Overall goal:  The goal of the project is to develop and test a comprehensive cost-efficient portfolio of systems to map plastic pollution in all compartments of the water column, from the surface to the seabed.

As plastics are very diverse (different shapes, sizes, colours, densities) and accumulate differently in the compartments of the water column, no single method covers all observational needs. Therefore, we will test and deploy multiple innovative remote sensing technologies, combining aerial, underwater and satellite acquisitions. For cases where large scale direct detection of the quantity of plastics is not feasible, the use of proxies will be explored. Together, these technologies are capable to cover large areas. Thus, they are a cost-effective alternative to labour-intensive spot sampling and allow to significantly increase the volume of valuable data on plastic pollution locations and densities. Therefore, as an overall quantitative target, the integration of our technologies should be able to perform a full 3D assessment of all compartments at roughly the same cost of current monitoring. The feasibility and potential value will be demonstrated through case studies in the Belgian part of the North Sea and the Scheldt Estuary.

Concrete goals and success criteria: We plan to achieve the main project goal through the following set of concrete objectives:

• Improve the science case for a dedicated satellite mission. Success criteria: SSPIRIT will provide detection limits for the direct spaceborne detection of plastics in realistic conditions.
• Evaluate feasibility of remote optical detection of plastic pellets. Success criteria: SSPIRIT will provide detection limits for direct detection using smartphone or optical VNIR-SWIR technology, and validation of indirect detection using ecogeomorphological indicators.
• Improve the recognition potential for macrolitter using cameras (GoPro) and acoustic (Blueview and multibeam) methods. Success criteria: SSPIRIT will provide learning datasets for each of the 3 methods, processed to be suitable as input to machine learning methods.
• Improve the assessment of potential microplastic hotspots using remote sensing techniques. Success criteria: The project should establish if the link between microplastics content and SPM/turbidity is justified and quantify this relation within the research areas of the project.
• Improve the performance of a depth-averaged (Eulerian) particle transport model to predict turbidity and associated microplastic concentrations. Success criteria: The project will define the uncertainty range of SPM and microplastics concentrations relative to field data, accounting for all sources of uncertainty (including the uncertainty on the field data), and relative to previous turbidity modelling studies.

Improve the performance of a depth-averaged (Eulerian) particle transport model to predict pellet loss dispersal. Success criteria: The project will define the uncertainty range of predicted pellet concentrations relative to field data, accounting for all sources of uncertainty (including the uncertainty on the field data).