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

Abstract

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.

Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers

“…Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 28 accuracy of RF and SVM classifiers for OW-sea ice classification is 98.83% and 98.60% respectively, which are comparable to the results in previous studies [15,38,40]. Similarly, the FYI-MYI classification results are evaluated using 20,000 FYI samples and 240,967 MYI samples (Figure 11c,d), which results in an overall accuracy of 84.82% for RF, whereas 71.71% for SVM.…”

“…The RF aided method can discriminate sea ice from water with a success rate up to 98.03% validated with the collocated sea ice edge maps from the special sensor microwave imager sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). Llaveria et al [40] applied the NN algorithm for sea ice concentration and sea ice extent sensing using GNSS-R data from the FFSCat mission [40]. Rodriguez-Alvarez et al [30] initially exploited the implementation of the classification and regression tree (CART) algorithm for sea ice classification using GNSS-R observables derived from GNSS-R DDM.…”

Sea Surface Salinity and Wind Speed Retrievals Using GNSS-R and L-Band Microwave Radiometry Data from FMPL-2 Onboard the FSSCat Mission

“…The instrument aimed at providing coarse soil moisture over land, and sea ice concentration/extension and thickness maps over the poles. Data collected from 1 October 2020 to 4 December 2020 have been used in this study to validate the capability of FMPL-2 to retrieve sea ice concentration and extension [17], sea ice thickness [18], and soil moisture [19]. One of the key aspects of FMPL-2 algorithms is the use of machine learning techniques and, in particular, Artificial Neural Networks (ANN) to retrieve these ECVs.…”

“…One of the key aspects of FMPL-2 algorithms is the use of machine learning techniques and, in particular, Artificial Neural Networks (ANN) to retrieve these ECVs. Moreover, as presented in [17,19], the use of combined GNSS-R and L-band MWR measurements as input features of an ANN algorithm provides the best sea ice and soil moisture estimates.…”

Sea-Ice Permittivity Derived From GNSS Reflection Profiles: Results of the MOSAiC Expedition

“…Sea-ice extent and concentration can be monitored on a global scale using scanning-mode microwave radiometers [8]. Also GNSS reflectometry algorithms provide scatterometric products to determine sea-ice extent [9], [10] or they contribute to radiometer observations [11]. New findings on reflectometry-based retrievals of sea-ice parameter can foster application as proposed, for example, in the G-TERN mission concept [12].…”