CubeSats have the potential to revolutionise Earth observation from space by exploring different operating points in terms of spatial, temporal, and spectral resolution. However, physical limitations, mostly in terms of size, prevent them from obtaining high-quality images. OPS-SAT, while characteriaed by a small form factor, provides an impressively capable and flexible hardware/software platform for executing demanding machine learning algorithms (inference stage).

The continuously increasing amount of long-term and of historic data in EO facilities in the form of online datasets and archives makes it necessary to address technologies for the long-term management of these data sets, including their consolidation, preservation, and continuation across multiple missions. The management of long EO data time series of continuing or historic missions, with more than 20 years of data available already today, requires technical solutions and technologies which differ considerably from the ones exploited by existing systems.

Background and justification:

The de-risk activity has been focused on the critical aspects of the dual-service constellation and satellite, to remove the principal risks related to understanding the: - Market analysis in order to identify end-users for the proposed service - System concept and architecture definition, including payloads and their accommodation - Preliminary analysis of the dual service expected performances - Combined dual-service quality - Preliminary business case with the preliminary calculation of the total service cost.

The LEO-GEO4GHG project studied the feasibility of new frontiers of cloud computing in space and artificial intelligence (AI) combining multiple sources of data such as the SATLANTIS GEI-SAT constellation and other external meteorological and atmospheric data with the final aim of providing near real-time methane detection and quantification.

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.

Payload data transmitters in Earth Observation (EO) missions are moving from X-band to K-band (i.e. 25.5 to 27 GHz), where the downlink data rates can be increased due to the four times higher availability of bandwidth than in X-band. In addition, an interest is certainly growing in the field of small-size satellites and cube-sats. Therefore, there is a need to reduce the size/mass of the K-Band transmitter while increasing the DC-to-RF efficiency in order to relax the constraints of the thermal management systems at platform level.

The main objective of this study was to evaluate the merits of a dual-camera setup to acquire high-resolution images of identified events of iinterest, on board, through AI methods. A mission analysis of the approach for a single-satellite, dual camera setup as well as for a two-satellite (leading-trailing), single camera setup was performed. The dual-camera AI approach was demonstrated on commercially available high-performance embedded computers (Xilinx Kria SoM / Google Edge TPU SoM) through a state-of-the-art active fire detection use case.