## 1 Introduction  
Arctic sea ice reached its annual minimum extent in mid-September 2020, being the second lowest on record [1]. Continuous knowledge regarding the distribution of sea ice is particularly important for polar and mid-latitude climate monitoring [2]. Sea ice volume is an essential climate indicator that is required to understand the balances of mass and surface energy, which can be estimated through observations of sea ice thickness (SIT) and sea ice concentration (SIC). Multi-source data records of annual mean SIT--acquired from on-ice measurements, airborne and satellite-based observations--have proved that both SIT and the amount of multi-year ice in the Arctic have been decreasing by about one-third during the last 40 years [3]. Space-borne microwave radiometry observations over polar areas have been available since 1979, and data at low microwave frequencies can be collected independently of daylight and are widely unaffected by atmospheric conditions. Brightness temperature (T\({}_{B}\)) observations at L-band (\(\sim\)1.4 GHz) are sensitive to thin sea ice up to \(\sim\)0.6 m [4; 5]. SIT retrievals above 1 m have been successfully derived based on sea ice freeboard (Fb) estimates from satellite altimeters [6; 7]. The European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was launched in 2009 carrying on board the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), the first spaceborne L-band interferometric radiometer [8; 9]. MIRAS provides T\({}_{B}\) data at global coverage with a revisit time at the equator of 1-3 days and a spatial resolution of \(\sim\)40 km. Several methods have been developed to estimate the distribution of thin SIT at Arctic scale while using SMOS T\({}_{B}\) data at multiple incidence angles and polarizations [10; 11]. A combined thermodynamic and radiative transfer model has been used to evaluate the variations of ice-physical properties, including temperature and salinity [12; 13]. T\({}_{B}\) polarization differences were combined in growth models, along with empirical assumptions on sea ice properties [14]. A thin SIT product was developed on the basis of retrievals from the SMOS and the Soil Moisture Active Passive (SMAP) missions (also operating at L-band) [15]. ESA's CryoSat-2 mission was launched in 2010 and it provides ice-sheet elevation and Fb estimates from radar altimetry [16]. Since then, several approaches have been implemented to provide maps of Arctic SIT from CryoSat-2 altimetry data [17; 18; 19]. SMOS and CryoSat-2 observations were merged according to their sensitivity ranges and uncertainties in a combined method to generate weekly Arctic full-range SIT [20; 21]. Process-based retrieval algorithms to infer SIT rely on strong model assumptions and empirically determined sea ice properties. These simplifications are required due to sparsely available validation data and limited knowledge on the distribution of sea ice, exhibiting large spatio-temporal variability. Passive microwave sensors often do not capture all of the necessary information about SIT, but they also require ancillary data. The main uncertainties originate from long revisit times and large spatial resolution of satellites. Current SIT products show sufficient accuracy during the Arctic freeze-up period from mid-October to mid-March, but they do not perform well during the Arctic melting season. Machine learning algorithms can often perform better on complex problems for which traditional approaches miss out hidden links between the model parameters among large amounts of data. They have been successfully applied to segment sea ice based on the distribution of surface signatures of satellite observations to recognize patterns of sea ice properties among different scales [22; 23; 24]. Data-driven approaches, such as neural networks (NN), were developed decades ago, and they can adapt to new ice conditions such as changing sea ice types and intermittent periods of freeze up and melting.