## 5 Conclusions  
Polar regions are environments with strong seasonal dynamics and spatial heterogeneity, which are difficult to be adequately addressed using process-based or simple regression models. This work has presented a new approach focusing on the retrieval of SIT maps based on predictive regression neural networks. Two independent models have been implemented and trained with Arctic data to yield maps of Arctic thin SIT and full-range SIT. Information regarding non-linearly related sea ice parameters is contained in hidden relationships between a selected number of observations. These relationships were considered in an additional number of hidden layers of a NN model. The model input features comprise of the FMPL-2 T\({}_{B}\) observations that were provided by the FSSCat mission launched on 3 September 2020, which carries the FMPL-2 payload with the first GNSS-Reflectometer and L-band microwave radiometer on board a CubeSat, and ancillary data, including SIC and surface temperature maps. A thin SIT model was implemented using the SIT\({}_{SMOS}\) product as ground truth data and targets SIT values up to 0.6 m, being limited by the sensitivity of L-band T\({}_{B}\) observations. Adding complementary information of CryoSat-2 sea ice Rb data to the existing input features allowed us to extend the sensitivity range to values that are larger than those covered by the thin SIT model. This enabled implementing a second model yielding full-range SIT, which was evaluated using maps of the merged SIT\({}_{CS25 MOS}\) product as ground truth data. The input features were processed and both models were trained on Arctic data during early Arctic freeze up from 15 October to 4 December 2020. A number of hyperparameters was adjusted to prevent the models from data overfitting to obtain the optimal fit through the minimization of the MAE cost function. The thin ice model shows good performance with an overall MAE of 0.065 m and it generalizes well up to a SIT of 0.5 m, while underestimating for higher SIT values. The best fit of the full-range model results in a MAE of 0.237 m, and the predictions match well for thin ice and SIT above 1.5 m. Hereby, the main error contribution originates from predicted values in the intermediate SIT range. Major losses may be attributed to an existing sensitivity gap of the sensors or to the limited availability of sufficient values for training during the observed period. The predictive models allowed for producing weekly composite maps of thin and full-range SIT at the Arctic scale. In a first attempt, the full-range model that was obtained from Arctic observations was also applied to Antarctic data to produce monthly composites of Antarctic SIT. It is important to note that a disseminated product of full-range SIT maps at Antarctic scale was not available for comparison during the observed period. Neural networks have the advantage to be adaptive to new datasets and models are able to capture variable sea ice conditions and changing the relationships of the input variables when considering longer learning periods. Future tasks encompass the application of the methodology to additional data to better pinpoint limitations and evaluate model performance on regional scale. This work verified the success of the FSSCat mission and the potential of applying FMPL-2 T\({}_{B}\) to estimate SIT over polar areas using data from a single CubeSat. Using a constellation of CubeSats of the same kind effectively increases the satellite revisit and the spatial resolution of T\({}_{B}\) maps, and has the potential to improve model accuracy. These constellations would demonstrate a feasible moderate-cost alternative to complement large satellite mission or to substitute them during gaps of non-existent data, in order to guarantee the continuous monitoring of polar sea ice at both hemispheres. Conceptualization, C.H., J.F.M.-M., D.L. and A.C.; Data curation, C.H., J.F.M.-M. and M.P. ; Investigation, C.H. ; Methodology, C.H., J.F.M.-M. and D.L.