In this article, a review of polarimetric synthetic aperture radar (SAR) methods for sea oil slick observation is presented. Marine oil pollution monitoring is a topic of great applicative and scientific relevance. In this framework, the use of remotely sensed measurements is of special interest and, in particular, the SAR because of its almost all-weather and all-day imaging capability at fine spatial resolution is the most effective tool. Conventional single-polarization SAR oil spill monitoring techniques are limited in their capability to detect oil slicks since they strongly rely on suitable thresholds, training samples, and ancillary information. Hence, an expert image analyst is due. The launch of a number of polarimetric SAR missions, along with the understanding of the peculiar physical mechanisms governing the scattering by an oil slick, led to a new paradigm (known as physical processing) that fostered a set of polarimetric algorithms particularly robust and efficient. Hence, suitable polarimetric models that exploit the departure from the slick-free sea Bragg scattering have been developed to effectively address oil slick monitoring. A set of polarimetric features extracted following such electromagnetic models have been proved to be reliable for oil slick monitoring. Polarimetric SAR observations led to a significant improvement in sea oil slick observation since they allow distinguishing oil slicks from a broad class of lookalikes in an unsupervised way. In addition, deeper information on the damping properties of the pollutant can be also inferred, which is of paramount importance for remediation purposes. Such physical processing has been demonstrated to be robust at variance of microwave carrier frequency, e.g. L-, C-, and X-band, and suitable to be exploited to extract information by dual-polarized, full-polarized, and compact modes. All these make such a physical approach of operational interest since it is able to exploit a larger set of SAR measurements building up a virtual constellation. In this review all these are detailed

In this paper, a theoretical and experimental analysis of polarimetric synthetic aperture radar (SAR) architectures is undertaken for sea oil slick observation purposes. Reference is made to the conventional full-polarimetric (FP) SAR that is here contrasted with new-generation polarimetric SAR architectures, known as compact-polarimetric (CP) SAR. Two CP modes are considered, i.e., the hybrid-polarity and π/4 modes, whose measurements are emulated from actual L- and C-band FP SAR data. Polarimetric sea surface scattering is predicted according to an extended version of the Bragg scattering model (X-Bragg) in order to point out the differences exhibited between FP and CP SAR architectures and among CP SAR modes. Theoretical predictions are then contrasted with experiments undertaken on actual polarimetric SAR data collected over well-known oil slicks and weak-damping surfactants. Results confirm model prediction, showing that differences mainly apply when polarimetric features are estimated over slick-free sea surface using different SAR architectures, with the π/4 mode behaving closer to FP SAR. Although CP SAR architectures measure only a subset of the FP information content, they represent an interesting operational alternative for both detecting oil slicks and discriminating them from weak-damping surfactants.

This study proposes a new metric to process dual-polarimetric coherent and incoherent synthetic aperture radar (SAR) data for coastline extraction purposes. The metric, based on the correlation between co- and cross-polarized channels, allows discriminating land from sea in an unsupervised way. Then, simple image processing is adopted to extract continuous coastline from the binary image. Experiments, undertaken on multipolarization C- (RadarSAT-2 and Sentinel-1) and X-band (Cosmo-SkyMed) SAR data collected in South of Italy together with Global Positioning System ground truth, confirm the soundness of the method which is shown to be both effective (a whole SAR scene is processed in seconds) and accurate (the mean error is less than $5$ and $7$ pixels for RadarSAT-2 and CosmoSkyMed, respectively).

Sea environments harbor a wide variety of life forms that have adapted to live in hard and sometimes extreme conditions. Among the marine living organisms, extremophiles represent a group of microorganisms that attract increasing interest in relation to their ability to produce an array of molecules that enable them to thrive in almost every marine environment. Extremophiles can be found in virtually every extreme environment on Earth, since they can tolerate very harsh environmental conditions in terms of temperature, pH, pressure, radiation, etc. Marine extremophiles are the focus of growing interest in relation to their ability to produce biotechnologically useful enzymes, the so-called extremozymes. Thanks to their resistance to temperature, pH, salt, and pollutants, marine extremozymes are promising biocatalysts for new and sustainable industrial processes, thus representing an opportunity for several biotechnological applications. Since the marine microbioma, i.e., the complex of microorganisms living in sea environments, is still largely unexplored finding new species is a central issue for green biotechnology. Here we described the main marine environments where extremophiles can be found, some existing or potential biotechnological applications of marine extremozymes for biofuels production and bioremediation, and some possible approaches for the search of new biotechnologically useful species from marine environments.

In this study, COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) data, collected by the single-polarization stripmap Himage mode, are exploited for coastline extraction purposes. First, a multipolarization analysis of sea surface backscattering is undertaken using the Improved Integral Equation Method (IIEM). Then, a simple two-step approach is proposed to extract the continuous coastline from co- and cross-polarized Himage CSK SAR data. The approach is framed into two steps: 1) obtaining a binary output from the SAR image using a global threshold Constant False Alarm Rate (CFAR) detector; and 2) extracting the continuous one-pixel coastline from the binary output with a conventional Sobel edge detector. The proposed approach is verified against actual CSK SAR data and it is shown to be both effective and accurate when low-to-moderate wind conditions apply.