Authors - Areej Almazroa, Sara Albahlal, Dalia Alswailem, Dhay Altamimi, Aljoharah Aldaej, Heba Kurdi Abstract - Monitoring marine litter is essential for planetary and human survival. This study proposes a novel framework integrating satellite data and big data analytics to assess marine litter distribution in coastal and oceanic environments. Leveraging open-source imagery from COPERNICUS Sentinel-2 and LANDSAT, the framework utilizes reflectance methodologies and image processing to identify and classify marine debris, focusing on spectral bands from visible blue (490 nm) to short-wave infrared (1610 nm). A pilot case study in San Diego, California, demonstrates the approach’s feasibility. The study explores the potential of microwave radiometry and machine learning for material detection and contour analysis, showing how satellite data can support dynamic and cross-platform monitoring systems. Results validate the use of remote sensing technologies to map plastic debris, providing a replicable methodology that combines emergent (e.g., satellites, drones) and traditional (e.g., sampling) techniques. This approach contributes to a deeper understanding of plastic pollution pathways, sources, and impacts across economic sectors. By generating harmonized data on mismanaged plastic waste, the study informs sustainability strategies and circular economy practices, helping redesign systemic plastic management and supporting local and global environmental governance.
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