Persistent Responsive Real-Time Earth Observation small satellite constellation mission (PERTEO) was a study dedicated to providing a global democratic service to support disaster management. Earth observation and satellite-based communications play a key role in supporting disaster management targeting responsiveness and the persistence of the service. In the terminology of on-demand Copernicus services, responsiveness is determined by the lead-time and latency. The persistence is determined by the availability and by the revisit time of the service.

'Don't try this at home' (named after one of the AI ships from I.Banks Culture series) is a concept demonstrating the benefits of having a cognitive cloud computing infrastructure in space. Such an infrastructure will provide a comprehensive and scalable set of commercial services to EO sensors and space-based data providers. It enables several applications by efficiently collecting and fusing information on target objects from observations of different platforms at various wavelengths, resolutions, and times. As a result, it leads to timely, precise information.

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.

This activity developed high-performance data compression software with embedded data analysis features for radio-frequency, multi-band images and video. It offers excellent compression ratios and quality levels with very small computing requirements, which even allow for real-time operation on modest processors. Tests on data from ESA's OPS-SAT satellite reveal image ratios over 1:10 with excellent visual quality, and radio-frequency ratios over 1:3 with nearly identical spectrograms thanks to a smart lossy approach.

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).

‘STARCOP’ is an initiative to use machine learning and multiple satellites with diverse detection capabilities to quickly detect methane leaks and send notifications in near real-time. In this project Trillium Technologies developed machine learning models to automatically detect methane in hyperspectral and multispectral imagery. Their hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in test data while reducing the false positive rate by 39% when compared to state-of-the-art models.

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).

Development of instruments and algorithms for remote sensing of plastics need standardised global in-situ observations. Compared to aquatic environments, dry shores are more accessible to frequent in situ observations. As part of the HyperDrone project, funded by the Discovery Element of the European Space Agency’s Basic Activities, we aimed to develop a standardised indicator for in-situ radiometric detection of plastic debris with the view to be deployed globally on different platforms.