Detection of thick patches of floating oil emulsions using X, C, and L-band SAR during Deep water Horizon oil spill

In this paper we use examples of 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 correspond to regions of emulsified (thicker) oil, which were verified by sea level observations and other remote sensing instruments. The method is sensitive to the SAR incident angles. L-band SAR was found to have the largest window of incidence angles (between 16 and 38 degrees off-nadir angle) that were able to detect Oil Emulsions (OE). C-band SAR were found to have a narrower OE detectable window (between 18 to 32 degrees off-nadir angle) than L-band. The X-band SAR had the narrowest OE detectable window (between 20 to 31 degrees off-nadir angle).