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A large time series of 42 dual-polarimetric co-polarized TerraSAR-X (TSX) StripMap Synthetic Aperture Radar (SAR) measurements are exploited to monitor a well-known oil seep area, i.e., the Taylor Energy site in the Gulf of Mexico. A comprehensive scattering analysis is undertaken to assess the impact of SAR imaging parameters (polarization, angle of incidence - AOI, noise floor) and environmental conditions (wind speed - WS, oil properties) on single-polarization SAR-based sea oil seep observation. The main goal of this study is to evaluate the reliability of the scattering-based information derived from the time series of TSX SAR imagery. A two-scale backscattering model is considered to give a physical framework that supports a better understanding of the effects of the above-mentioned factors. Experimental results showed that the high TSX noise floor significantly limits a reliable interpretation of the slick-free sea surface and oil seep backscattering at AOIs larger than 34 and 26, respectively, since they are contaminated by noise. Hence, it is shown that, at larger AOIs, the joint contribution of noisy SAR measurements and low oil backscattering does not result in a larger oil/sea separability. The latter is not remarkably influenced by polarization and WS, under low-to-moderate conditions. Experiments also demonstrate that, when reliable SAR measurements are available, sea oil seep backscattering is affected by the oil's damping properties more than its concentration in the water column. The time variability of the polluted area is also estimated using the time series of TSX imagery and the obtained results agree with independent analysis undertaken on the same test site. [ABSTRACT FROM AUTHOR]

This work is aimed at identifying the origin of fine-scale features on the sea surface in synthetic aperture radar (SAR) imagery with the help of in-situ measurements as well as numerical models (presented in a companion paper). We are interested in natural and artificial features starting from the horizontal scale of the upper ocean mixed layer, around 30-50 m. These features are often associated with three-dimensional upper ocean dynamics. We have conducted a number of studies involving in-situ observations in the Straits of Florida during SAR satellite overpass. The data include examples of sharp frontal interfaces, wakes of surface ships, internal wave signatures, as well as slicks of artificial and natural origin. Atmospheric processes, such as squall lines and rain cells, produced prominent signatures on the sea surface. This data has allowed us to test an approach for distinguishing between natural and artificial features and atmospheric influences in SAR images that is based on a co-polarized phase difference filter. [ABSTRACT FROM AUTHOR]

Continuous operational monitoring by means of remote sensing contributes significantly towards less occurrence of oil spills over European waters however, operational activities show regular occurrence of accidental and deliberate oil spills over the North Sea, particularly from offshore platform installations. Since the areas covered by oil spills are usually large and scattered over the North Sea, satellite remote sensing particularly Synthetic Aperture Radar (SAR) represents an effective tool for operational oil spill detection. This paper describes the development of a semi-automated approach for oil spill detection, optimized for near real time offshore platform sourced pollution monitoring context. Eight feature parameters are extracted from each segmented dark spot. The classification algorithm is based on artificial neural network. An initial evaluation of this methodology has been carried out on 156 TerraSAR-X images. Wind and current history information also have been analyzed for particular cases in order to evaluate their influences on spill trajectory., (Copyright © 2014 Elsevier Ltd. All rights reserved.)