Real-Time Thickness Measurement of Marine Oil Spill by Fiber-Optic Surface Plasmon Resonance Sensors

Rapid detection of marine oil spills is becoming increasingly critical in the face of frequent marine oil spills. Oil slick thickness measurement is critical in the hazard assessment of such oil leaks. As surface plasmon resonance (SPR) sensors are sensitive to slight changes in refractive index, they can monitor offshore oil spills arising from significant differences in the refractive index between oil and water. This study presents a gold-film fiber-optic surface plasmon resonance (FOSPR) sensor prepared by polydopamine accelerated wet chemical plating for rapid and real-time measurement of oil slick thickness. We examined oil thickness detection at two interfaces, namely, water-oil and air-oil. Detection sensitivity of −1.373%/mm is obtained at the water-oil interface in the thickness range of 0–5 mm; detection sensitivity of −2.742%/mm is obtained at the air-oil interface in the thickness range of 0–10 mm. Temperature and salinity present negligible effects on the oil slick thickness measurement. The fabricated FOSPR sensor has the ability to detect the presence of oil as well as quantify the oil thickness. It has favorable repeatability and reusability, demonstrating the significant potential for use in the estimation of marine oil slick thickness.

Detection of Actionable Oil using RADARSAT-2 and Simulated RADARSAT Constellation Mission Data

688970 For oil spill response, one of the key parameters is detection of actionable oil. Actionable oil (AO), which tends to be thick, emulsified oil, refers to oil that can be cleaned-up versus non-actionable oil (sheen) which cannot be readily cleaned-up. Previous studies by MDA of a controlled oil spills in the North Sea have shown the capability of RADARSAT-2 quad polarized data to detect AO using the entropy parameter (H) derived from the Cloude-Pottier decomposition. H → 0 for oil-free water and H → 1 in the presence of an oil slick. To further test the detection of AO, RADARSAT-2, ASTER, WorldView imagery and in situ measurement were acquired April 25, 2017 at the MC20 site off Louisiana. Five oil thickness classes ranging from 1 micron to 100 microns were derived from a maximum likelihood classifier based on the ASTER and WorldView images and in situ samples. For oil-free water, the average H was 0.13 and 0.62 for the slick. There was good correlation between the variability of H and the oil thickness classes. Specifically, larger H was correlated with oil thickness in the 50 – 200 micron range and smaller H was correlated with oil in the 1 micro range. Not surprising there was overlap in H for area were the oil thickness was ~ 1 micron and the area deemed to be slick-free. The results indicate that actionable oil can be discriminated from non-actionable oil based on the relative difference of H. Although these results are encouraging, one of the operational limitations is based on the use of the relatively small swath-width (25 km – 50 km) of the RADARSAT-2 quad polarized mode. The impact of the swath-width can be mitigated with data from the RADARSAT Constellation Mission (RCM). The RCM has a compact polarimetry (CP) mode that provides more polarimetric information than dual polarized modes, less than quad polarized modes, but is available for swath widths up to 500 km. Analysis of simulated SC50 RCM data (50 m resolution, 350 km swath width) derived from the aforementioned RADARSAT-2 image shows similar oil-slick variability that was observed in the RADARSAT-2 image and hence the capability to detect AO.

Measurement of floating oil layer thicknesses at the Santa Barbara seeps in California to support interpretation of satellite imagery

<p>The offshore natural oil seeps along the California coast near Santa Barbara are a natural testing site for the calibration of remote sensing systems aimed at the detection of oil spills. The main difference between these seeps and other permanent sources of floating oil (natural and unnatural seeps in the Gulf of Mexico) is the petroleum composition. Moreover, while it has been documented that most natural seeps worldwide change their rate of oil discharge over time, the Santa Barbara seeps have maintained a high rate, frequently forming thick layers of floating oil in recent years. This allowed us to perform multiple experiments developing floating oil layer thickness measurement techniques from sea-level instruments. These measurements were then used in validation of airborne and satellite remote sensors.</p><p>At the Santa Barbara seeps, we have tested our previously developed method of measuring oil thickness with a crystal tube sampling mechanism that extracts an undisturbed floating oil profile at the sea surface. Samples are then post-processed to quantify the volume of oil captured. Our newer system consists of a submerged spectrophotometer that measures the ultraviolet (UV) and infrared (IR) light attenuation of the floating oil from a fixed UV-IR light source above the water. Both methods have been used for cross validation. The sampling tube is more accurate and precise for thicknesses below 50 um (from silver-rainbow sheens to metallic). Both systems work consistently on thicknesses ranging from >50 um to 350um (the latter was the thickest sample of oil measured at the seep sites). However, the advantage of the submerged spectrophotometer is the real time interpretation of the data. The maximum thickness measured in the laboratory for the submerged spectrophotometer was 2.5mm, while the maximum thickness measured from the sampling tube was 7cm of oil.</p><p>These thickness measuring instruments have been used to validate thermal and multispectral sensors mounted on an Unmanned Aerial System (UAS). By overlaying the thickness measurements collected in the field with synchronous data collected from the UAS sensors we can relate the thermal reflective radiation and multispectral signatures from different oil thicknesses. Maps with oil thickness classifications generated from the UAS data are then used to correlate with quasi-synchronous high resolution satellite images obtained by WorldView2-3, Planet, ALOS-2, and RADARSAT-2, all of which are hosted and viewable on the NOAA-Environmental Response Management Application (ERMA).  Further field expeditions scheduled for 2021 will include the UAVSAR sensor, an L-band airborne synthetic aperture radar operated by the NASA Airborne Science Program. This NASA microwave sensor operates at the same frequency as one of the sensors on the upcoming NASA-ISRO SAR (NISAR) mission scheduled to launch in 2022 and data acquired will be used to both improve thickness algorithm development and simulate the expected performance of the NISAR instrument for oil slick detection and characterization. We will prepare these methods to move to operational use as this new resource comes online adding a significant response asset to oil spill characterization and response.</p>

Remotely Measuring Oil Slick Thickness: An Epic Challenge

<p>Abstract: The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons. Two means are currently available to remotely measure oil thickness, namely, passive microwave radiometry and time of acoustic travel. Microwave radiometry is commercially developed at this time. Visual means to ascertain oil thickness are restricted by physics to thicknesses smaller than those of rainbow sheens (~3 µm), which rarely occur on large spills, and thin sheen. One can observe that some slicks are not sheen and are probably thicker. These three thickness regimes are not useful to oil spill countermeasures, as most of the oil is contained in the thick portion of a slick, the thickness of which is unknown and ranges over several orders of magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, and further research effort is needed. Several viable concepts have been developed but require further work and verification. One of the difficulties is that ground truthing and verification methods are generally not available for most thickness measurement methods.</p>

Transitioning SAR-derived Oil Spill Thickness Measurements into an Operational Context

<p>We describe an effort to develop a quantifiable approach for determining the thicker components of oil spills using microwave synthetic aperture radar (SAR) imagery that can be utilized in an operational context to guide clean-up efforts. The presence of mineral oil on the surface can suppress the SAR returns in two ways. First, surface oil dampens the capillary waves making those areas darker in SAR imagery, an effect that been used to determine oil extent. The second is by modifying the dielectric properties of the surface from those of clean seawater to either pure oil or a mixture of oil and water as the oil weathers and thickens to form an emulsion. The emulsion provides an intermediate conductive surface layer between the highly conductive ocean itself and the very low, ‘radar transparent’ sheen layers, resulting in a further reduction in the radar returns for areas with thicker oil within an inhomogeneous oil slick. The challenges are to quantify the thickness and conditions for which this thicker layer becomes separable from the thinner oil, determine whether multiple thicker components can be identified, identify which airborne and spaceborne SAR systems can be used for this purpose, and determine under what range of environmental conditions, particularly wind speed, it is possible.</p><p> </p><p>We are planning to hold field campaigns with in situ measurements and SAR and multispectral remote sensor data collections from drones, aircraft, and satellites. The field measurements include surface collections of oil, underwater spectrophotometry, and drone-based infrared, ultraviolet, and optical collections.  Coincident with the field measurements, the airborne L-band NASA-UAVSAR SAR system will image the seep fields to track temporal changes and overpassing satellite acquisitions will be acquired. UAVSAR provides fine resolution, low noise radar imagery under all weather and solar conditions and is fully polarimetric, which enables evaluation of multiple methods to characterize the oil slick. The system noise floor of this instrument, considerably less than all satellite SAR instruments, enables a detailed examination of the zones of reduced backscatter caused by varying oil thickness levels. The primary satellite SAR will be C-band Sentinel-1, accompanied potentially by C-band Radarsat-2 and L-band ALOS-2. Both the UAVSAR and satellite SAR analysis will utilize the contrast ratio, defined as the normalized radar cross section (NRCS) in open water divided by the NRCS in oil-covered water. The larger the ratio, the thicker the oil. The operational algorithm for oil thickness is under development using satellite SAR data and will be staged in NOAA’s SAR Ocean Product System (SAROPS) that currently produces SAR-derived wind speed and oil spill extent operationally, with the latter using the Texture-Classifying Neural Network (TCNNA) to automatically delineate oil versus non-oil covered areas. We are planning field campaigns at the natural oil seep area offshore of Santa Barbara, California, in March 2021 and during the 2022 Norwegian Clean Sea Association for Operating Companies’ (NOFO’s) coordinated releases of oil in the North Sea. Recent field collections and analysis will be shown, as available.</p>

Inversion of the Thickness of Crude Oil Film Based on an OG-CNN Model

In recent years, marine oil spill accidents have occurred frequently, seriously endangering marine ecological security. It is highly important to protect the marine ecological environment by carrying out research on the estimation of sea oil spills based on remote sensing technology. In this paper, we combine deep learning with remote sensing technology and propose an oil thickness inversion generative adversarial and convolutional neural network (OG-CNN) model for oil spill emergency monitoring. The model consists of a self-expanding module for the oil film spectral feature data and an oil film thickness inversion module. The feature data self-expanding module can automatically select spectral feature intervals with good spectral separability based on the measured spectral data and then expand the number of samples using a generative adversarial network (GAN) to enhance the generalization of the model. The oil film thickness inversion module is based on a one-dimensional convolutional neural network (1D-CNN). It extracts the characteristics of the spectral feature data of oil film with different thicknesses, and then accurately inverts the oil film’s absolute thickness. In this study, emulsification was not a factor considered, the results show that the absolute oil thickness inversion accuracy of the OG-CNN model proposed in this paper can reach 98.12%, the coefficient of determination can reach 0.987, and the mean deviation remains within ±0.06% under controlled experimental conditions. In the model stability test, the model maintains relatively stable inversion results under the interference of random Gaussian noise. The accuracy of the oil film thickness inversion result remains above 96%, the coefficient of determination can reach 0.973, and the mean deviation is controlled within ±0.6%, which indicates excellent robustness.

Current velocity, temperature and oil thickness as determinants of the refloating process of stranded oil in the Bohai Sea

After oil spill accidents, weathered oil slicks can drift to coastal areas and interact with shoreline substrates. This process has been demonstrated to be the cause of the formation of stranded oil, which has attracted much attention. However, the refloating process of stranded oil when coastal hydrodynamic conditions change has been little investigated. This study evaluated the effects of current velocity, temperature and oil thickness on the refloating process of a simulated oil patty in a flow-through tank. The oil refloating efficiency (ORE) was used to quantitatively examine the degree of refloating. Non-linear fitting results indicated that the ORE increased gradually over time and then plateaued. Both observations and measurements indicated that higher current velocity brought about more oil refloat and enhanced the oil refloating rate. Furthermore, both the mass of refloating oil and the oil refloating rate showed a positive linear correlation with current velocity. The effects of temperature on the oil refloating process were determined by the effects of temperature on oil viscosity. In addition, the ORE at equilibrium increased linearly with increasing oil thickness. An empirical model was introduced and found to be closely consistent with the experimental data. This information is useful in predicting the fate and transport of stranded oil in the Bohai Sea.