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An Ultracompact Hyperspectral imager in the Thermal Infrared

The activity followed the development of an innovative hyperspectral imaging system for remote sensing applications in the thermal infrared. The camera is compact, lightweight and robust. The activity covered the development of the three modules of the imager, namely the spectrometer, the telescope and the detector. The consortium designed and manufactured an innovative spectrometer based on Fourier transform spectroscopy, consisting on calomel birefringent crystals, with working range in the thermal infrared.

Italy

S 2021-11

CNR
BLUE: Brillouin - backscatter - fluorescence LIDAR research for Underwater Exploration of marine litter

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:

Italy

20-D-T-TEC-01

CNR-IFAC
MUSS2: Multi-Model synthetic S2-HS (HyperSpectral) data for marine/plastic debris characterization

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.

Belgium

20-D-T-TEC-01

VRIJE UNIVERSITEIT...
NEU4SST - Neuromorphic Processing for Space Surveillance and Tracking

In this project the authors looked at event-based sensing and processing for space situational awareness (SSA). Many advantages exist with the new paradigm of neuromorphic engineering, the authors investigate whether event-based optical data is suitable for the task and if processing with Spiking Neural Networks (SNN) provides advantages in terms of efficiency and efficacy. With the implementation of a novel heterogeneous LIF SNN, authors claim to surpass the state of the art in event-based SSA with a 15% increase in accuracy.

United Kingdom

EISI_I-2022-00363

UNIVERSITY OF STRATHCLYDE
Hybrid Edge-Cloud AI Accelerated Astrometric Reduction Pipeline for Agile Near-Real Time In-Situ Space Surveillance and Tracking

With the 'democratisation of space', and the proliferation of large non-geostationary orbit constellations, the pace at which human-made objects are being deployed into orbit demands urgent action in defining and supporting a global space traffic management plan.

Germany

EISI_I-2022-00365

Vyoma
Plastic Plants: Detecting water hyacinth patches as a proxy for riverine plastic transport

The project, part of the ESA Discovery Campaign on Remote Sensing of Plastic Marine Litter, developed a method to estimate floating macroplastic accumulation and transport rates in rivers using remote sensing. It combined the detection of floating water hyacinths using Sentinel-2 and Sentinel-1 with in-situ estimates of macroplastic densities carried by this invasive aquatic weed. Water hyacinths typically form large patches of several meters of width and length, and can thus be detected from space.

Netherlands

20-D-T-TEC-01-i

Wageningen University...
Detecting riverine plastic conglomerations, fluxes and pathways in Indonesia

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:

Netherlands

20-D-T-TEC-01

Deltares
SPOTS: Spectral properties of submerged and biofouled marine plastic litter

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 Netherlands

20-D-S-TEC-01-h

The Ocean Cleanup
D-TACS: on demand data transformations and auto-calibration in-orbit

Many use cases for insight generated in orbit require the integration and calibration of raw data from multiple instruments and vantage-points – a future vision sometimes referred to as ‘hybrid observation’. Such a capability would be a foundational requirement in the processing chain for rapid follow-up observations especially when we need to estimate quantitative variables (e.g. soil moisture or water pollutants).

United Kingdom

EISI_I-2022-00379

Trillium Technologies
MARLISAT

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:

France

20-D-T-TEC-01-m

CLS

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