platform oil spill based on Aisa + airborne hyperspectral image taking the Zhujiang River estuary as an example," Transactions of Oceanology & Limnology, Vol. 1, 179-184, 2009. Google Scholar

7. Lu, Y., Y. Zhou, Y. Liu, et al. "Using remote sensing to detect the polarized sunglint reflected from oil slicks beyond the critical angle," Journal of Geophysical Research, Vol. 122, No. 8, 6342-6354, 2017. Google Scholar

8. Baszanowska, E. and Z. Otremba, "Spectral signatures of fluorescence and light absorption to identify crude oils found in the marine environment," Journal of the European Optical Society Rapid Publications, Vol. 9, No. 16, 4583-4587, 2014. Google Scholar

9. Hagemann, H. W. and A. Hollerbach, "The fluorescence behavior of crude oils with respect to their thermal maturation and degradation," Organic Geochemistry, Vol. 10, No. 1, 473-480, 1986.
doi:10.1016/0146-6380(86)90047-1 Google Scholar

10. Stasiuk, L. D. and L. R. Snowdon, "Fluorescence micro-spectrometry of synthetic and natural hydrocarbon fluid inclusions: Crude oil chemistry, density and application to petroleum migration," Applied Geochemistry, Vol. 12, No. 3, 229-233, 1997.
doi:10.1016/S0883-2927(96)00047-9 Google Scholar

11. Ryder, A. G., "Quantitative analysis of crude oils by fluorescence lifetime and steady state measurements using 380 nm excitation," Applied Spectroscopy, Vol. 56, No. 1, 107-116, 2002.
doi:10.1366/0003702021954287 Google Scholar

12. Alpem, B., et al. "Detection and evaluation of hydrocarbons in source rocks by fluorescence microscopy," Organic Geochemistry, Vol. 20, No. 6, 789-795, 1993.
doi:10.1016/0146-6380(93)90063-H Google Scholar

13. Delaune, P. L., et al. "Enhanced wellsite technique for oil detection and characterization," SPE Annual Technical Conference and Exhibition Proceedings, Formation Evaluation and Reservoir Geology, 801-816, SPE-56802, 1999. Google Scholar

14. Pradier, B., et al. "Chemical basis of fluorescence alteration of crude oils and kerogens. 1. Microfluorimetry of an oil and its isolated fractions --- Relationships with chemical-structure," Organic Geochemistry, Vol. 16, No. 1-3, 451-460, 1990.
doi:10.1016/0146-6380(90)90061-4 Google Scholar

15. Pharr, D. Y., et al. "Fingerprinting petroleum contamination using synchronous scanning fluorescence spectroscopy," Ground Water, Vol. 30, No. 4, 484-489, 1993.
doi:10.1111/j.1745-6584.1992.tb01523.x Google Scholar

16. Gao, F., J. Li, H. Lin, and S. He, "Oil pollution discrimination by an inelastic hyperspectral Scheimpflug lidar system," Optics Express, Vol. 25, No. 21, 25515-25522, 2017.
doi:10.1364/OE.25.025515 Google Scholar

17. Han, Z., J. Wan, Y. Li, K. Liu, and J. Liu, Detection method of marine oil spilling and emulsified oil based on hyperspectral imaging under UV induction, Vol. 36, No. 1, 302-309, Acta Optica Sinica, 2016.

18. Fingas, M. and C. Brown, "Review of oil spill remote sensing," Marine Pollution Bulletin, Vol. 83, No. 1, 9-23, 2014.
doi:10.1016/j.marpolbul.2014.03.059 Google Scholar

19. Liu, D.-Q., X.-N. Luan, J.-J. Guo, T.-W. Cui, J.-C. Jin, and R.-E. Zheng, "A small LIF remote detection system for port oil spill monitoring," Marine Sciences, Vol. 42, No. 1, 65-69, 2018. Google Scholar

20. Violetta, D., S. M. Babichenko, and L. Aleksey, "Natural water fluorescence characteristics based on lidar investigations of a surface water layer polluted by an oil film," Oceanologia, Vol. 44, No. 3, 339-354, 2002. Google Scholar

21. Cox, C. and W. Munk, "Measurement of the roughness of the sea surface from photographs of the sun’s glitter," JOSA, Vol. 44, No. 11, 838-850, 1954.
doi:10.1364/JOSA.44.000838 Google Scholar

22. Jackson, C. R. and W. Alpers, "The role of the critical angle in brightness reversals on sunglint images of the sea surface," Journal of Geophysical Research Oceans, Vol. 115, No. C9, 2010. Google Scholar

23. Zhang, H. and M. H. Wang, "Evaluation of sunglint models using measurements," J. Quant. Spectrosc. Radiat. Transfer, Vol. 111, 492-506, 2010.
doi:10.1016/j.jqsrt.2009.10.001 Google Scholar

24. Chami, M., "Importance of the polarization in the retrieval of oceanic constituents from the remote sensing reflectance," Journal of Geophysical Research: Oceans, Vol. 112, No. C5, 2007. Google Scholar

25. Harmel, T. and M. Chami, "Determination of sea surface wind speed using the polarimetric and multi directional properties of satel-lite measurements in visible bands," Geophysical Research Letters, Vol. 39, No. 19, L19611, 2012.
doi:10.1029/2012GL053508 Google Scholar

26. Priest, R. G. and S. R. Meier, "Polarimetric microfacet scattering theory with applications to absorptive and reflective surfaces," Optical Engineering, Vol. 41, No. 5, 988-993, 2002.
doi:10.1117/1.1467360 Google Scholar

27. Diner, D. J., F. Xu, J. V. Martonchik, B. E. Rheingans, S. Geier, V. M. Jovanovic, Ab. Davis, R. A. Chipman, and S. C. McClain, "Exploration of a polarized surface Bidirectional reflectance model using the ground-based Multiangle Spectro Polarimetric Imager," Atmosphere, Vol. 3, 591-691, 2012.
doi:10.3390/atmos3040591 Google Scholar

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

Prof. Sailing He

Dr. Hongguang Dong