## 4 Discussion  
This work has presented the first results over polar areas using the FMPL-2 T\({}_{B}\) observations of the FSSCat mission to predict SIT based on NN approaches. The thin SIT model performs well for sea ice up to \(\sim\)0.5 m. Above 0.5 m--where T\({}_{B}\) is less sensitive to SIT--the values are notably underestimated, and the network is not capable of inferring SIT at higher range from the input feature interactions [5]. The retrieval algorithm of the corresponding SIT\({}_{SMOS}\) product used as ground truth depends on the distribution of the physical temperature of sea ice and performs better for cold conditions [12]. This may contribute to the observed deviations between the model prediction and ground truth data for higher SIT. To extend the sensitivity range of the thin model, the input features are complemented with sea ice Rb observations from the Cryostat-2 mission to yield full-range  
Figure 10: Application of the Arctic full-range SIT model to Antarctic data. (**a**) Distribution of the predicted SIT values; (**b**) Monthly composite of Antarctic full-range SIT from 15 October to 14 November 2020. Figure 9: Comparison between the weekly composites of predicted Arctic full-range SIT maps (**a**) and the corresponding ground truth SIT\({}_{CS2 SMOS}\) product (**b**), including their deviations (**c**), from 22–28 October 2020. SIT, using the SIT\({}_{CS2 SMOS}\) product as ground truth data. The full-range model performs well for values above 1.5 m and for thin SIT. However, between 0.5 m and 1.5 m, a gap of higher errors of magnitudes around 0.25 m to 0.3 m remains. The uncertainty of the merged SIT\({}_{CS2 SMOS}\) product is a combination of those of the original SIT maps that are derived from SMOS (SIT\({}_{SMOS}\)) and CryoSat-2 (SIT\({}_{CS2}\)) [21]. The observed errors of the model predictions in the intermediate SIT range between 0.5 m and 1.5 m are in agreement with those of the SIT\({}_{SMOS}\) and SIT\({}_{CS2}\) products, revealing high relative uncertainties between 25% and 75% in the same SIT range. Thus, the higher uncertainty of the SIT\({}_{CS2 SMOS}\) product at the mostly interpolated intermediate SIT range may affect the learning process during model training and it limits the prediction accuracy of the network at that particular range. Instead of using the interpolated SIT\({}_{CS2 SMOS}\) product as a single target variable in the output layer of the network, more consistent predictions may be obtained by using SIT\({}_{SMOS}\) and SIT\({}_{CS2}\) data as two separate variables. The two predicted outputs could be interpolated a posteriori according to their individual error distributions. A conceivable solution to generally overcome the challenge of the remaining sensitivity gap--which is not covered by the altimetry measurements and the radiometry observations at L-band--will be to introduce another feature to the training set. Radiometry observations at P-band (\(\sim\)500 MHz), as indicated in Figure 2b in Section 'Data and Methods', can complement the lack of sensitivity in the intermediate range. It can provide the necessary information content to the network to overcome the limitations of both the saturation of T\({}_{B}\) for higher SIT and the predominantly high uncertainties of sea ice Pb corresponding to small SIT values. Although ancillary data at higher resolution are added to reveal information of sea ice at smaller scale, the large radiometer footprint of \(\sim\)350 \(\times\) 500 km\({}^{2}\) smoothes out the observations and limits the detectable small-scale features of the SIT. Large-scale averaging overall reduces the standard deviation and the dynamic range of the T\({}_{B}\) observations. Predictions of the full-range model are currently underestimated at areas of predominately thicker and more heterogeneous multi-year ice, where T\({}_{B}\) observations at L-band are already saturated.