## 1 Introduction
Before Arctic-wide L-band observations from SMOS were available, NNs were used to infer SIT based on SIC maps from satellite radiometry observations at higher bands, and ancillary geophysical parameters, such as surface air temperature and ice drift velocity [25]. The time series of SIC maps were analyzed on the basis of NNs to forecast SIC by assessing the time-varying characteristics of previous observations [26; 27]. Snow depth is an important factor with regard to the inference of SIT from both microwave radiometry and freeboard observations, and its estimation is particularly complicated due to the complexity of the radiation properties of the snow layer. Therefore, regression NN approaches have been developed to invert snow depth using data from multi-frequency satellite microwave radiometer measurements, including the Special Sensor Microwave Imager (SSM/I), the Scanning Multi-channel Microwave Radiometer (SMMR), the Advanced Microwave Scanning Radiometer 2 (AMSR2), and SMOS [28; 29; 30; 31]. NNs are efficient and often easy to implement. They have the advantage to be capable of recognizing the principal forcing mechanisms contained in the non-linear relationships between geophysical parameters, and they can provide similar or better results when compared to those of conventional models. There are only few satellite missions available that are suitable for providing sufficient coverage to retrieve SIT at polar scale. The new Copernicus Imaging Microwave Radiometer (CIMR) mission, which is expected to be launched after 2025, is planned to follow up the SMOS and SMAP missions providing data continuity with at least daily revisit in the polar regions [32; 33]. Today, nanosatellite technology has reached the maturity to perform scientific missions, and the number of deployed instruments has grown steadily over the past decade [34; 35]. CubeSats are miniature satellites that are composed of small unit cubes (U) of 10 \(\times\) 10 \(\times\) 10 cm\({}^{3}\), with a maximum weight of 1.33 kg. Their payloads have an advantage over those carried by traditional missions (i.e., large passive optical and microwave payloads), due to their smaller dimensions in terms of size, mass, power consumption, and downlink capability. Markedly reduced expenses in development, construction, and satellite launch make them more accessible to universities and research institutes, and their use is on the rise to expand the field of applications in Earth observation. Commercial companies, such as 'Planet' and 'Spire', operate constellations of hundreds of 3-U CubeSats (\(\sim\)30 \(\times\) 10 \(\times\) 10 cm\({}^{3}\)), carrying optical imagers or GNSS-Radio Occultation payloads [36; 37]. The FSSCat mission, launched on 3 September 2020, is formed by two federated CubeSats, with one of them (\({}^{3}\)Cat-5/A) carrying the Flexible Microwave Payload-2 (FMPL-2) on board, a combined Reflected Global Navigation Satellite System (GNSS-R) radiometer and the first radiometer operating at L-band ever deployed on a CubeSat [38]. It is designed to provide maps of sea ice extent (SIE) and SIT over both poles on a five-day basis and soil moisture over land at low-moderate resolution, and it is the first CubeSat mission contributing to the Copernicus system (Land and Marine Environment Monitoring Services). Both of the FMPL-2 instruments have been successfully validated in orbit and the first set of nominal acquisitions are available from 1-13 October 2020 [39]. This work investigates predictive regression NN frameworks to infer SIT based on the first FMPL-2 \(T_{B}\) acquisitions provided by the FSSCat mission. To do so, two separate NNs were implemented to generate Arctic and Antarctic SIT maps by combining FMPL-2 \(T_{B}\) data from the FSSCat mission with ancillary maps of SIC from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and skin temperature provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).