## 1 Introduction
In addition to SAR, oil spills can be detected from optical satellite imagery, e.g., Sentinel-2 [20], Landsat-8 [21], and MODIS [22]. Depending on the oil thickness, oil slicks may appear as dark or bright objects in optical images [23]. The oil index computed from reflectance values of spectral bands has been widely used for oil spill detection. It can be computed from the ratio of blue and SWIR bands [20], or from the combination of red-green-blue bands [24; 25]. Just as with SAR data, the detection of oil slicks is based on a three-stage approach: (1) identification of darker or brighter regions with respect to a background; (2) feature extraction from identified dark or bright regions; and (3) identification of oil slicks and look-alikes [26]. This is implemented using classification methods ranging from more conventional approaches such as adaptive thresholding [20] to those based on deep learning [27; 28]. Classification approaches dealing with optical data share many of the challenges that SAR-based methods face. Apart from detecting oil slicks, there has been a recent increase in interest in monitoring oil drift. This involves leveraging various types of satellite imagery, which is especially crucial for rescue operations. However, only a limited number of studies [29; 30; 31] have delved into this area. Study [31] proposed the combination of multi-band SAR and optical images to monitor floating oil drift off the coast of Norway. While yielding new insights, the proposed approach faces challenges when applied at a large scale due to limited access to critical devices, such as UAV-equipped SAR and optical sensors. Likewise, the low quality of some airborne images makes it difficult to observe variations in oil shapes. The study focused on short-term observations (6-11 h), lacking overnight measurements. Study [32] used Sentinel-1 images acquired on three different days (10, 16, and 22 August 2020) to observe oil spills caused by a shipw accident near Mauritius Island. However, a significant time lag between these Sentinel-1 images makes the evaluation of the impact of met-ocean variables on oil drift more complicated, as various factors, e.g., oil-slickcleanup and a discontinuity of the oil leak, may emerge between two sequential Sentinel-1 acquisitions and thus impact oil-drift observation. The usage of multi-sensor data can significantly enhance the timeliness of oil spill detection, thereby improving the monitoring of oil-slick evolution and drift. While supervised machine learning methods can achieve high performance in oil-slick detection when models are properly specified and trained, they are susceptible to domain and distribution shifts. For example, a neural network trained on SAR data may not perform well on optical data. Even within the same data modality, such as training on Sentinel-1 data and testing on ICEYE-X, performance degradation can occur due to differences in wavelength, spatial resolution and radiometric calibration. Collecting training data from multiple sensors is both time-consuming and labor-intensive. Furthermore, acquiring sufficient training data from commercial sensors is challenging and costly. These challenges can impede the adoption of supervised algorithms for oil-slick mapping and monitoring using multiple sensors. In this paper, we introduce an unsupervised and sensor-agnostic method for oil spill detection that can be applied across various sensors (e.g., both SAR and optical). This method can detect oil slicks over large areas in a short time frame. To the best of our knowledge, this is the first study to utilize multiple sensors for oil spill detection and to analyze oil evolution and drift with auxiliary data on wind speed and sea current velocity. To detect oil slicks from SAR and optical satellite datasets, we developed a two-step algorithm that integrates an object detection algorithm, known as the Hierarchical Split-Based Approach (HSBA) [33], with oil contour determination.