Unmixing of Pollution-Associated Sea Snot in the Near Surface After Its Outbreak in the Sea of Marmara Using Hyperspectral PRISMA Data

Abstract

The mucilage outbreak in the Sea of Marmara in the spring of 2021 has once again emphasized the importance of addressing climate and pollution associated hazards. Although multispectral images have traditionally been used for such purposes, an analysis of marine mucilage, with its spectral similarity to marine debris, and its spectral variations due to composition and/or sediment or bacterial aggregation, is a prime candidate to benefit from the advantages of hyperspectral data. The recently launched PRISMA mission provides an important opportunity to this end. This work proposes the use of unmixing on PRISMA datasets in order to analyze the spectral characteristics, the variation due to aggregation, and the spatial distribution, of marine mucilage. The proposed approach provides consistent and relevant information on two different datasets, with the potential to benefit cleaning and understanding efforts for marine mucilage. In addition, unlike the previous studies with supervised classification, the proposed approach does not require a training step, and the abundance fraction maps obtained using unmixing are easy to interpret and analyze for mucilage aggregation.