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The amount of plastics on seawater is causing ecosystem damage to both land areas and water bodies. It is proven that once plastic particles reach the sea, they will be degraded. As their detection is not easy with  in situ  sampling, remote sensing techniques could help detect and evaluate their impact on ecosystems. To understand the main limitations of detecting floating plastic material through remote images, an a pilot experiment was carried out on an artificial floating plastic target deployed at the Aegean Sea (Greece) within the Plastic Litter Project 2021. Hyperspectral PRISMA, multispectral PlanetScope and visible Unmanned Aerial Systems (UAS) acquisitions were analysed. A nonlinear unmixing technique was applied to derive the spectral signature of the plastic target; finally, the linear unmixing approach allowed for determining the plastic percentage occupation at the pixel level. Different band combinations of the PRISMA data were selected to evaluate which provided the best result for the detection; one of these band combinations was retrieved via the principal component analysis. Only slight differences were achieved using the different PRISMA band-sets. As expected, since the artificial plastic target had a diameter comparable with the PRISMA spatial resolution (~30 m), its detection was a challenging task caused by the water influence in the pixel (mixed pixels). The detection was realized by benefitting from the high amount of available spectral bands, as confirmed by the comparative test with a PlanetScope image used at its original spatial resolution (3 m) and after degrading it at the PRISMA level. Results demonstrated that an optimal target detection was possible with few spectral bands, only taking advantage of the high spatial resolution (compared with the target dimension). Indeed, unreliable plastic fractions were derived at the PRISMA spatial resolution with a limited number of spectral bands.

Marine plastic detection using PRISMA hyperspectral satellite imagery in a controlled environment