Plastic marine litter is a very complex global environmental issue, with plastic litter at the sea surface representing only a small fraction of plastic entering the sea. In this respect, the BLUE project investigated the potential of diverse LIDAR techniques (backscatter, fluorescence, Raman) to address the detection of plastic litter not only floating on the sea surface, but also suspended in the first few metres under the surface, with also a focus on detecting microplastics and discriminating plastic from other types of litter. The approach adopted was four-fold:

For plastic detection, characterisation and tracking, we need rich spectral information, ideally hyperspectral data. We would like hyperspectral data up to short-wave infrared (2500 nm) and around 0.5 m ground sample distance. Neither of these characteristics are existent in current Earth observation missions, and will still be difficult to find in future Earth observation missions.

The project, named in short 'Plastic Monitor', was part of the ESA Discovery Campaign on Remote Sensing of Plastic Marine Litter, funded by the Discovery element of ESA’s Basic Activities. Plastic Monitor aimed to assess the feasibility of detecting heavy plastic pollution loads in an Indonesian river using satellite imagery and ground truth data, and to demonstrate how remote sensing can enhance the quantification and monitoring of plastic input into the marine environment.

The specific objectives of the project were to:

Recent studies have shown that remote sensing of floating marine plastic litter (MPL) is feasible from unmanned aerial systems, aircraft and satellite missions. However, in the infrared (IR) spectrum, water is a strong light absorber which makes the spectral detection and discrimination of plastics challenging. Additionally, MPL is often covered with living organisms and it is unclear what impact different thickness of biofouling may have on the spectral reflectance of floating plastic.

The objective of the MARLISAT project was to further develop marine plastic monitoring through the use of multiple satellite technologies. In effect, the intention was to use satellite technology to detect marine litter, track it and forecast its pathways.

Four satellite technologies were combined:

In the ocean, transport and mixing processes tend to disperse matter in suspension over wide spatial (~100 km) and long temporal (~month) scales. Fronts appear at the boundary between water masses with different properties and are caused by diverse oceanic features and processes, including bottom topography. Frontal structures include tidal mixing fronts, shelf-break fronts, upwelling fronts, estuarine fronts, plume fronts, fronts generated by convergence or divergence of water masses, and frontal eddies (Acha et al. 2015; Largier 1993).

The overall goal of the TRACE project was to build a remote sensing based fully automated system for the detection and tracking of large marine litter and accumulation patches of smaller litter items in order to obtain precise and reliable data on floating macro-litter regarding their quantity, position, accumulation zones, material properties, floating depth, and sources. The tracking mechanism has been implemented by coupling the daily satellite-based detections with an oceanographic forecasting system and by a two-step object matching approach.

There is an urgent need to assess the levels of plastic pollution in the ocean, to allow for future monitoring and determine the efficacy of any remediation put into force. As a consequence, parallel to in situ observations, there is a growing interest in detecting marine debris using remote sensing satellite and UAV imagery. The Marine Remote Sensing Group (MRSG) at the University of the Aegean is one of the few groups worldwide with in-deep experience in developing artificial plastic debris target structures, in different configurations and polymer composition.

Remote detection and mapping of floating marine plastic litter (FMPL) from space needs to operate in synergy with clean up actions. This implicates that an observation system is needed combining high revisit and high spatial resolution. Currently there is no single remote sensing technology with sufficient revisit capabilities and spatial/spectral resolution relevant to FMPL dynamics. Therefore a combination of small drones, satellites and High Altitude Pseudo-Satellite (HAPS) is anticipated to provide the long term sustainable technical solution.

“Remote Sensing for Marine Litter - RESMALI” has identified the ideal parameters to study marine litter (ML) from space, including spectral bands with affordable signal-to-noise ratio (SNR) at top-of-atmosphere (TOA) level, in a range of wavelengths from visible to SWIR. To continue this path towards a dedicated mission, performance assessment against simulated real-case scenarios of ML is key. We want to build a simulator to estimate ML signal at TOA from a set of scenarios representing the various applications already targeted by the community (e.g.