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The UN SDG14.1.1b Plastic Debris Density Sub-Indicator From Earth Observation: Progress, Challenges and Perspectives in International Waters
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Researchers reviewed how satellites could track large patches of floating plastic litter in ocean waters outside national borders, as part of a United Nations monitoring goal. Satellite tracking has worked better near coastlines, but in open international waters, current satellite images lack enough detail and there is little on-site data to check accuracy. The review suggests better satellite technology and combining it with modeling and field studies is needed for improved monitoring.
Marine litter (ML) has attracted attention from international and national regulatory bodies because of its impact on biodiversity, water quality and the blue economy. Accordingly, ML is part of the United Nations (UN) Sustainable Development Goals, with quantitative monitoring descriptors proposed for the Goal 14 “Conserve and sustainably use the oceans, seas and marine resources for sustainable development” and the 14.1.1b sub-indicator “Plastic patches greater than 10 m”. Here, we focus our expert-driven evaluation on the prospects of monitoring the abundance of floating plastic ML in international waters from Earth observation (EO) as a complementary data source for deriving the 14.1.1b sub-indicator. The assessment we conducted, revealed the potential application of EO to enable global studies of general ML. There is significant progress in generating various ML indicators using moderate-to-medium pixel resolution (< 60 m/pixel) observations, primarily within the national waters of UN member states. Contrarily, the development of indicators in international waters has been lacking due to reduced resolution (> 100 m/pixel) of currently available EO data, the sparsity of in-situ validation observations and the limited size of the floating litter patches with plastics alone. Low-resolution EO sensors with high signal-to-noise could be leveraged, using knowledge gained from the wealth of evidence-based research in national waters. However, further research and development is essential in EO technologies to better monitor ML including investigating the potential synergy of multimodal remote sensing tools. Moreover, all EO tools must be complemented by modelling and field campaigns for a more holistic view of ML. Finally, the recommendation is to prioritise the monitoring of ML as an essential variable for future EO missions. Graphic Abstract
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