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© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Earth Observation missions have increasingly demanding requirements, i.e., higher spatial resolutions and SNR, and lower revisit times, etc. High-resolution images have to be captured with precise pointing accuracy which is achieved by the satellite's attitude determination and control system (ADCS). This subsystem embeds multiple sensors and actuators capable of changing the satellite attitude by changing the torque applied to the platform. In small satellites, magnetorquers, and more recently reaction wheels as well, are the most frequently used actuators. These components fulfill their main purpose, but exhibit some drawbacks, notably a nonconstant jitter that provokes nondesired oscillations of the spacecraft, which may result in blurred images. This work presents a deblurring methodology that uses data coming from the ADCS sensors to infer a spatially variant point-spread function. Moreover, the impact of having a lower temporal resolution than the ideal on the sensors is analyzed and a method is presented to improve it. This work has been supported by project "Sensing with pioneering opportunistic techniques-SPOT," Spanish Agencia Estatal de Investigación Grant RTI2018-099008-B-C21 and EU ERDF funds, and UPC-CommSensLab María de Maeztu Unit Grant MDM-2016-0600.

2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 11-16 July 2021, Brussels, Belgium, FSSCat, the “Federated Satellite Systems/ 3 Cat-5” mission was the winner of the 2017 ESA S^3 (Sentinel Small Satellite) Challenge and overall winner of the Copernicus Masters competition. FSSCat consists of two 6 unit cubesats carrying on board UPC's Flexible Microwave Payload - 2 (FMPL-2), an L-band microwave radiometer and GNSS-Reflectometer implemented in a software defined radio, and Cosine's HyperScout-2 visible and near infrared + thermal infrared hyperspectral imager, enhanced with PhiSat-1, a on board Artificial intelligence experiment for cloud detection. Both spacecrafts include optical and UHF inter-satellite links technology demonstrators, provided by Golbriak Space and UPC, respectively. This paper describes the mission, and the main scientific results of the FMPL-2 obtained during the first three months of the mission, notably the sea ice concentration and thickness, and the downscaled soil moisture products over the Northern hemisphere

2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 11-16 July 2021, Brussels, Belgium.-- 4 pages, 4 figures, Knowledge about sea ice concentration and extent in polar regions is of great interest both for economic interests, and as a proxy of the climate change. Retrieved maps are based on data from microwave radiometers, which are currently provided by large satellite missions. Nowadays, CubeSats have proven to be a cost-effective alternative. Due to their low cost, they can be launched in large constellations to obtain high spatial coverage and daily revisit. This study presents a neural network approach to generate sea ice concentration and sea ice extension maps using the L-band microwave radiometer, and the GNSS-Reflectometer data from the FMPL-2 instrument onboard 3 Cat-5/A, one of the two CubeSats of the FSSCat mission. The results obtained during the first 2 months of the mission are presented

Several approaches have been developed to yield Arctic sea ice thickness based on satellite observations. Microwave radiometry operating at L-band is sensitive to sea ice properties and allows to retrieve thin sea ice up to ~ 0.5 m. Sea ice thickness retrievals above 1 m can be successfully derived using sea ice freeboard data from satellite altimeters. Current inference models are build upon empirically determined assumptions on the physical composition of sea ice and are validated against regionally available data. However, sea ice can exhibit time-dependent non-linear relations between sea ice properties during the process of formation and melting, which cannot be fully addressed by simple inversion models. Until now, an accurate estimation of sea ice thickness requires specific conditions and is only viable during Arctic freeze up from mid-October to mid-April. Neural networks are an efficient model-based learning technique capable of resolving complex systems while recognizing hidden links among large amounts of data. Models have the advantage to be adaptive to new data and can therefore reflect seasonally changing sea ice conditions to make predictions based on the relationship between a set of input features. FSSCat is a two 6-unit CubeSat mission launched on September 3, 2020, which carries the FMPL-2 payload on board the 3Cat-5/A, one out of two spacecrafts. FMPL-2 encompasses the first L-band radiometer and GNSS-Reflectometer on a CubeSat, designed to provide global brightness temperature data suitable for soil moisture retrieval on land and sea ice applications.In this work a predictive regression neural network was built to predict thin sea ice thickness up to 0.6 m at Arctic scale based on FMPL-2 observations and ancillary data including sea ice concentration and surface temperature. The network was trained based on CubeSat acquisitions during early Arctic freeze up from October 15 to December 4, 2020, using existing maps of daily estimated sea ice thickness derived from the Soil Moisture and Ocean Salinity (SMOS) mission as ground truth data. Hyperparameters were optimized to prevent the model from overfitting and overgeneralization with the best fit resulting in an overall mean absolute error of 6.5 cm. Preliminary results reveal good performance up to 0.5 m, whereas predicted values are slightly underestimated for higher thickness. The thin ice model allows to produce weekly composites of Arctic sea ice thickness maps. Future work involves the complementation of the input features with sea ice freeboard observations from the Cryosat-2 mission to extend the sensitivity range of the current network and to validate the findings with on-site data. Aim is to apply the model trained on Arctic data to retrieve full-range Arctic and Antarctic sea ice thickness maps. The presented approach demonstrated the potential of neural networks for sea ice parameter retrieval and indicated that data acquired by moderate-cost CubeSat missions can offer scientifically valuable contributions to applications in Earth observation.

Special issue Applications of GNSS Reflectometry for Earth Observation II.-- 22 pages, 13 figures, 2 tables, supplementary material https://doi.org/10.3390/rs13050994.-- Data used in this study will be publicly and freely available for everyone at the Copernicus system as part of the FSSCat mission, The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45 ºN). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM, This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). This work was (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21 / AEI / 10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work was also (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). Joan Francesc Munoz-Martin received support from the grant for the recruitment of early-stage research staff FI-DGR 2018 of the AGAUR - Generalitat de Catalunya (FEDER), Spain; Christoph Herbert received the support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050 and funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673; David Llavería received support from an FPU fellowship from the Spanish Ministry of Education FPU18/06107, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

Special issue Applications of GNSS Reflectometry for Earth Observation II.-- 19 pages, 17 figures, 4 tables, supplementary material https://doi.org/10.3390/rs13061139.-- Data used in this study will be publicly and freely available for everyone through the Copernicus system as part of the FSSCat mission, CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved, This work was supported by 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). this work has been (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21/AEI/10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work has also been (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, and by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). David Llavería receives support from a FPU fellowship from the Spanish Ministry of Education FPU18/06107.; Joan Francesc Munoz-Martin receives support from the grant for recruitment of early stage research staff FI-DGR 2018 of the AGAUR—Generalitat de Catalunya (FEDER), Spain; C.H. receives support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050, and funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673

Nowadays, constellations and distributed networks of satellites are emerging as clear development trends in the space system market to enable augmentation, enhancement, and possibilities of new applications for future Earth Observation (EO) missions. While the adoption of these satellite architectures is gaining momentum for the attaining of ever more stringent application requirements and stakeholder needs, the efforts to analyze their benefits and suitability, and to assess their impact for future programmes remains as an open challenge to the EO community. In this context, this paper presents the mission and system architecture conceived during the Horizon 2020 ONION project, a European Union research activity that proposes a systematic approach to the optimization of EO space infrastructures. In particular, ONION addressed the design of complementary assets that progressively supplement current programs and took part in the exploration of needs and implementation of architectures for the Copernicus Space Component for EO. Among several use cases considered, the ONION project focused on proposing system architectures to provide improved revisit time, data latency and image resolution for a demanding application scenario of interest: Marine Weather Forecast (MWF). A set of promising system architectures has been subject of a comprehensive assessment, based on mission analysis expertise and detailed simulation for evaluating several key parameters such as revisit time and data latency of each measurement of interest, on-board memory evolution and power budget of each satellite of the constellation, ground station contacts and inter-satellite links. The architectures are built with several heterogeneous satellite nodes distributed in different orbital planes. Each platform can embark different instrument sets, which provide the required measurements for each use case. A detailed mission analysis has then been performed to the selected architecture for the MWF use case, including a refined data flow analysis to optimize system resources; a refined power budget analysis; a delta-V and a fuel budget analysis considering all the possible phases of the mission. This includes from the correction of launcher injection errors and acquisition of nominal satellite position inside the constellation, orbit maintenance to control altitude, collision avoidance to avoid collision with space debris objects and end-of-life (EOL) disposal to comply with EOL guidelines. The relevance of the system architecture selected for the MWF has been evaluated for three use cases of interest (Arctic sea-ice monitoring, maritime fishery pressure and aquaculture, agricultural hydric stress) to show the versatility and the feasibility of the chosen architecture to be adapted for other EO applications., This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 687490.

Attitude determination and control systems (ADCS) are critical in many satellite missions, especially when high pointing accuracy is needed, such as in optical imagers. Current actuators used to control the platform's attitude, such as reaction wheels or magnetorquers, have some limitations, inducing some jitter, which is crucial in small satellites. This jitter induces the image blurring. This work analyzes the impact of actuator's jitter, and then a deblurring technique to minimize and correct the distortion produced is studied. Two points are addressed to restore the blurred images. First, the data coming from the inertial sensors embarked on the satellite to infer the movement of the camera is used. Secondly, a deblurring technique for non-constant blurring over an image is applied. This project has been sponsored by the Spanish Ministry of Science, Innovation and Universities, "Sensing with Pioneering Opportunistic Techniques", grant RTI2018- 099008-B-C21 and by “CommSensLab” Excellence Research Unit Maria de Maeztu (MINECO grant MDM2016-0600), and by ICREA Academia award from the Generalitat de Catalunya, and by “Programa de Formación de Profesorado Universitario” FPU18/06107.

Global Navigation Satellite System Reflectometry and L-band microwave radiometry have been used for soil moisture, biomass, and cryosphere studies. Combining both technologies in a low-power and cost-effective solution could largely improve current Earth observations. 3Cat-4 mission is a 1-Unit CubeSat technology demonstrator of the Flexible Microwave Payload - 1, a reduced size payload that combines these two technologies. This work presents the objectives of the 3Cat-4 mission and the details of the spacecraft architecture and performance. Each spacecraft subsystem is detailed at hardware and software levels. In addition, the presented results indicate that the current spacecraft design will survive the flight conditions (i.e. thermal and structural ones).

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. IEEE Access Best Multimedia Award 2018, pel video de 4'50'' que acompanya l'article: sobre el sistema ONION (a Distributed Satellite Systems (DSS) concept formed by a Walker-Delta constellation formed by 8 heavy nodes including a SAR-X and an optical imager, and 8 small nodes including a GNSS-R payload. ONION provides innovative solutions to complement the current and planned Copernicus offer, targeting relevant and large user communities) The space industry is currently witnessing two concurrent trends: the increased modularity and miniaturization of technologies and the deployment of constellations of distributed satellite systems. As a consequence of the first trend, the relevance of small satellites in line with the “cheaper and faster” philosophy is increasing. The second one opens up completely new horizons by enabling the design of architectures aimed at improving the performance, reliability, and efficiency of current and future space missions. The EU H2020 ONION project (“Operational Network of Individual Observation Nodes”) has leveraged on the concept of Fractionated and Federated Satellite Systems (FFSS) to develop and design innovative mission architectures resulting in a competitive advantage for European Earth Observation (EO) systems. Starting from the analysis of emerging needs in the European EO market, the solutions to meet these needs are identified and characterized by exploring FFSS. In analogy with terrestrial networks, these systems envision the distribution of satellite functionalities amongst multiple cooperating spacecrafts (nodes of a network), possibly independent, and flying on different orbits. FFSS are considered by many as the future of spacebased infrastructures, as they offer a pragmatic, progressive, and scalable approach to improve existing and future space missions. This work summarizes the main results of the ONION project and the high-level design of the Marine Weather Forecast mission for polar regions. Peer Reviewed Award-winning

Satellite architectures where networked, heterogeneous observation nodes capture data in a distributed manner are seen as feasible solutions to address the needs of next-generation Earth observation services (i.e. higher spatial, spectral and temporal resolutions at viable costs). Nevertheless, the problems that designers face when approaching these systems-of-systems are still eclipsed by the heterogeneity, dimensionality and multi-level complexity of those. In spite of the many underlying technological challenges, how to optimally architect distributed satellite systems, remains an open source of debate. In this context, this paper presents a design-oriented methodology that is aimed at providing high-level design solutions for this type of architectures in generic EO use-cases. In order to find optimal solutions, the methodology detailed in this paper is grounded on an aggregated architectural figure-of-merit that compresses: (a) system-level performance metrics; (b) use-case requirements; (c) development and launch costs; and (d) a set of architectural quality attributes. The latter contributing term models, assesses and weights several of the so-called 'ilities' of an architecture and allows to select designs that exhibit some desired qualities. With a dimensionality of more than five thousand architectural alternatives, the study has been illustrated with a marine weather forecast use-case. Both the exploration of design alternatives and the analysis of the results have shown the benefits of medium and small satellite platforms and have stressed their potential in the design of distributed satellite systems. Finally, this paper concludes by suggesting that this very optimization framework and methodology could also be used for a quantitative gap analysis aiming at deriving the technological road map for future engineering teams.

Earth observation satellite programs are currently facing, for some applications, the need to deliver hourly revisit times, subkilometric spatial resolutions, and near-real-time data access times. These stringent requirements, combined with the consolidation of small-satellite platforms and novel distributed architecture approaches, are stressing the need to study the design of new, heterogeneous, and heavily networked satellite systems that can potentially replace or complement traditional space assets. In this context, this paper presents partial results from ONION, a research project devoted to studying distributed satellite systems and their architecting characteristics. A design-oriented framework that allows selecting optimal architectures for the given user needs is presented in this paper. The framework has been used in the study of a strategic use-case and its results are hereby presented. From an initial design space of 5586 potential architectures, the framework has been able to preselect 28 candidate designs by an exhaustive analysis of their performance and by quantifying their quality attributes. This very exploration of architectures and the characteristics of the solution space are presented in this paper along with the selected solution and the results of a detailed performance analysis.