光谱学与光谱分析 |
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Thermal Infrared Emissivity Spectrum and Its Characteristics of Crude Oil Slick Covered Seawater |
XIONG Pan1,2, GU Xing-fa1, YU Tao1, MENG Qing-yan1, LI Jia-guo1, SHI Ji-xiang4, CHENG Yang3, WANG Liang1, LIU Wen-song3, LIU Qi-yue1, ZHAO Li-min1* |
1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. China University of Petroleum (Huadong), Qingdao 266580, China 4. East China Institute of Technology, Fuzhou 330013, China |
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Abstract Detecting oil slick covered seawater surface using the thermal infrared remote sensing technology exists the advantages such as: oil spill detection with thermal infrared spectrum can be performed in the nighttime which is superior to visible spectrum, the thermal infrared spectrum is superior to detect the radiation characteristics of both the oil slick and the seawater compared to the mid-wavelength infrared spectrum and which have great potential to detect the oil slick thickness. And the emissivity is the ratio of the radiation of an object at a given temperature in normal range of the temperature (260~320 K) and the blackbody radiation under the same temperature., the emissivity of an object is unrelated to the temperature, but only is dependent with the wavelength and material properties. Using the seawater taken from Bohai Bay and crude oil taken from Gudao oil production plant of Shengli Oilfield in Dongying city of Shandong Province, an experiment was designed to study the characteristics and mechanism of thermal infrared emissivity spectrum of artificial crude oil slick covered seawater surface with its thickness. During the experiment, crude oil was continuously dropped into the seawater to generate artificial oil slick with different thicknesses. By adding each drop of crude oil, we measured thereflectivity of the oil slick in the thermal infrared spectrum with the Fourier transform infrared spectrometer (102F) and then calculated its thermal infrared emissivity. The results show that the thermal infrared emissivity of oil slick changes significantly with its thickness when oil slick is relatively thin (20~120 μm), which provides an effective means for detecting the existence of offshore thin oil slick. In the spectrum ranges from 8 to 10 μm and from 13.2 to 14 μm, there is a steady emissivity difference between the seawater and thin oil slickwith thickness of 20 μm. The emissivity of oil slick changes marginally with oil slick thickness and clearly below that of seawater in the spectrum range from 11.7 to 14 μm, this spectrum range can be practically used to distinguish oil slick from seawater; Around the wavelength of 11.72,12.2,12.55,13.48 and 13.8 μm, the emissivity of oil slick presents clearly increasing or decreasing trends with the increase of its thickness, which are one of the best wavelengths for observing the offshore oil slick and estimating its thickness.
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Received: 2014-02-20
Accepted: 2014-05-18
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Corresponding Authors:
ZHAO Li-min
E-mail: zhaolm@irsa.ac.cn
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[1] Fingas M F,Brown C E. Spill Science & Technology Bulletin,1997, 4:199. [2] Jha M N,Levy J,Gao Y. Sensors,2008, 8:236. [3] Fingas M,Brown C. Oil Spill Remote Sensing. In Earth System Monitoring. Springer,2013. 337. [4] Taylor S. Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire,1992. 14. [5] Salisbury J W,D’Aria D M,Sabins F F. Remote Sensing of Environment,1993, 45:225. [6] Lu Y,Tian Q,Li X. Science China Earth Sciences,2011, 54:678. [7] Lu Y,Tian Q,Wang X,et al. International Journal of Digital Earth,2013, 6:76. [8] Lu Y,Tian Q,Song P. Journal of Remote Sensing,2009, 13:691. [9] Lu Y,Chen J,Bao Y. Science China: Information Science,2011, 41:193. [10] Brekke C,Solberg A H S. Remote Sensing of Environment,2005, 95:1. [11] Lu Yingcheng, Li Xiang, Tian Qingjiu, et al. Optics Express, 2012, 20(22): 24496. [12] Lu Yingcheng, Li Xiang, Tian Qingjiu, et al. Marine Geodesy, 2013,36(3): 334. [13] Ingram P M,Muse A H. Geoscience and Remote Sensing, IEEE Transactions on,2001, 39:2158. [14] Pigeat P,Rouxel D,Weber B. Physical Review B,1998, 57:9293. |
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