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纸质出版日期： 2020-08-07 ，
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沈亚峰,刘建强,丁静,焦俊男,孙绍杰,陆应诚.2020.海洋一号C星光学载荷对海面溢油的识别能力分析.遥感学报,24(8): 933-944
Shen Y F,Liu J Q,Ding J,Jiao J N,Sun S J and Lu Y C. 2020. HY-1C COCTS and CZI observation of marine oil spills in the South China Sea. Journal of Remote Sensing(Chinese)，24(8): 933-944[DOI：10.11834/jrs.20209475]
中国首颗海洋水色业务卫星(HY-1C)搭载有水色水温扫描仪(COCTS)、海岸带成像仪(CZI)、紫外成像仪(UVI)等多套光学载荷，已于2019-06开展业务化运行。溢油是海洋环境监测的重要目标之一。近年来，光学遥感对海面溢油不同污染类型的识别、分类与估算原理得以阐明，其技术优势获得认可，能有效促进海面溢油的精细化定量探测。本研究以中国近海，主要以2019-02-20中国南海东沙岛附近海域一次溢油事件为例，收集了HY-1C星COCTS和CZI数据，并获取了同一天的VIIRS、MODIS Terra & Aqua遥感数据，开展了海面溢油的识别分类及其对比分析，重点评估COCTS与CZI光学载荷对海面溢油识别的效能。结果表明：在弱耀光对比条件下，COCTS、CZI、VIIRS均能有效识别此次东沙岛溢油，且COCTS对溢油的识别能力不弱于VIIRS数据；CZI不仅能识别此次溢油，还能区分此次溢油形成的海面油膜与溢油乳化物，从而提供更多的判定信息；CZI数据中，不同溢油污染类型的光谱信号离散度大于清洁海水平均噪声，展现了溢油量估算的潜力。HY-1C星光学载荷展现了优良的辐射分辨率、高信噪比等性能，随着HY-1D星的发射，双星组网观测还能提供更佳的时相分辨率数据，未来会在海洋溢油的实时、定量、精细化观测中提供丰富的数据支撑。
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
θ
m
) 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.
遥感HY-1C星水色水温扫描仪海岸带成像仪溢油光学遥感
remote sensingHY-1CCOCTSCZIoil spillsoptical remote sensing
Brekke C and Solberg A H S. 2005. Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95(1): 1-13 [DOI: 10.1016/j.rse.2004.11.015http://dx.doi.org/10.1016/j.rse.2004.11.015]
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
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
付玉慧, 李栖筠, 张宝茹. 2008. 海洋溢油光谱分析与卫星信息提取. 遥感学报, 12(6): 1010-1016 [DOI: 10.3321/j.issn:1007-4619.2008.06.026http://dx.doi.org/10.3321/j.issn:1007-4619.2008.06.026]
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 [DOI: 10.5670/oceanog.2013.38http://dx.doi.org/10.5670/oceanog.2013.38]
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 [DOI: 10.1117/1.JRS.12.026008http://dx.doi.org/10.1117/1.JRS.12.026008]
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 [DOI: 10.1016/j.rse.2013.02.012http://dx.doi.org/10.1016/j.rse.2013.02.012]
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 [DOI: 10.1029/2008gl036119http://dx.doi.org/10.1029/2008gl036119]
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 [DOI: 10.1029/2009JC006037http://dx.doi.org/10.1029/2009JC006037]
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 [DOI: 10.3390/s18020641http://dx.doi.org/10.3390/s18020641]
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 [DOI: 10.1126/science.1199697http://dx.doi.org/10.1126/science.1199697]
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 [DOI: 10.1007/s00367-003-0135-0http://dx.doi.org/10.1007/s00367-003-0135-0]
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 [DOI: 10.1016/j.rse.2012.03.024http://dx.doi.org/10.1016/j.rse.2012.03.024]
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
陆应诚, 胡传民, 孙绍杰, 张民伟, 周杨, 石静, 温颜沙. 2016. 海洋溢油与烃渗漏的光学遥感研究进展. 遥感学报, 20(5): 1259-1269 [DOI: 10.11834/jrs.20166122http://dx.doi.org/10.11834/jrs.20166122]
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 [DOI: 10.1364/OE.20.024496http://dx.doi.org/10.1364/OE.20.024496]
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 [DOI: 10.1080/01490419.2013.793633http://dx.doi.org/10.1080/01490419.2013.793633]
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
陆应诚, 刘建强, 丁静, 石静, 陈君颖, 叶小敏. 2019. 中国东海“桑吉”轮溢油污染类型的光学遥感识别. 科学通报, 64(31): 3213-3222
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 [DOI: 10.1016/j.rse.2020.111778http://dx.doi.org/10.1016/j.rse.2020.111778]
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 [DOI: 10.1016/j.rse.2019.05.002http://dx.doi.org/10.1016/j.rse.2019.05.002]
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 [DOI: 10.1002/2015JC011001http://dx.doi.org/10.1002/2015JC011001]
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 [DOI: 10.1007/s11430-010-4154-1http://dx.doi.org/10.1007/s11430-010-4154-1]
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
陆应诚, 田庆久, 宋鹏飞, 李姗姗. 2009. 海面油膜高光谱遥感信息提取. 遥感学报, 13(4): 686-695 [DOI: 10.3321/j.issn:1007-4619.2009.04.011http://dx.doi.org/10.3321/j.issn:1007-4619.2009.04.011]
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 [DOI: 10.1007/s11434-008-0515-yhttp://dx.doi.org/10.1007/s11434-008-0515-y]
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 [DOI: 10.1016/j.rse.2016.04.007http://dx.doi.org/10.1016/j.rse.2016.04.007]
Minchew B. 2012. Determining the mixing of oil and sea water using polarimetric synthetic aperture radar. Geophysical Research Letters, 39(16): L16607 [DOI: 10.1029/2012GL052304http://dx.doi.org/10.1029/2012GL052304]
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 [DOI: 10.1109/TGRS.2013.2288517http://dx.doi.org/10.1109/TGRS.2013.2288517]
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 [DOI: 10.1016/0034-4257(93)90044-xhttp://dx.doi.org/10.1016/0034-4257(93)90044-x]
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 [DOI: 10.1016/j.isprsjprs.2018.09.017http://dx.doi.org/10.1016/j.isprsjprs.2018.09.017]
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 [DOI: 10.1002/2015GL066884http://dx.doi.org/10.1002/2015GL066884]
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 [DOI: 10.1109/TGRS.2018.2876091http://dx.doi.org/10.1109/TGRS.2018.2876091]
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 [DOI: 10.1016/j.marpolbul.2015.12.003http://dx.doi.org/10.1016/j.marpolbul.2015.12.003]
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 [DOI: 10.1016/j.marpolbul.2018.09.004http://dx.doi.org/10.1016/j.marpolbul.2018.09.004]
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 [DOI: 10.1016/j.marpolbul.2015.10.036http://dx.doi.org/10.1016/j.marpolbul.2015.10.036]
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 [DOI: 10.1002/2018GL077433http://dx.doi.org/10.1002/2018GL077433]
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 [DOI: 10.1080/17538947.2018.1470687http://dx.doi.org/10.1080/17538947.2018.1470687]
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 [DOI: 10.1016/j.rse.2009.05.010http://dx.doi.org/10.1016/j.rse.2009.05.010]
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 [DOI: 10.1029/2011GL047013http://dx.doi.org/10.1029/2011GL047013]
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 [DOI: 10.1016/S0034-4257(00)00191-7http://dx.doi.org/10.1016/S0034-4257(00)00191-7]
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 [DOI: 10.1016/j.compchemeng.2011.01.009http://dx.doi.org/10.1016/j.compchemeng.2011.01.009]
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