Overview and Outcome of the NASA Marine Oil Spill Thickness (MOST) Project

In 2018, NASA funded a project to develop and mature automated oil spill detection and thickness estimates from synthetic aperture radar (SAR) and optical imagery, based on focused field testing combined with in situ oil sampling and airborne imaging with the UAVSAR L-band SAR.  The goal was to implement these new algorithms and databases in a semi-automatic system that NOAA uses operationally to detect and assess oil spills and post-storm offshore damage and debris.  The study's field validation site was the Coal Oil Point seep field, an area of natural seep activity located in the Santa Barbara channel, California, which leaks approximately 100 barrels of crude oil per day.  There were three campaigns to collect calibration/validation data, in May 2021, October 2021, and June 2022, during which UAVSAR overflew boat crews collecting optical images from a drone and some water samples for thickness and viscosity analysis.  The data was combined with available satellite SAR and optical imagery collected around the same time and used to develop an algorithm for classifying oil by relative thickness based upon contrast with clean ocean.  One algorithm is tailored for Sentinel-1 data and uses Machine Learning (ML) methods, and the other is an analytical analysis that can be applied to any radar frequency's data.  The data acquired in June 2022 has been used additionally to evaluate differentiation of mineral oil slicks from low wind radar-dark areas using a series of rapid repeat SAR images. In this talk, the study, data collections, and developed algorithms and methods will be presented.