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Article

Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

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CommSensLab Unidad María de Maeztu—Department of Signal Theory and Communications, Universitat Politècnica de Catalunya and IEEC/CTE-UPC, 08034 Barcelona, Spain
2
Physical and Technological Oceanography Group, Institut de Ciències del Mar, Consejo Superior de Investigaciones Científicas (ICM-CSIC), 08003 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: José Darrozes
Remote Sens. 2021, 13(6), 1139; https://doi.org/10.3390/rs13061139
Received: 6 February 2021 / Revised: 14 March 2021 / Accepted: 14 March 2021 / Published: 17 March 2021
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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. View Full-Text
Keywords: sea ice; microwave radiometry; GNSS-R; nanosatellite; earth observation; neural networks sea ice; microwave radiometry; GNSS-R; nanosatellite; earth observation; neural networks
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MDPI and ACS Style

Llaveria, D.; Munoz-Martin, J.F.; Herbert, C.; Pablos, M.; Park, H.; Camps, A. Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks. Remote Sens. 2021, 13, 1139. https://doi.org/10.3390/rs13061139

AMA Style

Llaveria D, Munoz-Martin JF, Herbert C, Pablos M, Park H, Camps A. Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks. Remote Sensing. 2021; 13(6):1139. https://doi.org/10.3390/rs13061139

Chicago/Turabian Style

Llaveria, David, Juan Francesc Munoz-Martin, Christoph Herbert, Miriam Pablos, Hyuk Park, and Adriano Camps. 2021. "Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks" Remote Sensing 13, no. 6: 1139. https://doi.org/10.3390/rs13061139

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