Detection of floating oil on the ocean surface, and particularly thick layers, is crucial for emergency response to accidental oil discharges. While detection of oil presence on the ocean surface is relatively easy under most conditions with a variety of remote sensing techniques, estimation of the thickness of oil layers is technically challenging. In this paper we use Synthetic Aperture Radar (SAR) imagery collected during the DeepWater Horizon (DWH) oil spill and the Texture Classifier Neural Network Algorithm (TCNNA) to identify SAR image signatures that may correspond to regions of very thick emulsified oil. These locations were generally consistent with sea level observations and optical and thermal remote sensing instruments. Oil emulsions form after crude oil is discharged in the ocean and is subjected to weathering and coagulation processes that increase thicknesses of floating oil layers. The method of detection identifies regions of increased radar backscattering within larger regions of oil-covered water. Detection is dependent on SAR incident angles and the type of SAR beam mode configuration. L-band SAR was found to have the largest window of incidence angles (16 – 38o off-nadir) that could be used to detect oil emulsions. C-band SAR showed a narrower window (18 – 32o off-nadir) than L-band, while X-band SAR had the narrowest window (20 – 31o off-nadir). The results suggest that in case of future spills in the ocean, SAR data may be used to find locations of thick oil to help make management decisions.
SMOS satellite L-band radiometer: A new capability for ocean surface remote sensing in hurricanes