Biogenic Slicks Research Articles

On the joint use of scattering and damping models to predict X-band co-polarized backscattering from a slick-covered sea surface

In this study, sea surface scattering with and without surfactants is predicted using the two-scale boundary perturbation model (BPM) and the advanced integral equation model (AIEM) augmented with two different damping models, i.e., the Marangoni one and the model of local balance (MLB). Numerical predictions are showcased for both mineral oil and biogenic slicks. They are contrasted with actual satellite Synthetic Aperture Radar (SAR) measurements collected at X-band by the German TerraSAR-X sensor over mineral oil and plant oil slicks of known origin. Experimental results show that the two-scale BPM augmented with the Marangoni damping model is more suitable for predicting the normalized radar cross section and the damping ratio of plant oil (biogenic) slicks. In contrast, the AIEM combined with the damping MLB results in a better agreement with SAR measurements collected over mineral oil slicks.

Monitoring Mesoscale to Submesoscale Processes in Large Lakes with Sentinel-1 SAR Imagery: The Case of Lake Geneva

As in oceans, large-scale coherent circulations such as gyres and eddies are ubiquitous features in large lakes that are subject to the Coriolis force. They play a crucial role in the horizontal and vertical distribution of biological, chemical and physical parameters that can affect water quality. In order to make coherent circulation patterns evident, representative field measurements of near-surface currents have to be taken. This, unfortunately, is difficult due to the high spatial and temporal variability of gyres/eddies. As a result, few complete field observations of coherent circulation in oceans/lakes have been reported. With the advent of high-resolution satellite imagery, the potential to unravel and improve the understanding of mesoscale and submesoscale processes has substantially increased. Features in the satellite images, however, must be verified by field measurements and numerical simulations. In the present study, Sentinel-1 SAR satellite imagery was used to detect gyres/eddies in a large lake (Lake Geneva). Comparing SAR images with realistic high-resolution numerical model results and in situ observations allowed for identification of distinct signatures of mesoscale gyres, which can be revealed through submesoscale current patterns. Under low wind conditions, cyclonic gyres manifest themselves in SAR images either through biogenic slicks that are entrained in submesoscale and mesoscale currents, or by pelagic upwelling that appears as smooth, dark elliptical areas in their centers. This unique combination of simultaneous SAR imagery, three-dimensional numerical simulations and field observations confirmed that SAR imagery can provide valuable insights into the spatial scales of thus far unresolved mesoscale and submesoscale processes in a lake. Understanding these processes is required for developing effective lake management concepts.

Deep Learning for Mineral and Biogenic Oil Slick Classification With Airborne Synthetic Aperture Radar Data

Studies of oil slicks in the ocean environment with synthetic aperture radar (SAR) have found that one of the most complex challenges to oil spill detection is the separation of mineral oil spills from slicks that are biogenic in origin. The possible occurrence of multiple scattering mechanisms beyond Bragg scattering for the sea surface, with or without biogenic or mineral oil slicks, and even under low to moderate wind conditions, has also been a subject of debate because the measured signals from these radar-dark surfaces can be contaminated easily by noise. Therefore, the use of noise-uncontaminated data is required for oil spill study in order to avoid significant alteration in the measured radar backscatter, which can lead to misinterpretation and misclassification of the scattering mechanisms involved. To this end, this study uses uninhabited aerial vehicle SAR data, with a noise-equivalent sigma zero as low as −53 dB, to investigate slick classification within a deep learning framework in order to assess deep architectures’ capabilities for providing a reliable and accurate three-state classifier capable of separating mineral oil films from biogenic slicks and from the clean sea. The study exploits parameters with sensitivity to the dielectric constant and ocean wave damping properties, and convolutional neural networks’ (CNNs’) capability for learning nonlinear features, shapes, and textural and statistical patterns, in order to obtain significant classification accuracy. Very high accuracy results have been achieved, with values up to 0.91, 0.94, 0.98, and 0.99 under the most probable real-world spill acquisition conditions.

Oil Spill Discrimination by Using General Compact Polarimetric SAR Features

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance.

Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

As a major marine pollution source, oil spills largely threaten the sustainability of the coastal environment. Polarimetric synthetic aperture radar remote sensing has become a promising approach for marine oil spill detection since it could effectively separate crude oil and biogenic look-alikes. However, on the sea surface, the signal to noise ratio of Synthetic Aperture Radar (SAR) backscatter is usually low, especially for cross-polarized channels. In practice, it is necessary to combine the refined detail of slick-sea boundary derived from the co-polarized channel and the highly accurate crude slick/look-alike classification result obtained based on the polarimetric information. In this paper, the architecture for oil spill detection based on polarimetric SAR is proposed and analyzed. The performance of different polarimetric SAR filters for oil spill classification are compared. Polarimetric SAR features are extracted and taken as the input of Staked Auto Encoder (SAE) to achieve high accurate classification between crude oil, biogenic slicks, and clean sea surface. A post-processing method is proposed to combine the classification result derived from SAE and the refined boundary derived from VV channel power image based on special density thresholding (SDT). Experiments were conducted on spaceborne fully polarimetric SAR images where both crude oil and biogenic slicks were presented on the sea surface.

Experimental L-Band Airborne SAR for Oil Spill Response at Sea and in Coastal Waters.

Satellite synthetic aperture radar (SAR) is frequently used during oil spill response efforts to identify oil slick extent, but suffers from the major disadvantages of potential long latency between when a spill occurs and when a satellite can image the site and an inability to continuously track the spill as it develops. We show using data acquired with the Uninhabited Aerial Vehicle SAR (UAVSAR) instrument how a low noise, high resolution, L-band SAR could be used for oil spill response, with specific examples of tracking slick extent, position and weathering; determining zones of relatively thicker or more emulsified oil within a slick; and identifying oil slicks in coastal areas where look-alikes such as calm waters or biogenic slicks can confound the identification of mineral oil spills. From these key points, the essential features of an airborne SAR system for operational oil spill response are described, and further research needed to determine SAR’s capabilities and limitations in quantifying slick thickness is discussed.

Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

Polarimetric synthetic aperture radar (SAR) remote sensing provides an outstanding tool in oil spill detection and classification, for its advantages in distinguishing mineral oil and biogenic lookalikes. Various features can be extracted from polarimetric SAR data. The large number and correlated nature of polarimetric SAR features make the selection and optimization of these features impact on the performance of oil spill classification algorithms. In this paper, deep learning algorithms such as the stacked autoencoder (SAE) and deep belief network (DBN) are applied to optimize the polarimetric feature sets and reduce the feature dimension through layer-wise unsupervised pre-training. An experiment was conducted on RADARSAT-2 quad-polarimetric SAR image acquired during the Norwegian oil-on-water exercise of 2011, in which verified mineral, emulsions, and biogenic slicks were analyzed. The results show that oil spill classification achieved by deep networks outperformed both support vector machine (SVM) and traditional artificial neural networks (ANN) with similar parameter settings, especially when the number of training data samples is limited.

Oil spill detection by imaging radars: Challenges and pitfalls

Criteria for discriminating between radar signatures of oil films and biogenic slicks visible on synthetic aperture radar (SAR) images of the sea surface as dark patches are critically reviewed. We question the often claimed high success rate of oil spill detection algorithms using single-polarization SARs because the SAR images used to train these algorithms are based usually on subjective interpretation and are not validated by on-site inspections or multi-sensor measurements carried out from oil pollution surveillance planes. Furthermore, we doubt that polarimetric parameters derived from fully-polarimetric SAR data, like entropy, anisotropy, and mean scattering angle, are beneficial for discriminating between mineral oil films and biogenic slicks. We challenge the often-made claim that another scattering mechanism than Bragg scattering applies for radar backscattering from mineral oil films than from biogenic slicks. This view is supported by data acquired by the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) of NASA/JPL, which operates at L-band and has an extremely low noise floor. We suspect that opposing results obtained from previous analyses of spaceborne polarimetric SAR data are caused by the high noise floors of the spaceborne SARs. However, most of the analyzed spaceborne polarimetric data were not acquired at L-band, but at C-and X-band. On the other hand, differences in the statistical behavior of the radar backscattering could be real due to the fact that, other than biogenic surface films, mineral oil films, can form multi-layers, whose thickness can vary within an oil patch. Radar backscattering from emulsion layers can also fluctuate due to variations of the oil/water mixture ratio. These effects could cause an increase of the standard deviation (STD) of the co-polarized phase difference (CPD) for scattering at mineral oil films and emulsions. In the special case of thick oil layers or oil/water emulsion layers, where the radar is sensitive to the dielectric constant of the oil, discrimination becomes possible due the fact that Bragg scattering depends on the dielectric constant of the scattering medium.

The Potential Of The Single, Dual And Quad-Pol SAR Data On Oil Slicks Detection

During the oil spills response actions, the rapid location and mapping of the oil slicks at sea surface is needed, as well as the characterization of different types of oils, avoiding false alarms whenever possible. In this way, all information provided by remote sensors can be useful to improve the strategies to cleanup oceanic regions and to protect the vulnerable coastal areas, minimizing possible environmental damages. Synthetic Aperture Radars (SAR) are widely used for this purpose, due to their potential for systematic data acquisition covering large areas with near real time delivery capability, offering different spectral and spatial resolutions, swaths, incidence angles and polarization channels. Since the nineties automatic and semiautomatic algorithms have been developed to detect low backscatter regions related with oil slicks aiming to assist large scale monitoring initiatives. Traditionally, the majority of the SAR data used have commonly been acquired with wide swaths in single or dual-pol channels. However during the emergency situations, where the position of the oil spills are known, polarimetric SAR data (Pol SAR) can be used to provide detailed information about the occurrences, as well as to minimize the false alarms interference. Recent studies, using several polarimetric attributes, have been investigating the potential of dual, quad and compact polarization modes to detect different backscatter mechanisms in biogenic and mineral oil slicks, probably related with different chemical properties and thicknesses variations. The objective of this paper is to evaluate the potential and the trade-offs provided by single, dual and quad-polarized data to detect oil slicks at sea surface. To accomplish this, a set of SAR data acquired by RADARSAT-2 [quad-polarized] and Sentinel-1 [dual-polarized] were processed and classified using a region classifier named Pol Class based on stochastic distances. The Pol Class was used because of its potential to use proper statistical modeling to process SAR data in different formats. This work uses the Intensity and the Polarimetric modules to classify the SAR data. The Polarimetric module is used to evaluate all complex information contained in the Pol SAR data, and the Intensity module provides the dual and single-polarized channels assessment, making feasible to compare the advantages of using all possible polarimetric combinations. The expectation is to contribute with the scientific community evaluation about the trade-offs provided by the single, dual and quad-polarized SAR data applied to oil detection, considering as reference the needs of the operational use during both, routine monitoring and emergency situations.

Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual- and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual- and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.

The Extended Bragg Scattering Model-Based Method for Ship and Oil-Spill Observation Using Compact Polarimetric SAR

Ocean surveillance is one of the important applications in synthetic aperture radar (SAR) imagery. Polarimetric SAR provides multichannel information and shows great potential for ocean target observation. Oil-spills and ships possess different polarimetric features from the ocean surface, whose physical backscattering property is generally admitted as being dominated by the Bragg resonant scattering. In this study, we focus on the oil-spill and ship observation based on the polarimetric features. Oil-spills present a non-Bragg scattering property. While backscatter from ships is predominated by the double-bounce scattering and the multiple reflections between the ship and sea surface. Ships exhibit very different scattering characteristics from the ocean Bragg scattering. Based on the extended Bragg (X-Bragg) model, a new method is proposed for observing oil-spills and ships. This method allows distinguishing oil-spills from two kinds of important look-alikes, i.e., biogenic slicks and the low-wind region (LWR), and also shows good performance for ship enhancement. Experiments are performed on the C-band fully polarimetric SAR data acquired by both SIR-C/X-SAR and RADARSAT-2. The other issue concerned is the application potential of the hybrid dual-polarimetric (i.e., compact polarimetry (CP)) SAR mode, which has the advantage of providing larger imaging coverage compared to the full polarimetry. Based on the X-Bragg model, where the backscatter reflection symmetry is assumed, an equivalent method is proposed with the circularly polarized transmission CP mode. Experimental results show that similar results can be obtained between the CP and full polarimetry for ocean target observation.

Spatiotemporal measurement of surfactant distribution on gravity–capillary waves

Materials adsorbed onto the surface of a fluid – for instance, crude oil, biogenic slicks or industrial/medical surfactants – will move in response to surface waves. Owing to the difficulty of non-invasive measurement of the spatial distribution of a molecular monolayer, little is known about the dynamics that couple the surface waves and the evolving density field. Here, we report measurements of the spatiotemporal dynamics of the density field of an insoluble surfactant driven by gravity–capillary waves in a shallow cylindrical container. Standing Faraday waves and travelling waves generated by the meniscus are superimposed to create a non-trivial surfactant density field. We measure both the height field of the surface using moiré imaging, and the density field of the surfactant via the fluorescence of NBD-tagged phosphatidylcholine, a lipid. Through phase averaging stroboscopically acquired images of the density field, we determine that the surfactant accumulates on the leading edge of the travelling meniscus waves and in the troughs of the standing Faraday waves. We fit the spatiotemporal variations in the two fields using an ansatz consisting of a superposition of Bessel functions, and report measurements of the wavenumbers and energy damping factors associated with the meniscus and Faraday waves, as well as the spatial and temporal phase shifts between them. While these measurements are largely consistent for both types of waves and both fields, it is notable that the damping factors for height and surfactant in the meniscus waves do not agree. This raises the possibility that there is a contribution from longitudinal waves in addition to the gravity–capillary waves.

Characterization of Low-Backscatter Ocean Features in Dual-Copolarization SAR Using Log-Cumulants

The potential of using log-cumulants for discrimination between mineral oil spills and other low-backscatter ocean features in synthetic aperture radar data is investigated here. Radarsat-2 fine quad-polarization data containing experimental oil spills, as well as simulated biogenic slicks and a natural phenomenon, are analyzed. For this data set, the combined information of matrix log-cumulants of first and second order is found to clearly discriminate the majority of the mineral oil spills from the simulated biogenic slicks and the natural phenomenon. These preliminary findings suggest that the proposed method has a potential application in classification of low-backscatter ocean features of unknown origin.

Repair wind field in oil contaminated areas with SAR images

In this paper, we compared the normalized radar cross section in the cases of oil spill, biogenic slicks, and clean sea areas with image samples made from 11-pixel NRCS average, and determined their thresholds of the NRCS of the synthetic aperture radar. The results show that the thresholds of oil and biogenic slicks exhibit good consistency with the corresponding synthetic aperture radar images. In addition, we used the normalized radar cross section of clean water from adjacent patches of oil or biogenic slicks areas to replace that of oil or biogenic slicks areas, and retrieve wind field by CMOD5.n and compare wind velocity mending of oil and biogenic slicks areas with Weather Research and Forecasting modeled data, from which the root mean squares of wind speed (wind direction) inversion are 0.89 m/s (20.26°) and 0.88 m/s (7.07°), respectively. Therefore, after the occurrence of oil spill or biogenic slicks, the real wind field could be repaired using the method we introduced in this paper. We believe that this method could improve the accuracy in assessment of a real wind field on medium and small scales at sea, and enhance effectively the monitoring works on similar oil or biogenic slicks incidents at sea surface.

Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features

In this paper, we study surface slick characterization in polarimetric C-band synthetic aperture radar (SAR) data. The objective is to identify the most powerful multipolarization SAR descriptors for mineral oil spill versus biogenic slick discrimination. A systematic comparison of eight well-known multipolarization features is provided. The analysis is performed on data that we collected during a large-scale oil spill exercise at the Frigg field situated northwest of Stavanger, in June 2011. Controlled oil spills and simulated look-alikes were simultaneously captured within fine quad-polarization Radarsat-2 acquisitions during this experiment. Multipolarization features derived from only the copolarized complex scattering coefficients are explored. We find that the two most powerful multipolarization features extracted from this data set are the geometric intensity, measuring the combined intensity based on the determinant of the coherency matrix, and the real part of the copolarization cross product, which is related to the scattering behavior of the target. We show that these two features can distinguish between the simulated biogenic slicks and mineral oil types such as Balder and Oseberg blend, and that the discriminative power seems to be persistent with time.

Detection and monitoring of offshore oil seeps using ERS/ENVISAT SAR/ASAR data and seep-seismic studies in Krishna–Godavari offshore basin, India

An attempt has been made to use synthetic aperture radar (SAR) data for detection and monitoring of offshore oil seeps in the eastern offshore areas of the Krishna–Godavari Basin, which has been supplemented and correlated with collateral free-air gravity and seismic data. Images of the study area obtained from ENVISAT ASAR image mode were processed and analysed in detail. A number of natural oil seepages were identified and distinguished from pollution and biogenic slicks. These were subsequently studied using different parameters to assign various degrees of confidence. The repetitiveness of the identified seepages was studied and a total of five areas of seep repetitions had been recognized in the study area. The seeps that are repeated in images of different dates are more likely to be of natural origin than others. Simulation and modelling of a particular oil slick arising has been attempted over the Krishna–Godavari offshore using MIKE 21 software.

Using the optical and radar satellite images for the analysis of ecological conditions in the marine environment

Considered are the possibilities for detecting the zones of ecological problems of marine environment using the ocean color sensors data and the radar images. Presented are the peculiarities of satellite data interpretation and the experimental results indicating the spatial and temporal distribution of biogenic slicks and chlorophyll-a concentration as the indicators of ecological conditions in the marine environment. Proposed is the multiparameter correlation graph displaying the relationship between the ecological factors significant for studying the anthropogenic eutrophication.

Joint sun-glitter and radar imagery of surface slicks

A method is proposed to retrieve and interpret fine spatial variations of the sea surface roughness in sun glitter imagery. Observed sun glitter brightness anomalies are converted using a transfer function determined from the smoothed shape of sun glitter brightness. The method is applied to MODIS and MERIS sun glitter imagery of natural oil seeps and the catastrophic Deepwater Horizon oil spill in the Gulf of Mexico. The short-scale roughness variations in the presence of mineral oils slicks are consistently extracted and compared to variations associated with the biogenic slicks. In doing so, the wind speed dependency on the roughness anomalies is also considered. A comparison to normalized radar cross section (NRCS) anomalies taken from the corresponding high resolution ASAR images is performed, and similarities as well as differences are investigated. The results document significant benefit from the synergetic use of sun glitter and radar imagery for detection and monitoring of surface slicks.

The Two-Scale BPM Scattering Model for Sea Biogenic Slicks Contrast

Sea oil slick observation by means of synthetic aperture radar is still a scientific and operational challenge. In this paper, the sea surface scattering with and without biogenic slicks is analyzed by using the two-scale Boundary Perturbation Method scattering model. The surface slick is supposed to modify both the full-range sea surface spectrum and the slope probability density function by means of the Marangoni damping and by a reduced friction velocity. In this paper, the full-range Universite Catholique de Louvain sea surface spectrum is considered. A new contrast model, which overcomes the drawbacks of the contrast model based on the untilted Small Perturbation Method scattering model, is presented and illustrated in some L- and C-band biogenic slick cases.

Coastal upwelling observed by multi-satellite sensors

Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data.