Abstract

Haiyang-1C (HY-1C) is the first operational ocean color satellite of China, which is intended to obtain daily global ocean color data. The Ultraviolet Imager (UVI) onboard provides a potential novel detector for the detection of marine oil spills. Although airborne UV sensors have shown great efficiency for the detection of spilled oils, the capability of spaceborne UV sensor is not yet clear. In this study, we designed a ground-based experiment to interpret the UV characteristics of various weathered oils, and found that very thin oil films are quite sensitive to the UV radiation due to the surface interference light. Moreover, by comparing spaceborne and airborne UV images of spilled oils collected from HY-1C UVI and AVIRIS, the scale effect of ultraviolet remote sensing has been interpreted clearly. The interference light and sunglint reflection play different roles in the imaging process of spilled oils, leading them to appear radical different features (brighter or darker than the background oil-free seawater) in ground, airborne and spaceborne observation, which deserves further research. Ultraviolet remote sensing, therefore, can work as a new approach and improve the detection and monitoring of marine oil spills.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Haiyang-1C (HY-1C) satellite, launched on 7 September 2018, is the first operational ocean color satellite of China. It is equipped with five sensors, such as Chinese Ocean Color and Temperature Scanner (COCTS), Coastal Zone Imager (CZI), Ultraviolet Imager (UVI), Satellite Calibration Spectrometer (SCS) and Automatic Identification System (AIS). Particularly, the UVI onboard provides the only data for marine environment observation at 355 and 385 nm right now. These ultraviolet (UV) data will provide a broad future of application to oceanic research, including atmospheric correction of nearshore turbid water [1,2], estimating dissolved organic matter in water [3,4] and monitoring sea surface anomalies, such as marine oil spills [5].

Marine oil spills represent a typical environmental disaster that harms the marine and coastal environment [6]. In the process of weathering, various weathered oils with different visual characteristics can form, including non-emulsified oil slicks, water-in-oil (WO) emulsions and oil-in-water (OW) emulsions [79]. How to detect oil slicks with optical sensors is still a challenge due to the weak reflecting light signal. However, oil slicks show high reflectivity of UV radiation, which is a remarkable characteristic compared with other bands [10]. Some studies have verified the feasibility of using the UV sensors to detect spilled oils on airborne platforms. Oils appear brighter than background seawater in these UV images [11], and the oil-covered areas in the UV band are larger than those in the visible and infrared bands, as the UV band is more capable of detecting very thin oil slicks [12].

Apart from ultraviolet remote sensing, various remote sensing technologies have been applied to detecting and monitoring marine oil spills. Among these synthetic aperture radar (SAR) and optical remote sensing are the most-frequently used ones. SAR provides data under all-weather conditions, in which the spilled oils on the ocean surface reduce Bragg scattering and show negative contrast from surrounding oil-free seawater, but it can hardly be used to classify and quantify the different weathered oils [13,14]. Passive optical remote sensing is another popular tool as it is able to classify different weathered oils and quantify the thickness, concentration, or volume of them [79,1517]. Note that oils in optical images would appear either darker (negative contrast) or brighter (positive contrast) than oil-free seawater under different sunglint conditions [1719]. Besides, thermal remote sensing and laser fluorescence have also been used to detect spilled oils [20,21]. So far airborne ultraviolet remote sensing has shown great effectiveness for oil spill detection, but whether spilled oils can be detected and what characteristics would oils appear in spaceborne UV images remain unknown due to the lack of spaceborne UV sensor. Fortunately, the HY-1C UVI detected one oil spill incident near Indonesia on 20 April 2020, which was the first time that marine oil spills had been clearly observed in UV satellite images, providing valuable UV satellite data to fill these knowledge gaps.

Therefore, this work is intended to verify the ability of ultraviolet remote sensing to detect marine oil spills, especially in the UV characteristics of various weathered oils based on a ground-based experiment, and the features oils appear in UV images obtained from spaceborne and airborne sensors. Ultraviolet remote sensing represents a novel approach for marine environment monitoring. This work could provide new reference for using the UV sensors to detect other sea surface targets, even for the UV sensors equipped on the next generation of ocean color satellite of China.

2. Data and methods

2.1 Spaceborne and airborne images

2.1.1 HY-1C UVI images and other validation data

HY-1C is operating in a sun-synchronous orbit with a mean altitude of 782 km and a mean local solar time of 10:30 at the descending node. The UVI onboard collects data in a push-broom mode and covers a swath of ∼2900 km, ensuring a global coverage every day. The designed data spatial resolution at the subastral point is ∼550 m in full transmission mode and ∼1100 m in merged transmission mode. More parameters of the UVI are listed in Table  1.

Tables Icon

Table 1. Parameters of the HY-1C UVI.

The HY-1C detected one marine oil spill incident near Indonesia on 20 April 2020. The oil spills can be detected on both HY-1C CZI and UVI images. In this study, several UVI images (of spectral radiance, unit: mW·cm−2·µm−1·Sr−1, as the atmospheric correction of the UV band is not yet clear) were used to conduct analysis, and the CZI image (of Rayleigh-corrected reflectance, Rrc, dimensionless) covering the same area synchronously was used as proof. Based on the spectral response characteristics (Fig.  1(a), inset) and sunglint assessment of marine spilled oils, the CZI image detected both the oil slicks and oil emulsions (Fig.  1(a)). While in the UVI images, only oil slicks were detected.

 figure: Fig. 1.

Fig. 1. (a) HY-1C CZI true color RGB image (R: 650 nm, G: 560 nm, B: 460 nm) on 20 Apr 2020 (03:33 UTC), covering the spilled oils near Indonesia, part of it is enlarged and enhanced in the inset image. Seawater, oil slicks and oil emulsions are annotated with blue, green and red crosses, with their respective Rrc spectra shown in another inset image; (b) AVIRIS true color RGB image (R: 638.2 nm, G: 550.3 nm, B: 472.5 nm) on 17 May 2010 (Run 10, 20:12 UTC), covering the DWH oil spill (the background image was collected from MODIS Terra on the same day). Two regions of the AVIRIS image are selected and enlarged in the inset images. The DWH platform is marked with a black cross. Region I was ∼75 km from the platform, where oils formed large areas of oil slicks and a few oil emulsions. Region II was ∼32 km from the platform, where oils formed large areas of oil emulsions.

Download Full Size | PPT Slide | PDF

2.1.2 AVIRIS images

During the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico (GoM) in 2010, the airborne visible infrared imaging spectrometer (AVIRIS) onboard the ER-2 aircraft was used to collect hyperspectral images of the spilled oils [68,22]. The instrument collects data covering the wavelength range from 350 to 2500 nm in 224 channels with an average spectral resolution of 10 nm. Specifically, it has a UV band with central wavelength at 380.21 nm (band 2). In this study, one AVIRIS image obtained on 17 May 2010 (Run 10) was used to characterize oil slicks and various oil emulsions (Fig.  1(b)). The spatial resolution of AVIRIS data is ∼7.6 m at an altitude of 8500 m. The AVIRIS data was processed into reflectance (R, dimensionless) using atmospheric correction algorithm designed for hyperspectral airborne sensors [22].

2.2 Ground-based experiment

The experiment was conducted outdoors using the experimental settings illustrated in Fig.  2(a) and described below. The crude oil (i.e., Yiyang oil) produced in China, which was similar to the DWH oil spill in the GoM, was used for sample preparation, including WO emulsions with a concentration of 75%, OW emulsions with a concentration of 1%, thin oil slicks and thick oil slicks. Details on sample preparation have been illustrated by Lu [8]. One black plastic tank was used to contain clear water, and four black plastic beakers were used to contain the samples. All the containers were sprayed with black paint to reduce interference caused by reflection from the bottom and sides. The liquid level in the beakers was level with that in the tank. In order to reduce the influence of surface solar reflection, all samples were placed away from the solar specular reflection point.

 figure: Fig. 2.

Fig. 2. (a) Experimental setup for obtaining the UV image and reflectance spectra of different oil samples, the camera was set up 1.8 m above the surface, and the four beakers were within its field of view.; (b) Absolute quantum efficiency of the CCD camera as a function of wavelength; (c) and (d) Transmission and optical density (OD = lg(1/Transmission)) spectra of the single-band bandpass filter (central wavelength: 385 nm) (more information of the filter can be found on https://www.semrock.com/FilterDetails.aspx?id=FF01-385/26-25).

Download Full Size | PPT Slide | PDF

A CCD camera (Basler piA1600-35gm GigE) equipped with a single-band bandpass filter (Semrock FF01-385/26-25, with central wavelength at 385 nm) was used to obtain the UV image of the samples. The data was stored in the form of DN values, and then processed into grayscale values to characterize UV brightness. Note that oils would exhibit fluorescence characteristics upon exposure to UV radiation and the fluorescence response is in the range of 400 to 650 nm with peak centering at approximately 480 nm [20,21]. The filter used in this experiment can effectively reduce this influence. The observation was conducted outdoors at 12:00 local time on 13 December 2019 (windless and cloudless) and the sun zenith angle was approximately 55°. The reflectance spectra were measured by an ASD spectroradiometer (FieldSpec-FR, 350–2500 nm with a spectral resolution of 3.5 nm and filed of view of 25°).

3. Results and discussion

3.1 Experiment-derived ultraviolet signal difference of various weathered oils

The experimental data contained the reflectance spectra (dimensionless, Fig.  3(b)) and one UV image (Fig.  3(c)) of the samples. Note that there were thin oil films floating on the surface of OW emulsions. As shown in Fig.  3(c), thin oil films appeared brighter than either OW emulsions or the background water. Light interference theory can be used to describe the behavior of the incident UV light in the air-oil-water boundary [15,23]. Specifically, it is the multi-beam interference effect on the surface of thin oil films that increases their reflectance and makes them easily detected in the UV band [23]. Thin oil slicks on calm water can be regarded as optical thin films with homogeneous thickness and refractive index. The absorption and scattering of the incident UV light can be neglected due to the very low thickness, in fact, it experiences multiple reflection and refraction inside and ends as a series of parallel emergent light, consequently increasing the reflectance (Fig.  3(e)). However, this optical process mainly acts on the surface of thin oil films, so it can hardly be used for quantifying the thickness and volume of spilled oils.

 figure: Fig. 3.

Fig. 3. (a) The photograph of the samples. The shade is separated by white dotted lines; (b) Reflectance spectra of the samples. The spectral characteristics of “-OH” and “-CH” bonds can be seen clearly in the WO and OW emulsions, the wavelength ranges of the atmospheric absorption windows are indicated by gray bars [7,8]; (c) The experimentally obtained UV image of the samples (central wavelength: 385 nm). Regions of interest were delineated by red rectangles; (d) Histogram showing the mean normalized grayscale value of each sample in the UV image; (e) Schematic graph showing the multi-beam interference effect of oil slick. F0, Lg and Loil refer to the solar irradiance, sunglint reflection and interference light, respectively; (f) Classification maps using the statistical results in (d) to the five sub-regions in (c). Each sample is indicated by a small black cross and numbers corresponding to those in (d).

Download Full Size | PPT Slide | PDF

To further interpret the UV signal difference among various weathered oils, we obtained the mean grayscale values of the samples. Results are converted into a normalized histogram shown in Fig.  3(d), indicating that in the UV image: (1) all of the oils appear brighter than the background water; (2) the UV band of 385 nm can hardly be used to classify oil types as thick oil slicks and WO emulsions are close in UV brightness; (3) the UV brightness of oil slicks reduces with the increase of the thickness as the absorption effect of oil can no longer be neglected; and (4) thin oil films on water and those on OW emulsions vary in UV brightness though the multi-beam interference effect acts on both their surfaces, which is due to the difference in the refractive indexes of water and OW emulsions underneath.

3.2 Ultraviolet detection of spilled oils in HY-1C UVI images

Sunglint reflection plays an important role in the features of spilled oils (brighter or darker than the background oil-free seawater) in UV satellite images. Constrained by the high orbit height and coarse spatial resolution (≥ 550 m), the HY-1C UVI cannot discriminate the interference light on the surface of oil slicks (Loil in Fig.  3(e)). Therefore, it is the surface sunglint reflection that determines the feature of spilled oils in HY-1C UVI images (Lg in Fig.  3(e)). To evaluate it, the most effective indicator is θm defined as the angle between the viewing direction and the direction of mirror reflection [17]. The θm value of the oil spill on 20 April 2020 was 22°, which is larger than the potential critical angle range (θm is between 12° and 13° [19]). Therefore, the spilled oils appeared darker than the background oil-free seawater under weak sunglint (Figs.  4(a) and 4(b)) [16,18,19], which is different from the ground-based experiment (positive contrast). This can be proved by the spectral radiance data extracted along the artificial profile line (Figs.  4(c) and 4(d)). Besides, the spectral radiance difference between the spilled oils and the surrounding oil-free seawater implied that the UV band of 385 nm would be more practical for oil spill detection as the spectral radiance difference at 385 nm is larger than that at 355 nm. Of course, oil slicks and oil-free seawater could show positive contrast in some spaceborne UV images under strong sunglint, not caused by the interference light.

3.3 Proof of spilled oils in airborne ultraviolet image

The AVIRIS image of the DWH oil spill on 17 May 2010 was employed as proof of the UV features of oils, as it has a UV band of 380.21 nm and high spatial resolution (∼7.6 m), and was collected at a geometry avoiding sunglint during the flight. False color RGB images (R: 1672 nm, G: 831.5 nm, B: 647.8 nm) of the two regions in Fig.  1(b) are effective in distinguishing WO and OW emulsions as they show reddish and greenish color features, respectively (Figs.  5(a) and 5(c)) [9]. All these images suggest that the spilled oils formed large area of thin oil slicks and a few WO emulsions in region I, and large area of OW emulsions and a few WO emulsions in region II. Their corresponding UV images are shown in Figs.  5(b) and 5(d). Following the method illustrated by Shi et al. (2018) [7] and Lu et al. (2019, 2020) [8,9], various weathered oils are classified based on the spectral absorption peaks of “-CH” and “-OH” bonds (annotated with black arrows in Figs.  5(i) and 5(j)), and the classification results are shown in Figs.  5(e) to 5(h).

 figure: Fig. 4.

Fig. 4. (a) and (b) HY-1C UVI images covering the oil spill in Fig.  2(a), where only oil slicks can be detected. The θm value of the spilled oil is calculated and annotated (cosθm = cosθcosθ0 - sinθsinθ0cosφ, where θ, θ0 and φ refer to the sensor zenith, solar zenith and relative azimuth angles, respectively and all of them can be derived from the UVI data). Classification map of oil slicks and oil emulsions using the CZI data is in the inset image in (a); (c) and (d) The spectral radiance values (Lt) along the profile line in (a) and (b). “Rd” refers to the spectral radiance difference between the oil slicks and the surrounding oil-free seawater.

Download Full Size | PPT Slide | PDF

 figure: Fig. 5.

Fig. 5. (a) and (c) AVIRIS false color RGB image (R: 1672 nm, G: 831.5 nm, B: 647.8 nm) of the two regions in Fig.  1(b), where oil-free seawater, oil slicks, WO and OW emulsions are annotated with blue, black, red and green points, respectively. Their corresponding AVIRIS reflectance spectra are presented in (i) and (j), where the spectral characteristics of “-OH” and “-CH” can be seen clearly in the WO and OW emulsions [7,8]; (b) and (d) AVIRIS UV images (central wavelength: 380.21 nm) of the two regions; (e) to (h) Classification maps of oil slicks and oil emulsions corresponding to (a) to (d); (k) and (l) The normalized reflectance values extracted along the two profile lines at 380.21 nm, 657.8/773.1 nm and 1632 nm. Seawater, oil slicks, WO and OW emulsions are indicated corresponding with the targets annotated with the squares in Figs.  5(a) and 5(c), respectively.

Download Full Size | PPT Slide | PDF

In region I, the area of the oil slicks detected in the UV band is much larger than that in the visible and infrared wavelengths as very thin oil slicks are quite sensitive to UV radiation, but the WO emulsions can hardly be distinguished from oil slicks in this band. In region II, only part of the oil emulsions was detected in the UV image. The OW emulsions showed nearly no response as there were no oil films floating on the surface of oil emulsions. Besides, both of the oil slicks and WO emulsions appeared brighter than the oil-free seawater. It is further noted that the AVIRIS reflectance values of 380.21 nm, 657.8 nm, 773.1 nm and 1632 nm extracted along the two profile lines in Figs.  5(e) and 5(g) show spectral response consistent with the spatial distribution of different weathered oils and oil-free seawater as illustrated in section 3.1 and demonstrated by Lu et al. (2019, 2020) [8,9].

The ground-based experiment indicated that the UV band can hardly be used to classify oil types. To verify this, intersection of the spilled area detected by the UV band (380.21 nm) and the short-wave infrared (SWIR) band (1632 nm) of AVIRIS was selected, as the reflectance of different weathered oils varies in the SWIR band, thus being effective in classifying oil types. Note that the two bands selected have the same spatial resolution (∼7.6 m). As shown in Fig.  6, for each reflectance (R) interval in the SWIR band, different oil types can be classified, but the result cannot match with that in the UV band, further confirming that UV bands can hardly be used to classify oil types even at high spatial resolution.

 figure: Fig. 6.

Fig. 6. Comparison of oil spill detection between the UV (380.21 nm) and SWIR (1632 nm) band of AVIRIS for region I (oil slicks and WO emulsions) and region II (WO and OW emulsions) shown in Fig.  1(b).

Download Full Size | PPT Slide | PDF

4. Conclusion

In order to illuminate the ability of HY-1C UVI, a novel spaceborne sensor, to detect marine oil spills, this study carried out a carefully designed experiment and presented the preliminary results to interpret the UV characteristics of various weathered oils. These results indicate that marine spilled oils, especially thin oil slicks, have significant UV features due to the surface interference light, but the various weathered oils can hardly be classified using the UV bands alone. Particularly, interference light and sunglint reflection play different roles in airborne and spaceborne ultraviolet remote sensing of marine oil spills, leading spilled oils to appear radical different features in ground, airborne and spaceborne observation, which can be attributed to the scale effect of remote sensing. When the sunglint reflection is weak and the spatial resolution is high enough to discriminate the interference light, it is the enhancement-refection effect of interference light that determines the UV features of oil slicks. That is why the oil slicks and oil-free seawater show positive contrast in ground and airborne UV images. While in the HY-1C UVI images whose spatial resolution is coarse, the slight difference caused by the interference light cannot be discriminated. The sunglint reflectance of oil slicks is lower than that of oil-free seawater under weak sunglint, therefore appear negative contrast in UV images.

Although some limitations still exist, such as the lack of high-performance spaceborne UV sensors and the unclarity of atmospheric correction of the UV bands, the feasibility and potential of ultraviolet remote sensing in the monitoring and detection of marine oil spills have been demonstrated by these preliminary results. In the future, with the next generation of ocean color satellite sensors including UV bands, such as Geostationary Ocean Color Imager-II and the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission, it will be a promising tool and can complement the use of SAR, optical and thermal remote sensing in this field. Last but not least, this study could provide reference for using the short-wavelength electromagnetic radiation to detect other sea surface targets.

Funding

National Natural Science Foundation of China (42071387, 41771376); The Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (GML2019ZD0302).

Acknowledgments

We thank NSOAS (http://www.nsoas.org.cn/) and NASA (https://aviris.jpl.nasa.gov/alt_locator/) for providing HY-1C and AVIRIS data.

Disclosures

The authors declare no conflicts of interest.

References

1. X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754–20770 (2012). [CrossRef]  

2. R. Bai, X. He, Y. Bai, T. Li, and F. Gong, “Characteristics of water leaving reflectance at ultraviolet wavelengths: radiative transfer simulations,” Opt. Express 28(20), 29714–29729 (2020). [CrossRef]  

3. H. De Haan, “Solar UV-light penetration and photodegradation of humic substances in peaty lake water,” Limnol. Oceanogr. 38(5), 1072–1076 (1993). [CrossRef]  

4. J. Peuravuori and K. Pihlaja, “Molecular size distribution and spectroscopic properties of aquatic humic substances,” Anal. Chim. Acta. 337(2), 133–149 (1997). [CrossRef]  

5. D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

6. I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012). [CrossRef]  

7. J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018). [CrossRef]  

8. Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019). [CrossRef]  

9. Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020). [CrossRef]  

10. S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010). [CrossRef]  

11. C. E. Catoe and F. L. Orthlieb, “Remote sensing of oil spills,” Remote Sensing 11(23), 2762 (2019). [CrossRef]  

12. P. Wagner, T. Hengstermann, and O. Zielinski, “MEDUSA: An airborne multispectral oil spill detection and characterization system,” Proc. SPIE 4130, 610–620 (2000). [CrossRef]  

13. O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013). [CrossRef]  

14. C. Brekke and A. H. S. Solberg, “Oil spill detection by satellite remote sensing,” Remote Sens. Environ. 95(1), 1–13 (2005). [CrossRef]  

15. Y. Lu, X. Li, Q. Tian, and W. Han, “An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory,” Opt. Express 20(22), 24496–24504 (2012). [CrossRef]  

16. Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016). [CrossRef]  

17. C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009). [CrossRef]  

18. C. R. Jackson and W. Alpers, “The role of the critical angle in brightness reversals on sunglint images of the sea surface,” J. Geophys. Res. 115(C9), C09019 (2010). [CrossRef]  

19. Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018). [CrossRef]  

20. M. F. Fingas and C. E. Brown, “A review of oil spill remote sensing,” Sensors 18(2), 91 (2017). [CrossRef]  

21. C. E. Brown and M. F. Fingas, “Review of the development of laser fluorosensors for oil spill application,” Mar. Pollut. Bull. 47(9-12), 477–484 (2003). [CrossRef]  

22. R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

23. Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009). [CrossRef]  

References

  • View by:
  • |
  • |
  • |

  1. X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754–20770 (2012).
    [Crossref]
  2. R. Bai, X. He, Y. Bai, T. Li, and F. Gong, “Characteristics of water leaving reflectance at ultraviolet wavelengths: radiative transfer simulations,” Opt. Express 28(20), 29714–29729 (2020).
    [Crossref]
  3. H. De Haan, “Solar UV-light penetration and photodegradation of humic substances in peaty lake water,” Limnol. Oceanogr. 38(5), 1072–1076 (1993).
    [Crossref]
  4. J. Peuravuori and K. Pihlaja, “Molecular size distribution and spectroscopic properties of aquatic humic substances,” Anal. Chim. Acta. 337(2), 133–149 (1997).
    [Crossref]
  5. D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).
  6. I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
    [Crossref]
  7. J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
    [Crossref]
  8. Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
    [Crossref]
  9. Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
    [Crossref]
  10. S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
    [Crossref]
  11. C. E. Catoe and F. L. Orthlieb, “Remote sensing of oil spills,” Remote Sensing 11(23), 2762 (2019).
    [Crossref]
  12. P. Wagner, T. Hengstermann, and O. Zielinski, “MEDUSA: An airborne multispectral oil spill detection and characterization system,” Proc. SPIE 4130, 610–620 (2000).
    [Crossref]
  13. O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
    [Crossref]
  14. C. Brekke and A. H. S. Solberg, “Oil spill detection by satellite remote sensing,” Remote Sens. Environ. 95(1), 1–13 (2005).
    [Crossref]
  15. Y. Lu, X. Li, Q. Tian, and W. Han, “An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory,” Opt. Express 20(22), 24496–24504 (2012).
    [Crossref]
  16. Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
    [Crossref]
  17. C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009).
    [Crossref]
  18. C. R. Jackson and W. Alpers, “The role of the critical angle in brightness reversals on sunglint images of the sea surface,” J. Geophys. Res. 115(C9), C09019 (2010).
    [Crossref]
  19. Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
    [Crossref]
  20. M. F. Fingas and C. E. Brown, “A review of oil spill remote sensing,” Sensors 18(2), 91 (2017).
    [Crossref]
  21. C. E. Brown and M. F. Fingas, “Review of the development of laser fluorosensors for oil spill application,” Mar. Pollut. Bull. 47(9-12), 477–484 (2003).
    [Crossref]
  22. R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).
  23. Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
    [Crossref]

2020 (2)

R. Bai, X. He, Y. Bai, T. Li, and F. Gong, “Characteristics of water leaving reflectance at ultraviolet wavelengths: radiative transfer simulations,” Opt. Express 28(20), 29714–29729 (2020).
[Crossref]

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

2019 (2)

C. E. Catoe and F. L. Orthlieb, “Remote sensing of oil spills,” Remote Sensing 11(23), 2762 (2019).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

2018 (2)

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

2017 (1)

M. F. Fingas and C. E. Brown, “A review of oil spill remote sensing,” Sensors 18(2), 91 (2017).
[Crossref]

2016 (1)

Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
[Crossref]

2013 (1)

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

2012 (3)

Y. Lu, X. Li, Q. Tian, and W. Han, “An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory,” Opt. Express 20(22), 24496–24504 (2012).
[Crossref]

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754–20770 (2012).
[Crossref]

2010 (2)

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

C. R. Jackson and W. Alpers, “The role of the critical angle in brightness reversals on sunglint images of the sea surface,” J. Geophys. Res. 115(C9), C09019 (2010).
[Crossref]

2009 (2)

Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
[Crossref]

C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009).
[Crossref]

2005 (1)

C. Brekke and A. H. S. Solberg, “Oil spill detection by satellite remote sensing,” Remote Sens. Environ. 95(1), 1–13 (2005).
[Crossref]

2003 (1)

C. E. Brown and M. F. Fingas, “Review of the development of laser fluorosensors for oil spill application,” Mar. Pollut. Bull. 47(9-12), 477–484 (2003).
[Crossref]

2000 (1)

P. Wagner, T. Hengstermann, and O. Zielinski, “MEDUSA: An airborne multispectral oil spill detection and characterization system,” Proc. SPIE 4130, 610–620 (2000).
[Crossref]

1997 (1)

J. Peuravuori and K. Pihlaja, “Molecular size distribution and spectroscopic properties of aquatic humic substances,” Anal. Chim. Acta. 337(2), 133–149 (1997).
[Crossref]

1993 (1)

H. De Haan, “Solar UV-light penetration and photodegradation of humic substances in peaty lake water,” Limnol. Oceanogr. 38(5), 1072–1076 (1993).
[Crossref]

Alpers, W.

C. R. Jackson and W. Alpers, “The role of the critical angle in brightness reversals on sunglint images of the sea surface,” J. Geophys. Res. 115(C9), C09019 (2010).
[Crossref]

Bai, R.

Bai, Y.

Beatty, D. S.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Bostater, C. R.

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Bradley, E.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Brekke, C.

C. Brekke and A. H. S. Solberg, “Oil spill detection by satellite remote sensing,” Remote Sens. Environ. 95(1), 1–13 (2005).
[Crossref]

Brown, C. E.

M. F. Fingas and C. E. Brown, “A review of oil spill remote sensing,” Sensors 18(2), 91 (2017).
[Crossref]

C. E. Brown and M. F. Fingas, “Review of the development of laser fluorosensors for oil spill application,” Mar. Pollut. Bull. 47(9-12), 477–484 (2003).
[Crossref]

Catoe, C. E.

C. E. Catoe and F. L. Orthlieb, “Remote sensing of oil spills,” Remote Sensing 11(23), 2762 (2019).
[Crossref]

Clark, R.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Clark, R. N.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

De Haan, H.

H. De Haan, “Solar UV-light penetration and photodegradation of humic substances in peaty lake water,” Limnol. Oceanogr. 38(5), 1072–1076 (1993).
[Crossref]

Dennison, P.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Dominguez, R. N.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Dukhovskoy, D.

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

Eastwood, M.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Fang, S.

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

Feng, Q.

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Feng, X.

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

Fingas, M. F.

M. F. Fingas and C. E. Brown, “A review of oil spill remote sensing,” Sensors 18(2), 91 (2017).
[Crossref]

C. E. Brown and M. F. Fingas, “Review of the development of laser fluorosensors for oil spill application,” Mar. Pollut. Bull. 47(9-12), 477–484 (2003).
[Crossref]

Garcia-Pineda O, O.

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

Gong, F.

Green, R. O.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Han, W.

He, X.

Hengstermann, T.

P. Wagner, T. Hengstermann, and O. Zielinski, “MEDUSA: An airborne multispectral oil spill detection and characterization system,” Proc. SPIE 4130, 610–620 (2000).
[Crossref]

Hess, M.

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

Hoefen, T.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Holt, B.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Hu, C.

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
[Crossref]

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009).
[Crossref]

Hu, Y.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Huang, X.

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Jackson, C. R.

C. R. Jackson and W. Alpers, “The role of the critical angle in brightness reversals on sunglint images of the sea surface,” J. Geophys. Res. 115(C9), C09019 (2010).
[Crossref]

Jiao, J.

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

Jing, S.

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Jones, C. E.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Kokaly, R.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Lehr, W. J.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Leifer, I.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Li, T.

Li, X.

Y. Lu, X. Li, Q. Tian, and W. Han, “An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory,” Opt. Express 20(22), 24496–24504 (2012).
[Crossref]

C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009).
[Crossref]

Liu, Y.

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Livo, K. E.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Lu, Y.

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
[Crossref]

Y. Lu, X. Li, Q. Tian, and W. Han, “An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory,” Opt. Express 20(22), 24496–24504 (2012).
[Crossref]

Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
[Crossref]

Lundeen, S.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

MacDonald, I.

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

Mao, Z.

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Matheson, S.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

McCubbin, I.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Mertikas, S. P.

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Morey, S. L.

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

Muller-Karger, F. E.

C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009).
[Crossref]

Murch, B.

Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
[Crossref]

Neyt, X.

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Orthlieb, F. L.

C. E. Catoe and F. L. Orthlieb, “Remote sensing of oil spills,” Remote Sensing 11(23), 2762 (2019).
[Crossref]

Pan, D.

Peuravuori, J.

J. Peuravuori and K. Pihlaja, “Molecular size distribution and spectroscopic properties of aquatic humic substances,” Anal. Chim. Acta. 337(2), 133–149 (1997).
[Crossref]

Pichel, W. G.

C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009).
[Crossref]

Pihlaja, K.

J. Peuravuori and K. Pihlaja, “Molecular size distribution and spectroscopic properties of aquatic humic substances,” Anal. Chim. Acta. 337(2), 133–149 (1997).
[Crossref]

Qi, X.

Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
[Crossref]

Qian, W.

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Reif, M.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Roberts, D.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Roberts, D. A.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Ryan, T.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Sarture, C.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Shi, J.

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

Solberg, A. H. S.

C. Brekke and A. H. S. Solberg, “Oil spill detection by satellite remote sensing,” Remote Sens. Environ. 95(1), 1–13 (2005).
[Crossref]

Steele, D.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Sun, S.

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
[Crossref]

Svejkovsky, J.

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Swayze, G.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Swayze, G. A.

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

Tang, J.

Tian, Q.

Y. Lu, X. Li, Q. Tian, and W. Han, “An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory,” Opt. Express 20(22), 24496–24504 (2012).
[Crossref]

Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
[Crossref]

Velez-Reyes, M.

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Wagner, P.

P. Wagner, T. Hengstermann, and O. Zielinski, “MEDUSA: An airborne multispectral oil spill detection and characterization system,” Proc. SPIE 4130, 610–620 (2000).
[Crossref]

Wang, D.

Wang, J.

Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
[Crossref]

Wang, M.

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Wang, X.

Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
[Crossref]

Wen, Y.

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Wozencraft, J.

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Xu, C.

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

Yin, D.

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Zhang, M.

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
[Crossref]

Zhou, Y.

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

Zielinski, O.

P. Wagner, T. Hengstermann, and O. Zielinski, “MEDUSA: An airborne multispectral oil spill detection and characterization system,” Proc. SPIE 4130, 610–620 (2000).
[Crossref]

Anal. Chim. Acta. (1)

J. Peuravuori and K. Pihlaja, “Molecular size distribution and spectroscopic properties of aquatic humic substances,” Anal. Chim. Acta. 337(2), 133–149 (1997).
[Crossref]

Geophys. Res. Lett. (1)

C. Hu, X. Li, W. G. Pichel, and F. E. Muller-Karger, “Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery,” Geophys. Res. Lett. 36(1), L01604 (2009).
[Crossref]

Int. J. Digit. Earth (1)

Y. Wen, M. Wang, Y. Lu, S. Sun, M. Zhang, Z. Mao, S. Jing, and Y. Liu, “An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint,” Int. J. Digit. Earth 11(9), 972–979 (2018).
[Crossref]

ISPRS J Photogramm. (1)

J. Shi, J. Jiao, Y. Lu, M. Zhang, Z. Mao, and Y. Liu, “Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,” ISPRS J Photogramm. 146, 251–259 (2018).
[Crossref]

J. Geophys. Res. (1)

C. R. Jackson and W. Alpers, “The role of the critical angle in brightness reversals on sunglint images of the sea surface,” J. Geophys. Res. 115(C9), C09019 (2010).
[Crossref]

J. Geophys. Res. Oceans (1)

Y. Lu, S. Sun, M. Zhang, B. Murch, and C. Hu, “Refinement of the critical angle calculation for the contrast of oil slicks under sunglint,” J. Geophys. Res. Oceans 121(1), 148–161 (2016).
[Crossref]

Journal of Spectroscopy and Spectral Analysis (2)

Y. Lu, Q. Tian, X. Qi, J. Wang, and X. Wang, “Spectral response analysis of offshore thin oil slicks,” Journal of Spectroscopy and Spectral Analysis 29(4), 986–989 (2009).
[Crossref]

S. Fang, X. Huang, D. Yin, C. Xu, X. Feng, and Q. Feng, “Research of the ultraviolet reflectivity characteristic of simulative targets of oil spill on the ocean,” Journal of Spectroscopy and Spectral Analysis 30(3), 738–742 (2010).
[Crossref]

Limnol. Oceanogr. (1)

H. De Haan, “Solar UV-light penetration and photodegradation of humic substances in peaty lake water,” Limnol. Oceanogr. 38(5), 1072–1076 (1993).
[Crossref]

Mar. Pollut. Bull. (1)

C. E. Brown and M. F. Fingas, “Review of the development of laser fluorosensors for oil spill application,” Mar. Pollut. Bull. 47(9-12), 477–484 (2003).
[Crossref]

Oceanography (1)

O. Garcia-Pineda O, I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S. L. Morey, “Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar,” Oceanography 26(2), 124–137 (2013).
[Crossref]

Opt. Express (3)

Proc. SPIE (1)

P. Wagner, T. Hengstermann, and O. Zielinski, “MEDUSA: An airborne multispectral oil spill detection and characterization system,” Proc. SPIE 4130, 610–620 (2000).
[Crossref]

Remote Sens Environ. (3)

I. Leifer, W. J. Lehr, D. S. Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C. E. Jones, B. Holt, M. Reif, D. A. Roberts, J. Svejkovsky, G. Swayze, and J. Wozencraft, “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens Environ. 124(9), 185–209 (2012).
[Crossref]

Y. Lu, J. Shi, Y. Wen, C. Hu, Y. Zhou, S. Sun, M. Zhang, Z. Mao, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part I: Laboratory measurements and proof-of-concept with AVIRIS observations,” Remote Sens Environ. 230, 111183 (2019).
[Crossref]

Y. Lu, J. Shi, C. Hu, M. Zhang, S. Sun, and Y. Liu, “Optical interpretation of oil emulsions in the ocean - Part II: Applications to multi-band coarse-resolution imagery,” Remote Sens Environ. 242, 111778 (2020).
[Crossref]

Remote Sens. Environ. (1)

C. Brekke and A. H. S. Solberg, “Oil spill detection by satellite remote sensing,” Remote Sens. Environ. 95(1), 1–13 (2005).
[Crossref]

Remote Sensing (1)

C. E. Catoe and F. L. Orthlieb, “Remote sensing of oil spills,” Remote Sensing 11(23), 2762 (2019).
[Crossref]

Sensors (1)

M. F. Fingas and C. E. Brown, “A review of oil spill remote sensing,” Sensors 18(2), 91 (2017).
[Crossref]

Other (2)

R. N. Clark, G. A. Swayze, I. Leifer, K. E. Livo, S. Lundeen, M. Eastwood, R. O. Green, R. Kokaly, T. Hoefen, C. Sarture, I. McCubbin, D. Roberts, D. Steele, T. Ryan, and R. N. Dominguez, “Pearson, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. “A method for qualitative mapping of thick oil spills using imaging spectroscopy,” in U.S. Geological Survey Open-File Report 1167, 1–151 (2010).

D. Yin, X. Huang, W. Qian, Q. Feng, C. R. Bostater, S. P. Mertikas, X. Neyt, and M. Velez-Reyes, “Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance,” Proc. SPIE, 78250I (7825). -11 (2010).

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (6)

Fig. 1.
Fig. 1. (a) HY-1C CZI true color RGB image (R: 650 nm, G: 560 nm, B: 460 nm) on 20 Apr 2020 (03:33 UTC), covering the spilled oils near Indonesia, part of it is enlarged and enhanced in the inset image. Seawater, oil slicks and oil emulsions are annotated with blue, green and red crosses, with their respective Rrc spectra shown in another inset image; (b) AVIRIS true color RGB image (R: 638.2 nm, G: 550.3 nm, B: 472.5 nm) on 17 May 2010 (Run 10, 20:12 UTC), covering the DWH oil spill (the background image was collected from MODIS Terra on the same day). Two regions of the AVIRIS image are selected and enlarged in the inset images. The DWH platform is marked with a black cross. Region I was ∼75 km from the platform, where oils formed large areas of oil slicks and a few oil emulsions. Region II was ∼32 km from the platform, where oils formed large areas of oil emulsions.
Fig. 2.
Fig. 2. (a) Experimental setup for obtaining the UV image and reflectance spectra of different oil samples, the camera was set up 1.8 m above the surface, and the four beakers were within its field of view.; (b) Absolute quantum efficiency of the CCD camera as a function of wavelength; (c) and (d) Transmission and optical density (OD = lg(1/Transmission)) spectra of the single-band bandpass filter (central wavelength: 385 nm) (more information of the filter can be found on https://www.semrock.com/FilterDetails.aspx?id=FF01-385/26-25).
Fig. 3.
Fig. 3. (a) The photograph of the samples. The shade is separated by white dotted lines; (b) Reflectance spectra of the samples. The spectral characteristics of “-OH” and “-CH” bonds can be seen clearly in the WO and OW emulsions, the wavelength ranges of the atmospheric absorption windows are indicated by gray bars [7,8]; (c) The experimentally obtained UV image of the samples (central wavelength: 385 nm). Regions of interest were delineated by red rectangles; (d) Histogram showing the mean normalized grayscale value of each sample in the UV image; (e) Schematic graph showing the multi-beam interference effect of oil slick. F0, Lg and Loil refer to the solar irradiance, sunglint reflection and interference light, respectively; (f) Classification maps using the statistical results in (d) to the five sub-regions in (c). Each sample is indicated by a small black cross and numbers corresponding to those in (d).
Fig. 4.
Fig. 4. (a) and (b) HY-1C UVI images covering the oil spill in Fig.  2(a), where only oil slicks can be detected. The θm value of the spilled oil is calculated and annotated (cosθm = cosθcosθ0 - sinθsinθ0cosφ, where θ, θ0 and φ refer to the sensor zenith, solar zenith and relative azimuth angles, respectively and all of them can be derived from the UVI data). Classification map of oil slicks and oil emulsions using the CZI data is in the inset image in (a); (c) and (d) The spectral radiance values (Lt) along the profile line in (a) and (b). “Rd” refers to the spectral radiance difference between the oil slicks and the surrounding oil-free seawater.
Fig. 5.
Fig. 5. (a) and (c) AVIRIS false color RGB image (R: 1672 nm, G: 831.5 nm, B: 647.8 nm) of the two regions in Fig.  1(b), where oil-free seawater, oil slicks, WO and OW emulsions are annotated with blue, black, red and green points, respectively. Their corresponding AVIRIS reflectance spectra are presented in (i) and (j), where the spectral characteristics of “-OH” and “-CH” can be seen clearly in the WO and OW emulsions [7,8]; (b) and (d) AVIRIS UV images (central wavelength: 380.21 nm) of the two regions; (e) to (h) Classification maps of oil slicks and oil emulsions corresponding to (a) to (d); (k) and (l) The normalized reflectance values extracted along the two profile lines at 380.21 nm, 657.8/773.1 nm and 1632 nm. Seawater, oil slicks, WO and OW emulsions are indicated corresponding with the targets annotated with the squares in Figs.  5(a) and 5(c), respectively.
Fig. 6.
Fig. 6. Comparison of oil spill detection between the UV (380.21 nm) and SWIR (1632 nm) band of AVIRIS for region I (oil slicks and WO emulsions) and region II (WO and OW emulsions) shown in Fig.  1(b).

Tables (1)

Tables Icon

Table 1. Parameters of the HY-1C UVI.