The coastal regions in the Persian Gulf are renowned for having the largest oil reserves. Pipelines, as a means of oil transportation, are one of the most common options for transporting petroleum products in these regions. Hence, the risk of oil spill pollution has become a vital challenge for local authorities, which has gradually increased since the last decade. Therefore, it is essential to use a method for timely detection of oil spills to prevent environmental damage. In this study, Sentinel-1 SAR data and Sentinel-2 image have been used for timely detection of oil slicks in the Persian Gulf. The area of oil pollution that extracted from Sentinel-2 (optical data), Sentinel-1 (SAR data), and field data is 114.7, 98.5, and 124.7 square kilometres, respectively. The results indicated that the object-based image analysis (OBIA) method using optical data had better results than using SAR data. In addition, the results indicated that the OBIA could be used as a method to produce ground truth map. Therefore, this research demonstrates the applicability of remote sensing data to recognize oil spill pollution on the surface of the water with the applicability of Sentinel-1 and Sentinel-2 data.

波斯湾的沿海地区以拥有最大的石油储量而闻名。管道作为石油运输手段，是这些地区运输石油产品的最常见选择之一。因此，溢油污染风险已成为地方当局面临的重大挑战，自过去十年以来，这种风险逐渐增加。因此，有必要使用一种方法及时发现溢油，以防止环境破坏。在这项研究中，Sentinel-1 SAR 数据和 Sentinel-2 图像已被用于及时检测波斯湾的浮油。从 Sentinel-2（光学数据）、Sentinel-1（SAR 数据）和现场数据提取的油污面积分别为 114.7、98.5 和 124.7 平方公里。结果表明，使用光学数据的基于对象的图像分析（OBIA）方法比使用SAR数据具有更好的结果。此外，结果表明 OBIA 可用作生成地面实况图的方法。因此，本研究通过Sentinel-1和Sentinel-2数据的适用性证明了遥感数据在识别水面溢油污染方面的适用性。