Detecting and quantifying oil slick thickness by thermal remote sensing: A ground-based experiment

Lu, Yingcheng; Zhan, Wenfeng; Hu, Chuanmin

doi:10.1016/j.rse.2016.04.007

Cited by 65 publications

(32 citation statements)

References 53 publications

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“…Furthermore, the emissivity fluctuated considerably around 11.7 µm, 12.55 µm, and 12.8 µm. These positions are also mentioned in the research conducted by Xiong [27] and Shih [18]. According to the literature, the measured emissivity is the combination of emissivity of water and pure oil when the oil film is relatively thin.…”

Section: Emission Spectrummentioning

confidence: 89%

“…The thermal infrared region is used to study soil moisture [24], and it has been proven that the long-wave infrared band is more accurate than visible-near infrared and shortwave infrared for the prediction of soil properties [25]. Some studies have applied thermal infrared technology to fuel-oil measurements, using regression analyses, to test the degree of soil oil pollution [26], and through experiments have identified midday as the optimal detection time for oil slicks [27]. The relationship between the wavelength and emissivity of the oil-film thickness in the range of 0-380 µm was obtained by using the thermal infrared band to detect the oil slick thickness [22].…”

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confidence: 99%

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Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Guo

Liu

2020

JMSE

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Remote sensing is an important method for monitoring marine oil-spill accidents. However, methods for measuring oil-film thickness remain insufficient. Due to the stable differences in the surface emissivity and temperature of oil and water, the oil film can be detected using thermal infrared. This study measured emissivity of seven different oil-film thicknesses and seven different American Petroleum Institute (API) densities, and analyzed the spectral characteristics. Results show an optimal wavelength position for oil-film thickness and fuel API density monitoring is 12.55 µm. Principal component analysis and continuum removal methods were used for data processing. Stepwise multiple linear regression was used to establish relationships between emissivity and oil slick thicknesses and API densities. Oil-film thickness and fuel API density data were analyzed by principal component analysis and continuum removal before regression analysis. The spectral emissivity data was convolved into Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) thermal bands to determine potential of the sensor in oil-film detection. The result shows that neither could be used to estimate thickness. The AVHRR-4 band and band 12 and 13 of the ASTER could be used to separate oils from water and have potential to distinguish different oil types. deficiencies in measuring fuel thickness [12]. Thermal infrared analysis distinguishes oil on the sea surface by thermal comparison. Because the emissivity of water and oil are different, thermal contrast is generated, and the emissivity depends on the thickness of the oil slick [15]. During the daytime, thick oil slicks become warmer than the surrounding water while thinner, detectable slicks appear cooler. This reverses at nighttime [16,17]. A thin film interference theory-based model is used to describe the thickness-dependent contrast between the background water and the sea surface covered by crude oil [18,19]. The thermal infrared emissivity of fuels was measured and analyzed using statistical methods [20][21][22]. At the same time, the different emission spectra of the fuels were detected, and a relationship between the fuel emissivity and the American Petroleum Institute (API) density was obtained.At present, thermal infrared technology is widely used in vegetation and soil detection. Among these [23], the relationship between the emissivity spectrum of the thermal infrared region and the leaf area index is obtained. The thermal infrared region is used to study soil moisture [24], and it has been proven that the long-wave infrared band is more accurate than visible-near infrared and shortwave infrared for the prediction of soil properties [25]. Some studies have applied thermal infrared technology to fuel-oil measurements, using regression analyses, to test the degree of soil oil pollution [26], and through experiments have identified midday as the optimal detection time for oil slicks [27]. The relationship between the...

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Section: Emission Spectrummentioning

confidence: 89%

mentioning

confidence: 99%

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Guo

Liu

2020

JMSE

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“…Traditional hyperspectral oil-slick identification methods focus on detecting thick oil slicks because they contain abundant hydrocarbons, whereas sheens contain much less. Thus, thick oil slicks show greater hydrocarbon spectral characteristics than the surrounding sheens [13,14]. The spectral curves of sheens are very similar to that of seawater.…”

Section: Introductionmentioning

confidence: 91%

Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images

et al. 2018

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Abstract:It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness of oil slicks and cannot detect sheens. Since the spectra of oil slicks should be affected by seawater as well as oil, this paper investigated the use of spectral indices of hydrocarbons and seawater to identify different thicknesses of oil slicks. In this research, a measurement, called index separability (IS), was proposed for quantitatively evaluating the identification ability of these spectral indices. Based on the evaluation results, experiments were conducted to validate the applicability of these spectral indices. The results show that the spectral indices of hydrocarbons are more suitable for detecting continuous true color oil slicks and emulsions and that spectral indices of seawater are more suitable for sheens and seawater. In addition, the spectral indices of hydrocarbons and seawater are complementary for detecting oil slicks. Finally, combining the spectral indices of hydrocarbons and seawater is conducive to achieving more accurate oil slick recognition results.

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“…[], and Lu et al . []), including optical sensors [ Chust and Sagarminaga , ; Giammona et al ., ; Hu et al ., ; Lu et al ., ; Sugioka et al ., ; Sun and Hu , ; Sun et al ., ], synthetic aperture radar (SAR) [ Brekke and Solberg , ; Garcia‐Pineda et al ., ; Hodgins et al ., ; Keramitsoglou et al ., ; Zheng et al ., ], thermal sensors [ Asanuma et al ., ; Cai et al ., ; Cross , ; Innman et al ., ; Leifer et al ., ; Lu et al ., ; Salisbury et al ., ; Tseng and Chiu , ], and laser fluorescence [ Brown et al ., ; Brown and Fingas , ]. These different technologies possess unique characteristics, theoretical bases, related data processing techniques, and quantitative remote sensing models.…”

Section: Introductionmentioning

confidence: 98%

“…Oil slicks on the ocean surface can be detected by employing multiple different types of remote sensing approaches (e.g., see the reviews by Brekke and Solberg [2005], Leifer et al [2012], and Lu et al [2013a]), including optical sensors [Chust and Sagarminaga, 2007;Giammona et al, 1995;Hu et al, 2003Hu et al, , 2009Lu et al, 2011Lu et al, , 2012Lu et al, , 2013aLu et al, , 2013bSugioka et al, 1999;Sun et al, 2015, synthetic aperture radar (SAR) [Brekke and Solberg, 2005;Garcia-Pineda et al, 2013;Hodgins et al, 1996;Keramitsoglou et al, 2006;Zheng et al, 2001], thermal sensors [Asanuma et al, 1986;Cai et al, 2007;Cross, 1992;Innman et al, 2010;Leifer et al, 2012;Lu et al, 2016b;Salisbury et al, 1993;Tseng and Chiu, 1994], and laser fluorescence [Brown et al, 1996;Brown and Fingas, 2003]. These different technologies possess unique characteristics, theoretical bases, related data processing techniques, and quantitative remote sensing models.…”

Section: Introductionmentioning

confidence: 99%

Using remote sensing to detect the polarized sunglint reflected from oil slicks beyond the critical angle

Zhou

Liu

et al. 2017

JGR Oceans

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The critical angle at which the brightness of oil slicks and oil‐free seawater is reversed occurs under sunglint and is often shown as an area of uncertainty due to different roughness and surface Fresnel reflection parameters. Consequently, differentiating oil slicks from the seawater in these areas using optical sensors is a challenge. Polarized optical remote sensing techniques provide complementary information for intensity imagery with different physical properties and, thus, possess the ability to resolve this difficult problem. In the polarized reflectance model, the degree of linear polarization (DOLP) of sunglint depends on accurately knowing the Stokes parameter for the reflected light, and varies with the refractive index of the surface layer and viewing angles. For the polarized detection of oil slicks, the highest sensitivity of the DOLP to the refractive index is located within the specular reflection direction where the sum of the solar and sensor zenith angles is 82.6°. The modeled results clearly indicate that the DOLP of oil slicks is weaker in comparison with oil‐free seawater under sunglint. Using measurements from the space‐borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) over the Deepwater Horizon oil spill in the Gulf of Mexico, we illustrate that the PARASOL‐derived DOLP difference between the oil spill and seawater is obvious and is in accordance with the modeled results. These preliminary results suggest that the potential of multiangle measurement and feasibility of deriving refractive index of ocean surface from space‐borne sensors need further researches.

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Detecting and quantifying oil slick thickness by thermal remote sensing: A ground-based experiment

Cited by 65 publications

References 53 publications

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images

Using remote sensing to detect the polarized sunglint reflected from oil slicks beyond the critical angle

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