Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks
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 mis...
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Multidisciplinary Digital Publishing Institute
2021
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ftcsic:oai:digital.csic.es:10261/237947 2024-02-11T09:58:33+01:00 Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks Llaveria, David Muñoz-Martín, Joan Francesc Herbert, Christoph Pablos, Miriam Park, Hyuk Camps, Adriano Agencia Estatal de Investigación (España) Ministerio de Ciencia, Innovación y Universidades (España) Ministerio de Educación (España) Generalitat de Catalunya Fundación "la Caixa" European Commission 2021-03 http://hdl.handle.net/10261/237947 https://doi.org/10.3390/rs13061139 https://doi.org/10.13039/501100002809 https://doi.org/10.13039/501100000780 https://doi.org/10.13039/501100011033 en eng Multidisciplinary Digital Publishing Institute #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099008-B-C21 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/ESP2017-89463-C3 info:eu-repo/grantAgreement/EC/H2020/713673 Publisher's version https://doi.org/10.3390/rs13061139 Sí Remote Sensing 13(6): 1139 (2021) CEX2019-000928-S http://hdl.handle.net/10261/237947 doi:10.3390/rs13061139 2072-4292 http://dx.doi.org/10.13039/501100002809 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100011033 open Sea ice Microwave radiometry GNSS-R Nanosatellite Earth observation Neural networks artículo http://purl.org/coar/resource_type/c_6501 2021 ftcsic https://doi.org/10.3390/rs1306113910.13039/50110000280910.13039/50110000078010.13039/501100011033 2024-01-16T11:07:10Z 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 ... Article in Journal/Newspaper Antarc* Antarctic Arctic Sea ice Digital.CSIC (Spanish National Research Council) Antarctic Arctic The Antarctic |
institution |
Open Polar |
collection |
Digital.CSIC (Spanish National Research Council) |
op_collection_id |
ftcsic |
language |
English |
topic |
Sea ice Microwave radiometry GNSS-R Nanosatellite Earth observation Neural networks |
spellingShingle |
Sea ice Microwave radiometry GNSS-R Nanosatellite Earth observation Neural networks Llaveria, David Muñoz-Martín, Joan Francesc Herbert, Christoph Pablos, Miriam Park, Hyuk Camps, Adriano Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks |
topic_facet |
Sea ice Microwave radiometry GNSS-R Nanosatellite Earth observation Neural networks |
description |
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 ... |
author2 |
Agencia Estatal de Investigación (España) Ministerio de Ciencia, Innovación y Universidades (España) Ministerio de Educación (España) Generalitat de Catalunya Fundación "la Caixa" European Commission |
format |
Article in Journal/Newspaper |
author |
Llaveria, David Muñoz-Martín, Joan Francesc Herbert, Christoph Pablos, Miriam Park, Hyuk Camps, Adriano |
author_facet |
Llaveria, David Muñoz-Martín, Joan Francesc Herbert, Christoph Pablos, Miriam Park, Hyuk Camps, Adriano |
author_sort |
Llaveria, David |
title |
Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks |
title_short |
Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks |
title_full |
Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks |
title_fullStr |
Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks |
title_full_unstemmed |
Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks |
title_sort |
sea ice concentration and sea ice extent mapping with l-band microwave radiometry and gnss-r data from the ffscat mission using neural networks |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
http://hdl.handle.net/10261/237947 https://doi.org/10.3390/rs13061139 https://doi.org/10.13039/501100002809 https://doi.org/10.13039/501100000780 https://doi.org/10.13039/501100011033 |
geographic |
Antarctic Arctic The Antarctic |
geographic_facet |
Antarctic Arctic The Antarctic |
genre |
Antarc* Antarctic Arctic Sea ice |
genre_facet |
Antarc* Antarctic Arctic Sea ice |
op_relation |
#PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099008-B-C21 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/ESP2017-89463-C3 info:eu-repo/grantAgreement/EC/H2020/713673 Publisher's version https://doi.org/10.3390/rs13061139 Sí Remote Sensing 13(6): 1139 (2021) CEX2019-000928-S http://hdl.handle.net/10261/237947 doi:10.3390/rs13061139 2072-4292 http://dx.doi.org/10.13039/501100002809 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100011033 |
op_rights |
open |
op_doi |
https://doi.org/10.3390/rs1306113910.13039/50110000280910.13039/50110000078010.13039/501100011033 |
_version_ |
1790594224057483264 |