Enhancing Oil Spill Detection and Response: An Overview of Satellite-based Observation Technologies and Their Impact

Abstract

Oil spills are a major environmental issue that requires prompt detection and effective response strategies. Remote sensing technologies have shown great potential in improving oil spill detection and management. This paper aims to review and compare various remote sensing techniques and models used for oil spill detection and response, with a focus on evaluating their effectiveness in preventing offshore oil spills. The study involves a comprehensive review of recent research on remote sensing methods, such as neural network-based detection, Synthetic Aperture Radar (SAR), and optical sensors, alongside oil spill response techniques. The paper also utilizes the Publish or Perish (PoP) tool to analyze scientific papers related to oil spill detection and response. The PoP tool was employed to examine citation metrics, methodologies, and trends from 187 studies, including 16 focused on remote sensing techniques, 21 on oil spill methods, and 47 on related concerns. Results indicate that neural network-based methods are effective in high-risk areas, while SAR-based detection is recommended for regions with high sea states or cloud cover. The study also finds that a combination of response techniques, such as containment booms and bioremediation, can significantly improve the effectiveness of oil spill management. Moreover, the integration of multi-sensor data and machine learning techniques shows promise in enhancing detection accuracy and reducing false positives. In conclusion, the paper highlights the need for improved sensor technologies and the integration of various detection and response methods to enhance oil spill management efforts. Future research should focus on refining these techniques and developing cost-effective solutions to enable more efficient and timely responses to oil spills.