Abstract
 
Oil spills are one of the major environmental challenges affecting urban coastal cities globally. A critical industrial area, known as El-Suez refining plant, located in the Suez city in the northwestern Gulf of Suez, was chosen as a case study.
Therefore, this study aims to detect spatial-temporal contaminated soil from oil seepage events to understand the role of human activities and the physical condition of the study area. This was achieved using maximum likelihood classification, using multi-spectral satellite data of Sentinel-2 integrated with field sampling and previous studies on the same area. Analyzing Sentinel-2 data from 2015 to 2021 revealed a potential increase in contamination, coinciding with darker areas observed in the images. Additionally, spectral reflectance analysis confirmed the presence of hydrocarbons, with the 1700nm wavelength being the most reliable for detection. The resulting Land use Land cover (LU-LC) shows acceptable accuracy, with 83.33% overall and 80% for detecting contaminated soil, showcasing its potential for large-scale monitoring. The study successfully identified contaminated areas near pipelines and deactivated land farms, suggesting past bioremediation attempts. This study can be applied in similar areas to mitigate the oil spills from storage tanks and oil transfer pipelines, enhancing the environmental management strategy of oil pollution.