Quantifying ocean surface oil thickness using thermal remote sensing

Jiao, Junnan; Lu, Yingcheng; Hu, Chuanmin; Shi, Jing; Sun, Shaojie; Liu, Yongchao

doi:10.1016/j.rse.2021.112513

Cited by 28 publications

(10 citation statements)

References 36 publications

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“…It is precisely because the brightness temperature difference between crude oil and WO emulsions of different concentrations is not obvious, which makes the mixing phenomenon of crude oil and WO emulsions enhanced in the recognition results of the HTI, so the vast majority of crude oil is wrongly divided into WO emulsions. This shows that thermal infrared remote sensing technology cannot be used to distinguish crude oil and WO emulsion, which is consistent with the conclusion reached by Jiao et al (2021). In order to explain the identification results of different oil spill pollution types using the HTI, the spectral and brightness temperature characteristics of different oils, crude oil and its emulsions in different states have been analyzed and discussed in the previous section.…”

Section: Figuresupporting

confidence: 77%

“…The infrared emissivity of seawater and oil film is different, and the oilwater boundary is well identified. Thermal infrared remote sensing is almost not affected by the change of light, which can realize the detection of marine oil spill and the inversion of oil film thickness, but it is difficult to distinguish the type of oil spill emulsion (Jing et al, 2011;Wang and Hu, 2015;Lu et al, 2016c;Guo et al, 2020;Jiao et al, 2021;Li et al, 2022). The comparison of marine oil spill monitoring capabilities of various remote sensing technologies is listed in Table 1.…”

mentioning

confidence: 99%

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Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing

Yang

Hu²,

Zhang³

et al. 2023

Front. Mar. Sci.

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The types of marine oil spill pollution are closely related to source tracing and pollution disposal, which is an important basis for oil spill pollution punishment. The types of marine oil spill pollution generally include different types of oil products as well as crude oil and its emulsions in different states. This paper designed and implemented two outdoor oil spill simulation experiments, obtained the hyperspectral and thermal infrared remote sensing data of different oil spill pollution types, constructed a hyperspectral recognition algorithm of oil spill pollution type based on classical machine learning, ensemble learning and deep learning models, and explored to improve the identification ability of hyperspectral oil spill pollution type by adding thermal infrared features. The research shows that hyperspectral combined with thermal infrared remote sensing can effectively improve the recognition accuracy of different oils, but thermal infrared remote sensing cannot be used to distinguish crude oil and high concentration water-in-oil emulsion. On this basis, the recognition ability of hyperspectral combined with thermal infrared for different oil film thicknesses is also discussed. The combination of hyperspectral and thermal infrared remote sensing can provide important technical support for emergency response to maritime emergencies and oil spill monitoring business of relevant departments.

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Section: Figuresupporting

confidence: 77%

mentioning

confidence: 99%

Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing

Yang

Hu²,

Zhang³

et al. 2023

Front. Mar. Sci.

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“…The worst time for the oil slick measurements were the period before sunrise and after sunset. This work was extended to quantify oil slick thickness on ocean surfaces in [16].…”

Section: B In Situ Methodsmentioning

confidence: 99%

In Situ Microwave Sensors and Switching Circuit for Oil Slick Thickness Measurement

Dala

Arslan

2022

IEEE Sensors J.

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The measurement of oil slick thickness, although at an early stage, has immense applications in determining the extent and quantity of crude oil accidentally discharged in oceans. In this work, we present the design, development and measurement of sensors and switching circuit that were integrated to form a novel microwave oil spill sensor for the determination of oil slick thickness. We used four ultra-wideband polydimethylsiloxane encapsulated antenna-sensors in a cuboid configuration connected to a customised RF switching circuit comprising of two single pole double throw (SPDT) switches. Each of the SPDT switches were connected to two antennas and a port of the vector network analyser (VNA). We developed an android application on a smartphone to select each of the antenna-sensors for the oil slick thickness measurements. The four antenna-sensors operated in the radiating near-field regions. Crude oil has lower density compared to salted seawater, therefore, it floats on the surface. This enabled the four-sensor antenna array to establish the oil thickness using radio frequency. The simulated and measured transmission coefficients showed the capability of the oil slick measurement sensor and switching circuit to determine the oil slick thickness using a novel contact-based in situ microwave approach.

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“…The presence of these noises can blur image details, affect the accuracy of wave front sensing, and ultimately impact the clarity of observed images. Solar images are obtained by capturing photons emitted by the Sun, and photons interact with the image sensor in a random manner, resulting in photon noise (Jiao et al 2021). Photon noise is typically the dominant source of noise in solar images due to its The third set of images corresponds to the sunspot run presented in Rempel (2012), computed with the MURaM code (Vögler et al 2005).…”

Section: Data Description and Pre-processingmentioning

confidence: 99%

Comparative Analysis of Image-shift Measurement Algorithms for Solar Shack–Hartmann Wavefront Sensors

Wei,

Quintero Noda,

Zhang

et al. 2023

PASP

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Observations of the Sun provide unique insights into its structure, evolution, and activity, with significant implications for space weather forecasting and solar energy technologies. Ground-based telescopes offer cost-effective and flexible solutions for high-resolution solar observations, but image quality can be affected by atmospheric turbulence. Adaptive optics (AO) systems equipped with Shack–Hartmann wave front sensors (SH-WFS) enable real-time image correction to mitigate these effects. The accuracy of SH-WFS relies on correlation algorithms that measure wave front shifts, but reaching consistent conclusions regarding their accuracy remains challenging. In this study, we conducted an evaluation and comparison of standard correlation algorithms (the Square Difference Function, Normalized Cross-Correlation, Absolute Difference Function, Absolute Difference Function-Squared, and the Covariance Function in the frequency domain (CFF)) using simulated and authentic solar images. We optimized the algorithms through pre-processing techniques and carefully selected the most suitable window function for the CFF algorithm. Additionally, we analyzed the influence of various factors, such as shift ranges, bias, and the size of live images on the accuracy of algorithms. The consistent findings revealed that the CFF algorithm demonstrates superior measurement accuracy and robustness compared to the others. Choosing the CFF algorithm for solar observations can significantly enhance measurement accuracy, AO system performance, and the overall quality of solar research findings, thereby providing crucial support for space weather forecasting and other related scientific fields.

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Quantifying ocean surface oil thickness using thermal remote sensing

Cited by 28 publications

References 36 publications

Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing

Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing

In Situ Microwave Sensors and Switching Circuit for Oil Slick Thickness Measurement

Comparative Analysis of Image-shift Measurement Algorithms for Solar Shack–Hartmann Wavefront Sensors

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