Optical high-resolution payloads require very high structural and thermal stability to work at their top performance. Performing a thermal analysis and being able to perform De-Risk activities is surely critical for what concerns optical payloads which is a highly competitive market.

WASP is a prototype EO data processor, developed in the frame of the ESA Discovery Campaign on Remote Sensing of Plastic Marine Litter, exploiting Copernicus Sentinel-2 L1C images to detect and catalogue the presence of filaments of floating marine debris with high probability of containing anthropogenic litter.

The aim of this study was to explore and consolidate the possible sources of methane (CH₄) biases in retrievals from thermal infrared (TIR) sensors, to establish necessary actions and perform the related work for mitigating biases. The study distinguisged three types of error sources: spectroscopic knowledge and input data to radiative transfer models (RTM), forward model errors, and instrument biases.

For achieving this goal, the study addresses three main objectives, corresponding to three identified tasks:

The emergence of the classical world from quantum theory, and especially of a consensus among different observers, is a long-standing problem for the foundations of quantum theory. Using recent advances in multipartite quantum information theory, we shed light on these problems. Besides their interest for fundamental physics, they are related to quantum sensing and metrology using a quantum-network of stations, a situation of interest for the potential use of quantum-based measurement probes for exploring space and the Universe.

The main aim of OPTIMAL was to perform a feasibility study on a potential mission or system to detect marine plastic litter through satellite remote sensing. To do so, the project activities involved the following aspects:

The objective of the Aeolus/EarthCARE Aerosol Assimilation Studies (A3S) project was to allow assimilation of the Aeolus L2A lidar backscatter data, also paving the way for the assimilation of EarthCARE products. Since the Aeolus and EarthCARE missions were not yet launched at the time of the study, demonstration datasets based on model data (MOCAGE model for the aerosols and the IFS for the clouds) and a one-day dataset based on CALIPSO data (reprocessed at the UV wavelength and with the Aeolus orbit data) was created.

The inexorable progress made in the past years in the field of Earth observation (EO) technologies offers a huge potential for innovation in Disaster Risk Reduction (DRR) applications. Interferometry processing of SAR data is one of the standard techniques for the detection, detailed characterisation and monitoring of surface motion caused by natural disasters. Yet the transfer of this technology progress to the user community needs to move forward in order to ensure that advancements in satellite technologies are transformed into services to be used by the community.

There is a need to achieve optimal cost effectiveness from remote sensing missions. Concurrently, a key scientific need in remote sensing is to address the spatial and temporal resolution of atmospheric measurements. Improved temporal resolution observations of the atmosphere – and importantly a reduced latency between observation and delivery to the user – can directly improve weather forecasting services.

In the context of satellite remote sensing, “hyper-spectral” is a generic term that means that “many channels” are observed. For InfraRed (IR) instruments, this term is associated with thousands channels, but for the MicroWave (MW) domain, an instrument with hundred of channels is already considered to be hyper-spectral. There are two main alternative concepts in using hyper-spectral information in MW:

Biomass burning is a key Earth system process, a major element of the terrestrial carbon cycle and a globally highly significant source of certain atmospheric trace gases and (primarily fine mode) aerosols. Natural and anthropogenic biomass burning occurs on all continents except Antarctica, with an estimated average of 3.5 - 4.5 million km2 of vegetation burnt in global wildfires each year (though this does not include the very large numbers of "small" agricultural fires that are difficult to map using current coarse spatial resolution remote sensing datasets).