Microwave radiometry at L-band is sensitive to sea ice thickness (SIT) up to ~ 60 cm. Current methods to infer SIT depend on ice-physical properties and data provided by the ESA's Soil Moisture and Ocean Salinity (SMOS) mission. However, retrieval accuracy is limited due to seasonally and regionally variable surface conditions during the formation and melting of sea ice. In this work, Arctic sea ice is segmented using a Bayesian unsupervised learning algorithm aiming to recognize spatial patterns by harnessing multi-incidence angle brightness temperature observations. The approach considers both statistical characteristics and spatial correlations of the observations. The temporal stability and separability of classes are analyzed to distinguish ambiguous from well-determined regions. Model uncertainty is quantified from class membership probabilities using information entropy. The presented approach opens up a new scope to improve current SIT retrieval algorithms, and can be particularly beneficial to investigate merged satellite products. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.713673. It was also funded through the award "Unidad de Excelencia María de Maeztu" MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project "L-band" ESP2017-89463-C3-2-R, and the project "Sensing with Pioneering Opportunistic Techniques (SPOT)" RTI2018-099008-B-C21/AEI/10.13039/501100011033. ; Peer Reviewed ; Postprint (published version)
The Earth Observation community is demanding new satellite applications that cover the need of monitoring different areas with high spatial resolution and short revisit times. These applications will generate huge amounts of data, and thus improvements in the downlink capacity are mandatory. Distributed Satellite Systems have emerged as a moderate-risk and cost-effective solution to meet these new requirements. These systems are groups of satellites that share a global and common objective. One of these systems are the Federated Satellite Systems, which rely on the collaboration between satellites that share unused resources, such as memory storage, computing capabilities, or downlink opportunities. In the same context, the Internet of Satellites paradigm expands the FSS concept to a multi-hop scenario, without predefining a satellite system architecture, and deploying temporal satellite networks. The basis of both concepts is the offer of unused satellite resources as services, being necessary that satellites notify their availability to other satellites that composes the system. This work presents the Opportunistic Service Avaliability Dissemination Protocol, which allows a satellite to publish an available service to be consumed by others. Details of the protocol behavior, and packet formats are presented as part of the protocol definition. Additionally, without loss of generality, the protocol has been verified in a realistic scenario composed of Earth Observation satellites, and the Telesat mega-constellation as a network backbone. The achieved results demonstrate the benefits of using the proposed protocol by doubling the downloaded data in some cases. ; This work was supported in part by the ''CommSensLab'' Excellence Research Unit Maria de Maeztu Ministerio de asuntos Económicos y transformación digital (MINECO) under Grant MDM-2016-0600; in part by the Spanish Ministerio de Ciencia e Innovación (MICINN) and European Union - European Regional Development Fund (EU ERDF) project ''Sensing with pioneering opportunistic techniques'' under Grant RTI2018-099008-B-C21; in part by the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR)—Generalitat de Catalunya (FEDER) under Grant FI-DGR 2015; and in part by the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya under Grant 2017 SGR 376 and Grant 2017 SGR 219. ; Peer Reviewed ; Postprint (published version)
Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide TB data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 TB and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation. ; This work was supported by 2017 ESA S3 challenge and Copernicus Masters overall winner award ("FSSCat" project), and has been (partially) sponsored by the 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). Christoph Herbert receives support 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; 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; David Llavería receives support from a FPU fellowship from the Spanish Ministry of Education FPU18/06107. ; Peer Reviewed ; Postprint (published version)
Presently, the Earth Observation community is demanding applications that provide low latency and high downlink capabilities. An increase in downlink contacts becomes essential to meet these new requirements. The Federated Satellite Systems concept addresses this demand by promoting satellite collaborations to share unused downlink opportunities. These collaborations are established opportunistically and temporarily, posing multiple technology challenges to be implemented in-orbit. This work contributes to the definition of the Federation Deployment Control Protocol which formalizes a mechanism to fairly establish and manage these collaborations by employing a negotiation process between the satellites. Moreover, this manuscript presents the results of a validation campaign of this protocol with three stratospheric balloons. In summary, more than 27 federations with 63.0% of throughput were established during the field campaign. Some of these federations were used to download data to the ground, and others were established to balance data storage between balloons. These federations allowed also the extension of the coverage of a ground station with a federation that relayed data through a balloon, and the achievement of a hybrid scenario with one balloon forwarding data from a ground device. The results demonstrate that the proposed protocol is functional and ready to be embedded in a CubeSat mission. ; This work has been (partially) funded by "CommSensLab" Excellence Research Unit Maria de Maeztu (MINECO grant MDM-2016-0600), the Spanish Ministerio MICINN and EU ERDF project "SPOT: Sensing with pioneering opportunistic techniques" (grant RTI2018-099008-BC21/AEI/10.13039/501100011033), by the grant PID2019-106808RA-I00/AEI/FEDER/UE from the EDRF and the Spanish Government, and by the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya (2017 SGR 376, and 2017 SGR 219). ; Peer Reviewed ; Postprint (published version)
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. ; Peer Reviewed ; Postprint (published version)
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° N). 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. ; Peer Reviewed ; Postprint (published version)
The Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) created the GRSS "Standards for Earth Observation Technical Committee" to advance the usability of remote sensing products by experts from academia, industry, and government through the creation and promotion of standards and best practices. In February 2019, a Project Authorization Request was approved by the IEEE Standards Association (IEEE-SA) with the title "Standard for Spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) Data and Metadata Content." At present, 4 GNSS constellations cover the Earth with their navigation signals: The United States of America (USA) Global Positioning System GPS with 31 Medium Earth Orbit (MEO) operational satellites, the Russian GLObal'naya NAvigatsionnaya Sputnikovaya Sistema GLONASS with 24 MEO operational satellites, the European Galileo with 24 MEO operational satellites, and the Chinese BeiDou-3 with 3 Inclined GeoSynchronous Orbit (IGSO), 24 MEO, and 2 Geosynchronous Equatorial Orbit (GEO) operational satellites. Additionally, several regional navigation constellations increase the number of available signals for remote sensing purposes: the Japanese Quasi-Zenith Satellite System QZSS with 1 GSO and 3 Tundra-type orbit operational satellites, and the Indian Regional Navigation Satellite System IRNSS with 3 GEO and 4 IGSO operational satellites. On the other hand, there are different GNSS-R processing techniques, instruments and spaceborne missions, and a wide variety of retrieval algorithms have been used. The heterogeneous nature of these signals of opportunity as well as the numerous working methodologies justify the need of a standard to further advance in the development of GNSS-R towards an operational Earth Observation technique. In particular, the scope of this working group is to develop a standard for data and metadata content arising from past, present, and future spaceborne missions such as the United Kingdom (UK) TechDemoSat-1 TDS-1, and the National Aeronautics and Space Administration (NASA) CYclone Global Navigation Satellite System CYGNSS constellation coordinated by the University of Michigan (UM). In this article we describe the scene study, including fundamental aspects, scientific applications, and historical milestones. The spaceborne standard is under development and it will be published in IEEE-SA. ; This work was supported in part by the National Aeronautics and Space Administration (NASA) Science Mission Directorate with the University of Michigan under Contract NNL13AQ00C. ; Peer Reviewed ; Article signat per 25 autors/es: Hugo Carreno-Luengo, University of Michigan (UM), Ann Arbor, MI, USA / Adriano Camps, CommSensLab-UPC, Universitat Politecnica de Catalunya (UPC), Barcelona, Spain / Chris Ruf, University of Michigan (UM), Ann Arbor, MI, USA / Nicolas Floury, European Space Research and Technology Center (ESTEC), European Space Agency (ESA), Noordwijk, The Netherlands / Manuel Martin-Neira, European Space Research and Technology Center (ESTEC), European Space Agency (ESA), Noordwijk, The Netherlands / Tianlin Wang, University of Michigan (UM), Ann Arbor, MI, USA / Siri Jodha Khalsa, National Snow and Ice Data Center (NSIDC), University of Colorado, Boulder, CO, USA / Maria Paola Clarizia, Deimos Space UK, Didcot, U.K. / Jennifer Reynolds, Deimos Space UK, Didcot, U.K. / Joel Johnson, Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA / Andrew O'Brien, Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA / Carmela Galdi, Universita degli Studi del Sannio, Benevento, Italy / Maurizio Di Bisceglie, Universita degli Studi del Sannio, Benevento, Italy / Andreas Dielacher, RUAG Space GmbH, Vienna, Austria / Philip Jales, Spire Global, Boulder, CO, USA / Martin Unwin, Surrey Satellite Technology Ltd. (SSTL), Guildford, U.K. / Lucinda King, Surrey Space Centre, University of Surrey, Guildford, U.K. / Giuseppe Foti, National Oceanography Center (NOC), Southampton, U.K. / Rashmi Shah, Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA, USA / Daniel Pascual, Deimos Space UK, Didcot, U.K. / Bill Schreiner, University Corporation for Atmospheric Research (UCAR), Boulder, CO, USA / Milad Asgarimehr, German Research Centre for Geosciences (GFZ), Potsdam, Germany / Jens Wickert, German Research Centre for Geosciences (GFZ), Potsdam, Germany, Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, Berlin, Germany / Serni Ribo, Instiut d'Estudis Espacials de Catalunya (IEEC), Barcelona, Spain, Institute of Space Sciences (ICE), Barcelona, Spain / Estel Cardellach, Instiut d'Estudis Espacials de Catalunya (IEEC), Barcelona, Spain, Institute of Space Sciences (ICE), Barcelona, Spain. ; Postprint (published version)
