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.

翻译：海洋垃圾对海洋及海岸环境构成重大威胁，其影响日益加剧。遥感技术凭借其大范围覆盖和高频观测的能力，为传统治理手段（如局部清理作业和拖网调查）提供了有效的补充。本研究结合辐射传输模型（RTM）模拟数据与哨兵-2任务多光谱仪器（MSI）实测数据，并融合机器学习算法，旨在探究海洋塑料污染的光谱行为特征，同时评估RTM在该研究领域的适用性。探索性分析与基于KMeans算法的无监督分类结果表明，污染物的光谱行为受聚合物类型、像素覆盖百分比等因素影响，并揭示了各元素间关联与分异的光谱特征及演变趋势。本方法对数据具有较强依赖性，若将其应用于更广泛、多元化的新数据集，有望获得更优结果。这些发现可为未来利用遥感技术探测海洋塑料污染的研究提供指导方向。