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

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 S 3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. T...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: David Llaveria, Juan Francesc Munoz-Martin, Christoph Herbert, Miriam Pablos, Hyuk Park, Adriano Camps
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
sea ice
microwave radiometry
GNSS-R
nanosatellite
earth observation
neural networks
Science
Q
Arctic
Antarctic
The Antarctic
Antarc*
Sea ice
Online Access:https://doi.org/10.3390/rs13061139
https://doaj.org/article/2706422e43f64cf5a0ad8b23f6b2a34a
Description
Summary: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 S 3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3 Cat-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.

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