## 1 Introduction
Originally designed to categorize image pixel values into \"target\" and \"background\" classes (suitable for delineating oil slicks), HSBA has been extensively utilized across various applications [34; 35; 36]. HSBA can be applied to different satellite imagery types (SAR and optical) and used to identify oil objects of different sizes within an entire image, achieving this in an unsupervised manner. Indeed, the areas affected by oil spills typically occupy only a fraction of an image, and therefore, leveraging adaptive object detection techniques rooted in local information becomes crucial. To differentiate oil-slick objects from similar-looking ones, we propose the use of oil contours as an additional step. This refinement is particularly crucial in complex scenarios where both oil objects and look-alikes (such as low-intensity wind areas, algae/seaweed, coastal freshwater, convective cells, clouds, and high-intensity reflectance areas) exhibit comparable input values, potentially leading to misclassifications by the HSBA algorithm. Leveraging the availability of different satellite sensors, this paper focuses on the development of a multi-source systematic and automatic monitoring system from various image types. Most datasets used in this paper (i.e., Sentinel-1/2/3 and Landsat-8) can be downloaded at no cost via public data sources. Moreover, they have a high spatial resolution, e.g., a 10 m pixel-size for Sentinel-1/2, and a wide swath, e.g., 250 km (Sentinel-1) and 290 km (Sentinel-2). Over several regions (e.g., the Persian Gulf) where the cloud impact is less significant, we can obtain a high number of optical images enabling oil spill detections at a high frequency. In particular, due to the crossing of Sentinel-1/2 and Landsat-8/9, we can combine the diverse images to monitor oil drift both in the short term (spanning several hours) and the long term (over 24 h). The met-ocean data used for evaluating these influences on the spatio-temporal variations of the oil spills can be obtained through the CMEMS program [37; 38]. Although their spatial resolution may be relatively large (25 km for surface wind [37] and 9 km for current [38]), they can offer data with a high temporal resolution (one hour for wind and 0.5 h for current) for most regions of the world. Other high-resolution met-ocean data sources can be considered for some specific regions, if necessary. Specifically, in this paper, we combine Sentinel-1 (SAR), Sentinel-2, Landsat-8/9 (optical), and Sentinel-3 (optical) images for the detection of floating oil slicks and the observation of their short-term (4-8 h) and long-term (24-32 h) evolution in the Persian Gulf (off the coast of Qatar). Likewise, for emergency cases like the shipw off the coastal region of Mauritius, we collocate SAR data, including Sentinel-1 IW (Interferometric Wide), EW (Extra Wide), and ICEYE-X, to observe the oil spills emanating from a stationary source (shipwreck) at a given time and every 13 to 21 h thereafter. To estimate the short- and long-term directions, distances, and velocities of oil drifts, we superpose the detected oil slicks from different satellite data. This approach also allows us to observe changes in the shape and size of the oil entities. Section 2 presents the collocation and pre-processing of various satellite images (Sentinel-1/2/3 and Landsat-8) for oil-slick detection off the shore of Qatar and Sentinel-1 IW/EW and ICEYE-X for oil spill observation off the shore of Mauritius. Section 3 presents the methodology for detecting dark areas and oil contours. Sections 4 and 5 illustrate the oil-slick evolution observation in areas offshore of Qatar and Mauritius. Section 6 validates the proposed algorithm. Sections 7 and 8 comprise the Discussion and Conclusion.