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During emergency responses to oil spills on the sea surface, quick detection and characterization of an oil slick is essential. The use of Synthetic Aperture Radar (SAR) in general and polarimetric SAR (PolSAR) in particular to detect and discriminate mineral oils from look-alikes is known. However, research exploring its potential to detect oil slick characteristics, e.g., thickness variations, is relatively new. Here a Multi-Source Image Processing System capable of processing optical, SAR and PolSAR data with proper statistical models was tested for the first time for oil slick characterization. An oil seep detected by NASA`s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in the Gulf of Mexico was used as a study case. This classifier uses a supervised approach to compare stochastic distances between different statistical distributions (fx) and hypothesis tests to associate confidence levels to the classification results. The classifier was able to detect zoning regions within the slick with high global accuracies and low uncertainties. Two different classes, likely associated with the thicker and thinner oil layers, were recognized. The best results, statistically equivalent, were obtained using different data formats: polarimetric, intensity pair and intensity single-channel. The presence of oceanic features in the form of oceanic fronts and internal waves created convergence zones that defined the shape, spreading and concentration of the thickest layers of oil. The statistical classifier was able to detect the thicker oil layers accumulated along these features. Identification of the relative thickness of spilled oils can increase the oil recovery efficiency, allowing better positioning of barriers and skimmers over the thickest layers. Decision makers can use this information to guide aerial surveillance, in situ oil samples collection and clean-up operations in order to minimize environmental impacts.

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Since May 2001 PETROBRAS is using spaceborne multi-sensor remote sensing for its sea surface monitoring program at the Campos, Santos and EspÍrito Santo Basins, southeastern Brazilian coast. Ocean color (SeaWiFS and MODIS), thermal infrared (NOAA/AVHRR), scatterometer (QuikSCAT) and Synthetic Aperture Radar (RADARSAT-1 and ENVISAT) data were integrated in order to detect and characterize different sorts of marine pollution and meteo-oceanographic phenomena. The near real time processing and delivery of the SAR data allowed the timely in-situ verification and sampling of the remotely detected events. Satellite sensors operating in the visible part of the spectrum are used to monitor ocean color variations and associated biomass changes. Thermal infrared radiometers are ideal to monitor features like oceanic fronts and upwelling plumes. However, the major limitation for both types of sensors is the extensive and persistent presence of clouds in the monitored area. Fortunately, microwave sensors such imaging spaceborne SAR permit the acquisition of oceanic scenes, regardless cloud coverage. With the spaceborne SAR systems available it is possible to have almost a daily synoptic view of large areas with suitable spatial resolution for the detection of different natural and men-made events. The integrated analysis of these dataset presents an important decision tool for emergencies, as well for the elaboration of contingency plans and evaluation of the oil industry activity impacts. Introduction Continental shelf and slope regions contain some of the Earth's most diverse and productive resources and include areas of complex and specialized ecosystems that are highly sensitive to human intervention. The interaction of complex and coupled physical and biochemical processes, as well as the wide range of space and time scales of oceanic phenomena present challenges for the effective use of spaceborne remote sensing data for sea surface monitoring and marine pollution detection. The increase availability of spaceborne SAR and visible-infrared remote sensing systems is providing opportunities for large scale oceanic monitoring and oil spill detection, compared to scattered ship observations or aircraft surveillance in limited areas. Of primary use to spill responders are the SAR sensors, which can provide high spatial resolution images of the sea surface, delivered in near real time. SAR works by emitting, and then measuring reflected microwaves. Unlike optical sensors, microwave energy penetrates clouds, rain, smoke, dust, or haze, enabling SAR systems to collect data under any atmospheric condition. Because they generate their own electromagnetic radiation to illuminate the ocean surface, SAR systems are "active systems" which can acquire data during the day or night. The physical mechanism that allows detection of oil and different oceanic surface phenomena is the differential modulation of wind induced capillary waves. These waves, which are only a few centimeters in length, produce backscattering of the incident radar pulse due to a Bragg scattering mechanism[1]. As a result, atmospheric processes that affect surface wind conditions or oceanic events that directly modulate the capillary waves produce signatures readily detected by SAR. The presence of oil dampens the capillary waves generating low backscatter region, dark in contrast with the background radar signal. Unfortunately, biogenic oil, fish sperm, algae blooms, fresh water intrusions and other surface phenomena can also produce regions of low radar backscatter which in turn can lead to misinterpretation [2]. However, the interpretation of oceanic SAR signatures is not trivial since more than one process can operate concurrently and different phenomena produce similar backscattering signal.