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Marine litter poses significant threats to marine and coastal environments,with its impacts ever-growing. Remote sensing provides an advantageoussupplement to traditional mitigation techniques, such as local cleaningoperations and trawl net surveys, due to its capabilities for extensivecoverage and frequent observation. In this study, we used Radiative TransferModel (RTM) simulated data and data from the Multispectral Instrument (MSI) ofthe Sentinel-2 mission in combination with machine learning algorithms. Our aimwas to study the spectral behavior of marine plastic pollution and evaluate theapplicability of RTMs within this research area. The results from theexploratory analysis and unsupervised classification using the KMeans algorithmindicate that the spectral behavior of pollutants is influenced by factors suchas the type of polymer and pixel coverage percentage. The findings also revealspectral characteristics and trends of association and differentiation amongelements. The applied methodology is strongly dependent on the data, and ifreapplied in new, more diverse, and detailed datasets, it can potentiallygenerate even better results. These insights can guide future research inremote sensing applications for detecting marine plastic pollution.

Spectral Analysis of Marine Debris in Simulated and Observed Sentinel-2/MSI Images using Unsupervised Classification