Oil pollution is one of the most persistent and harmful anthropogenic pressures on global marine and coastal ecosystems. Accidental discharges, chronic leaks, operational spills from shipping, offshore drilling, and industrial activities release millions of tons of hydrocarbons annually, threatening marine biodiversity, fisheries, and coastal livelihoods. Remote sensing has become the primary technology for oil spill detection, mapping, and monitoring, offering synoptic, repeatable, and objective coverage of extensive marine areas. This paper presents a systematic review of remote sensing for oil spill detection, mapping, and monitoring, grounded in a bibliometric analysis of 2856 verified documents authored by 6473 researchers, retrieved from five major academic databases (OpenAlex, CrossRef, EuropePMC, SemanticScholar, and CORE), spanning the period 2000 to 2026. Annual publication output grew from 16 documents in 2000 to a peak of 244 in 2025, reflecting a 15-fold growth driven by the Deepwater Horizon disaster (2010), the launch of Sentinel-1 (2014-2016), and the proliferation of deep learning frameworks. The review examines the physical principles of oil detection across the electromagnetic spectrum; compares radar, optical, hyperspectral, and thermal sensor platforms; and evaluates developments in artificial intelligence (AI) and data fusion methods for automated detection. Validation protocols, regional case studies from the Gulf of Mexico, North Sea, Mediterranean, Arctic, and Caspian Sea, and the integration of Earth observation with decision-support frameworks are also assessed. Key findings confirm that no single sensor is universally superior: synthetic aperture radar (SAR) provides all-weather, day-night capability, while optical and hyperspectral sensors deliver spectral and compositional insight. Deep learning models, particularly U-Net and transformer-based architectures, have achieved exceptional detection accuracy but face persistent challenges of data scarcity, look-alike discrimination, and limited cross-regional transferability. Emerging innovations in multi-sensor constellations, physics-informed deep learning, and cloud-native processing are identified as pathways toward real-time environmental intelligence and improved ocean governance.
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