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This article presents a method for monitoring the spatial dispersion of an oil slick and the temporal changes it experiences by enhancing and noise-filtering synthetic aperture radar (SAR) amplitude images. When accidental oil spills occur, spatial dispersion of the initial oil cover and assessing the temporal changes that affect it are important factors for recovery operations. Herein, the method proposed aims to facilitate a better understanding of the potential oil spread and oil-float flow from the source of the spill to adjacent coastal zones. Space-borne wide-swath SAR data is used in this regard, as it covers relatively large areas compared with other ground-based remote-sensing techniques and offer day and night observation under all weather conditions in coastal zones. The backscattered radar signal from a VV polarimetric channel, which showed a higher sensitivity for changes in the sea-surface roughness, was used. Heterogeneous surface-scattering phenomena were observed, and the amplitude image was a prominent factor in the formation of a Bragg surface around the seashore, which facilitated a better understanding of the spatial dispersion of a spilt oil layer and its temporal changes. The proposed method was applied to an oil-slick spread that happened after an oil tanker ran aground on a coral reef near Blue Bay Marine Park, south-east of the Mauritius Island, in July 2020. The method for deriving the separability between the oil and the non-oil sea surface was to render per-pixel classifiers more efficient for both speckle smoothing and texture preservation in satellite images. Furthermore, sea wind speed and direction, derived from SAR amplitude images, were used to assess the limitations and reliability of the proposed method. Based on the results, the proposed method indicated a substantial impact for facilitating a better understanding of the spatial dispersion of oil slicks and the temporal changes that affect them.

Monitoring the spatial dispersion of an oil slick by enhancing and noise-filtering SAR images using SENTINEL-1 satellite repeat-pass observations