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

TL;DR

This study explores the spectral properties of marine debris using Sentinel-2 MSI data and simulated models, demonstrating the potential of unsupervised machine learning to identify pollution characteristics for remote sensing applications.

Contribution

It introduces a novel approach combining RTM simulated data and Sentinel-2 MSI data with unsupervised classification to analyze marine debris spectral behavior.

Findings

Spectral behavior varies with polymer type and pixel coverage.

Unsupervised clustering reveals distinct spectral trends among pollutants.

Methodology's effectiveness depends on data diversity and quality.

Abstract

Marine litter poses significant threats to marine and coastal environments, with its impacts ever-growing. Remote sensing provides an advantageous supplement to traditional mitigation techniques, such as local cleaning operations and trawl net surveys, due to its capabilities for extensive coverage and frequent observation. In this study, we used Radiative Transfer Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of the Sentinel-2 mission in combination with machine learning algorithms. Our aim was to study the spectral behavior of marine plastic pollution and evaluate the applicability of RTMs within this research area. The results from the exploratory analysis and unsupervised classification using the KMeans algorithm indicate that the spectral behavior of pollutants is influenced by factors such as the type of polymer and pixel coverage percentage. The findings also reveal spectral characteristics and trends of association and differentiation among elements. The applied methodology is strongly dependent on the data, and if reapplied in new, more diverse, and detailed datasets, it can potentially generate even better results. These insights can guide future research in remote sensing applications for detecting marine plastic pollution.