Making use of multi-sensor satellite imagery for oil spill observation and oil drift monitoring

Over the last ten years, satellite imagery has become one of the most effective means to observe offshore oil spills, especially over a large area, thanks to their high spatial resolution and wide swath coverage. In particular, the use of images acquired by multi-sensors, including Synthetic Aperture Radar (SAR), optical, and visible/near-infrared, allows not only to early and quickly detect oil spills but also to monitor oil drift in short-terms (several hours) and long-terms (one or two days). Such approach has not been assessed in-depth in previous studies probably due to the lack of satellite data. A case study was presented in [1] to observe the movement of oil slicks through the combination of data acquired by SAR satellites, SAR airborne system, optical instrument, and in-situ observations. However, it is very complicated to implement such method for the other cases of oil spills since it requires many different platforms and sensors that are not systematically available. Therefore, this paper focuses uniquely on the collocation of sequential images acquired by various satellite sensors (SAR and optical) for oil drift monitoring in short-terms (several hours) and long-terms (up to 24–36 hours).

For instance, to observe oil spills caused by a wrecked ship offshore Corsica (France) in Oct. 2018, we combine the images acquired by Sentinel-1 SAR (Oct. 8, 2018, 05:27:57 UTC, 17:21:45 UTC; Oct. 9, 2018, 17:14:27 UTC), Sentinel-2 optical sensor (Oct. 9, 2018, 10:20:21 UTC), and Radarsat-2 SAR (Oct. 9, 2018, 04:04:15 UTC). The techniques of oil spill detection from SAR and optical images are different. They are based on an advanced image processing procedure that will be presented at the conference. Due to the time lags between these images, we can estimate the movement of the detected oil slicks in terms of distance, velocity, and direction for 6, 12, 24, 36 hours of observation. Finally, we compare the oil drift results with the hourly data of met-ocean variables (surface wind and current) to assess the impact of the latter on oil drift. Surface wind fields (0.25° × 0.25° grid) and current vectors (0.083° × 0.083° grid) are extracted from the ERA-5 [2] and CMEMS [3] reanalysis data, respectively.

[1] C. Brekke, M. M. Espeseth, K.-F. Dagestad, J. Röhrs, L. R. Hole, and A. Reigber, “Integrated analysis of multisensor datasets and oil drift simulations—a free-floating oil experiment in the open ocean,” J. of Geophysical Research: Oceans, vol. 126, e2020JC016499, 2021, doi: 10.1029/2020JC016499.

[2] H. Hersbach et al., “ERA5 hourly data on single levels from 1959 to present,” Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on <05-12-2022>), 10.24381/cds.adbb2d47, 2018.

[3] Global Ocean 1/12° Physics Analysis and Forecast updated Daily (Accessed on <05-12-2022>), doi: 10.48670/moi-00016.