سابِق|تال

HY-1C COCTS and CZI observation of marine oil spills in the South China Sea

  • role： First author第一作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

  • Email：ShenYaFeng1995@foxmail.com
  • Introduction：沈亚峰，1995年生，男，硕士研究生，研究方向为海洋资源与环境遥感。E-mail：ShenYaFeng1995@foxmail.com
SHEN Yafeng1,  
  • Affiliation：

    National Satellite Ocean Application Center, Beijing 100081, China

    Key Laboratory of Space Ocean Remote Sensing and Applications, Ministry of Natural Resources, Beijing 100081, China

LIU Jianqiang23,  
  • Affiliation：

    National Satellite Ocean Application Center, Beijing 100081, China

    Key Laboratory of Space Ocean Remote Sensing and Applications, Ministry of Natural Resources, Beijing 100081, China

DING Jing23,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

JIAO Junnan1,  
  • Affiliation：

    College of Marine Science, Sun Yat-sen University, Zhuhai 510275, China

SUN Shaojie4,  
  • role： Corresponding author通信作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

  • Email：Luyc@nju.edu.cn
  • Introduction：陆应诚，1979年生，男,副教授,研究方向为海洋资源与环境遥感。E-mail: Luyc@nju.edu.cn
LU Yingcheng1*

ملخص

An ocean color satellite of China (Haiyang-1C, HY-1C) was launched on September 7, 2018, equipped with several optical sensors, such as the Chinese Ocean Color and Temperature Scanner (COCTS), Coastal Zone Imager (CZI), and ultraviolet imager. These instruments were tested in orbit for 6 months and used in ocean and coastal zone environmental monitoring in June 2019. The optical remote sensing of oil spills is a key research direction in marine environmental monitoring. Significant progress has been made in recent years, demonstrating its ability to detect, classify, and estimate the volumes of various oil spills. In this paper, the marine oil spill incident near Dongsha Island in the South China Sea on February 20, 2019 was used as a case study. COCTS and CZI captured the oil spill, and the oil spill area was scanned using the Visible Infrared Imager Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS Terra & Aqua) on the same day. The angles (between the viewing direction and the direction of mirror reflection, θm) of COCTS, CZI, VIIRS, and MODIS Terra and Aqua images were extremely small, indicating that sunglint reflectance in these images can be ignored. Therefore, the light absorption and backscattering characteristics of oil spills and surrounding oil-free seawater account for the differences among the images. In other words, these light signals can only weakly detect marine oil spills compared with strong sunglint reflection. However, if backscattering can be distinguished, it can be useful in the identification of various types of weathered oil. COCTS, CZI, and VIIRS capture marine oil spills with high radiometric resolutions and signal-to-noise ratios. Moreover, oil spills form oil emulsions and non-emulsified oil slicks, which can be distinguished by CZI due to its high spatial resolution (~50 m). Uncertainty analysis of COCTS and CZI shows that the difference between an oil spill and the oil-free seawater of COCTS or CZI is obvious, implying that CZI can be used in estimating the volumes of oil spills. In the near future, the same sensors in the HY-1D satellite will be available, and a network observation system of the HY-1C/D satellite will provide global images daily. This resource will likely play an important role in future ocean color remote sensing.

مفهوم

remote sensing;HY-1C;COCTS;CZI;oil spills;optical remote sensing

References

  1. 1.
    Brekke C and Solberg A H S. 2005. Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95(1): 1-13
  2. 2.
    Clark R N, Swayze G A, Leifer I, Livo K E, Kokaly R, Hoefen T, Lundeen S, Eastwood M, Green R O, Pearson N, Sarture C, McCubbin I, Roberts D, Bradley E, Steele D, Ryan T, Dominguez R and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. 2010. A method for quantitative mapping of thick oil spills using imaging spectroscopy. Open-File Report 2010-1167. U. S. Geological Survey
  3. 3.
    Fu Y H, Li Q J and Zhang B R. 2008. Spectroscopic analysis of oil spill at sea and satellite information distilling. Journal of Remote Sensing, 12(6): 1010-1016
  4. 4.
    Garcia-Pineda O, MacDonald I, Hu C M, Svejkovsky J, Hess M, Dukhovskoy D and Morey S L. 2013. Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar. Oceanography, 26(2): 124-137
  5. 5.
    Hu C M, Feng L, Holmes J, Swayze G A, Leifer I, Melton C, Garcia O, MacDonald I, Hess M, Muller-Karger F, Graettinger G and Green R. 2018. Remote sensing estimation of surface oil volume during the 2010 Deepwater Horizon oil blowout in the Gulf of Mexico: scaling up AVIRIS observations with MODIS measurements. Journal of Applied Remote Sensing, 12(2): 026008
  6. 6.
    Hu C M, Feng L and Lee Z P. 2013. Uncertainties of seaWiFS and MODIS remote sensing reflectance: implications from clear water measurements. Remote sensing of Environment, 133: 168-182
  7. 7.
    Hu C M, Li X F, Pichel W G and Müller-Karger F E. 2009. Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery. Geophysical Research Letters, 36: L01604
  8. 8.
    Jackson C R and Alpers W. 2010. The role of the critical angle in brightness reversals on sunglint images of the sea surface. Journal of Geophysical Research, 115(C9): C09019
  9. 9.
    Jones C E and Holt B. 2018. Experimental L-Band airborne SAR for oil spill response at sea and in coastal waters. Sensors, 18(2): 641
  10. 10.
    Kessler J D, Valentine D L, Redmond M C, Du M, Chan E W, Mendes S D, Quiroz E W, Villanueva C J, Shusta S S, Werra L M, Yvon-Lewis S A and Weber T C. 2011. A persistent oxygen anomaly reveals the fate of spilled methane in the Deep Gulf of Mexico. Science, 331(6015): 312-315
  11. 11.
    Kvenvolden K A and Cooper C K. 2003. Natural seepage of crude oil into the marine environment. Geo-Marine Letters, 23(3/4): 140-146
  12. 12.
    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 and 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
  13. 13.
    Lu Y C, Hu C M, Sun S J, Zhang M W, Zhou Y, Shi J and Wen Y S. 2016. Overview of optical remote sensing of marine oil spills and hydrocarbon seepage. Journal of Remote Sensing, 20(5): 1259-1269
  14. 14.
    Lu Y C, Li X, Tian Q J and Han W C. 2012. An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory. Optics Express, 20(22): 24496-24504
  15. 15.
    Lu Y C, Li X, Tian Q J, Zheng G, Sun S J, Liu Y X and Yang Q. 2013. Progress in marine oil spill optical remote sensing: detected targets, spectral response characteristics, and theories. Marine Geodesy, 36(3): 334-346
  16. 16.
    Lu Y C, Liu J Q, Ding J, Shi J, Chen J Y and Ye X M. 2019. Optical remote identification of spilled oils from the SANCHI oil tanker collision in the East China Sea. Chinese Science Bulletin, 64(31): 3213-3222
  17. 17.
    Lu Y C, Shi J, Hu C M, Zhang M W, Sun S J and Liu Y X. 2020. Optical interpretation of oil emulsions in the ocean – Part II: applications to multi-band coarse-resolution imagery. Remote Sensing of Environment, 242: 111778
  18. 18.
    Lu Y C, Shi J, Wen Y S, Hu C M, Zhou Y, Sun S J, Zhang M W, Mao Z H and Liu Y X. 2019. Optical interpretation of oil emulsions in the ocean – Part I: laboratory measurements and proof-of-concept with AVIRIS observations. Remote Sensing of Environment, 230: 111183
  19. 19.
    Lu Y C, Sun S J, Zhang M W, Murch B and Hu C M. 2016b. Refinement of the critical angle calculation for the contrast reversal of oil slicks under sunglint. Journal of Geophysical Research: Oceans, 121(1): 148-161
  20. 20.
    Lu Y C, Tian Q J and 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
  21. 21.
    Lu Y C, Tian Q J, Song P F and Li S S. 2009. Study on extraction methods of offshore oil stick by hyperspectral remote sensing. Journal of Remote Sensing, 13(4): 686-695
  22. 22.
    Lu Y C, Tian Q J, Wang J J, Wang X C and Qi X P. 2008. Experimental study on spectral responses of offshore oil slick. Chinese Science Bulletin, 53(24): 3937-3741
  23. 23.
    Lu Y C, Zhan W F and Hu C M. 2016a. Detecting and quantifying oil slick thickness by thermal remote sensing: a ground-based experiment. Remote Sensing of Environment, 181: 207-217
  24. 24.
    Minchew B. 2012. Determining the mixing of oil and sea water using polarimetric synthetic aperture radar. Geophysical Research Letters, 39(16): L16607
  25. 25.
    Niclòs R, Doňa C, Valor E and Bisquert M. 2014. Thermal-infrared spectral and angular characterization of crude oil and seawater emissivities for oil slick identification. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5387-5395
  26. 26.
    Salisbury J W, D’Aria D M and Sabins F F. 1993. Thermal infrared remote sensing of crude oil slicks. Remote Sensing of Environment, 45(2): 225-231
  27. 27.
    Shi J, Jiao J N, Lu Y C, Zhang M W, Mao Z H and Liu Y X. 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
  28. 28.
    Sun S J and Hu C M. 2016. Sun glint requirement for the remote detection of surface oil films. Geophysical Research Letters, 43(1): 309-316
  29. 29.
    Sun S J and 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
  30. 30.
    Sun S J, Hu C M, Feng L, Swayze G A, Holmes J, Graettinger G, MacDonald I, Garcia O and Leifer I. 2016. Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: implications for spatial resolution requirements of remote sensors. Marine Pollution Bulletin, 103(1/2): 276-285
  31. 31.
    Sun S J, Hu C M, Garcia-Pineda O, Kourafalou V, Le Hénaff M and Androulidakis Y. 2018b. Remote sensing assessment of oil spills near a damaged platform in the Gulf of Mexico. Marine Pollution Bulletin, 136: 141-151
  32. 32.
    Sun S J, Hu C M and Thunnell J W. 2015. Surface oil footprint and trajectory of the Ixtoc-I oil spill determined from Landsat/MSS and CZCS observations. Marine Pollution Bulletin, 101(2): 632-641
  33. 33.
    Sun S J, Lu Y C, Liu Y X, Wang M Q and Hu C M. 2018a. Tracking an oil tanker collision and spilled oils in the East China Sea using multisensor day and night satellite imagery. Geophysical Research Letters, 45(7): 3212-3220
  34. 34.
    Wen Y S, Wang M Q, Lu Y C, Sun S J, Zhang M W, Mao Z H, Shi J and 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
  35. 35.
    Wettle M, Daniel P J, Logan G A and 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
  36. 36.
    Zhang B, Perrie W, Li X F and Pichel W G. 2011. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophysical Research Letters, 38(10): L10602
  37. 37.
    Zheng Q, Yan X H, Liu W T, Klemasa V and Sun D C. 2001. Space shuttle observations of open ocean oil slicks. Remote Sensing of Environment, 76(1): 49-56
  38. 38.
    Zhong Z X and You F Q. 2011. Oil spill response planning with consideration of physicochemical evolution of the oil slick: a multiobjective optimization approach. Computers and Chemical Engineering, 35(8): 1614-1630

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