Scattering model-based oil-slick-related parameters estimation from radar remote sensing: feasibility and simulation results

In this study, the potential of electromagnetic scat- tering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial intelligence (AI)-based approach. The backscattering coefficient of a slick-covered sea surface is predicted using the advanced integral equation model augmented with the model of local balance (MLB), an effective dielectric constant model, and a composite medium model to include the effect of an oil slick. Damping ratios (DRs), predicted for different oil parameters (namely, the oil thickness and seawater volume fraction), are used to train and test a four-layer neural network. Once successfully tested, the neural network is applied to an uninhabited aerial vehicle synthetic aperture radar (UAVSAR) image collected during the DeepWater Horizon (DWH) oil spill accident to retrieve the oil slick thickness and volume fraction of seawater in the oil layer. The inversion results show that the thicker (i.e., 2–4 mm) emulsions are located in the south and west of the slick and they are surrounded by thinner (i.e., <1 mm) oil films. In addition, the seawater volume fraction in the oil slick is found to be about 20%–30%. Results are contrasted with optical data and previous studies of the same accidental oil spill, showing qualitatively good agreement.