## 8 Conclusions  
This paper introduces an innovative method that can be applied to datasets with diverse characteristics (active and passive, and varying swath width, spatial resolution, frequency, and so on) for the detection of oil spills in various scenarios, such as floating oil off the coast of Qatar and oil leakage from a shipw off the coast of Mauritius. It demonstrates effective discrimination between oil and look-alikes, particularly in challenging conditions with strong surface wind. Furthermore, due to its ability to handle data from many satellite missions, it considerably reduces the latency in accessing information, which is critical during emergencies. Through collocating detected oil slicks from different satellite images acquired at different times and combining them with sea surface wind and current data, we were able to observe that oil-drift velocity and direction are notably influenced by wind and current, irrespective of oil slick sizes. Wind contributes significantly to oil shape and size evolution, while current has a more pronounced impact on oil movement, especially at low wind speeds (below 4-5 m/s). The oil drift tends to accelerate when wind and current move in the same direction and decelerates noticeably when they move in different directions.  
The integration of multi-source satellite imagery for oil spill detection and monitoring, along with the assessment of how met-ocean variables influence oil drift, as proposed in this paper, is crucial for minimizing the impact of oil spill incidents on biodiversity and ecosystems, particularly in coastal and vulnerable regions. This approach offers an effective and cost-efficient solution for observing oil spills and their movement in both short-term (several hours) and long-term (more than 24 h) scenarios.  
In future studies, the combination of Sentinel-1/2/3 and Landsat-8 images with different time lags for oil-drift observations will be integrated with numerical simulations from models such as GNOME and OpenOil. Additionally, the remote sensing-derived data from this study may serve as valuable training/validation datasets for the development of Machine Learning/Deep Learning models.  
Conceptualization, T.V.L. and R.-M.P.; methodology, T.V.L., R.-M.P. and M.C.; validation, T.V.L., R.-M.P. and M.C.; formal analysis, T.V.L., R.-M.P., M.C., Y.L. and P.M.; writing--original draft preparation, T.V.L.; writing--review and editing, T.V.L., R.-M.P., M.C., Y.L. and P.M.; supervision, R.-M.P. and M.C.; project administration, R.-M.P. and M.C.; funding acquisition, R.-M.P. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Luxembourg National Research Fund (FNR) in the framework of the Overseas CORE project (C20/SR114703579).  
Sentinel-1/2/3 images were provided by the ESA Copernicus program. Landsat-8 images were downloaded from [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 15 February 2022). ICEYE-X images were obtained through the ESA Third Party Missions program. Surface wind and current data were downloaded from [[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)) cdsappff/home (accessed on 1 September 2022) and [[https://marine.copernicus.eu/](https://marine.copernicus.eu/)]([https://marine.copernicus.eu/](https://marine.copernicus.eu/)) (accessed on 1 September 2022).  
The authors declare no conflicts of interest.