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Proof of concept for a new sensor to monitor marine litter from space.

Andrés CózarManuel AriasGiuseppe SuariaJosué ViejoStefano AlianiAristeidis G KoutroulisJames DelaneyGuillaume BonneryDiego MacíasRobin de VriesRomain SumerotCarmen Morales-CasellesAntonio TurielDaniel González-FernándezPaolo Corradi
Published in: Nature communications (2024)
Worldwide, governments are implementing strategies to combat marine litter. However, their effectiveness is largely unknown because we lack tools to systematically monitor marine litter over broad spatio-temporal scales. Metre-sized aggregations of floating debris generated by sea-surface convergence lines have been reported as a reliable target for detection from satellites. Yet, the usefulness of such ephemeral, scattered aggregations as proxy for sustained, large-scale monitoring of marine litter remains an open question for a dedicated Earth-Observation mission. Here, we track this proxy over a series of 300,000 satellite images of the entire Mediterranean Sea. The proxy is mainly related to recent inputs from land-based litter sources. Despite the limitations of in-orbit technology, satellite detections are sufficient to map hot-spots and capture trends, providing an unprecedented source-to-sink view of the marine litter phenomenon. Torrential rains largely control marine litter inputs, while coastal boundary currents and wind-driven surface sweep arise as key drivers for its distribution over the ocean. Satellite-based monitoring proves to be a real game changer for marine litter research and management. Furthermore, the development of an ad-hoc sensor can lower the minimum detectable concentration by one order of magnitude, ensuring operational monitoring, at least for seasonal-to-interannual variability in the mesoscale.
Keyphrases
  • deep learning
  • machine learning
  • risk assessment
  • quality improvement
  • convolutional neural network