## 3 Results
### Prediction of Arctic and Antarctic SIT  
The performance of the optimal fits was evaluated after applying the NN models to the unknown Arctic test set. The prediction error, the distribution of the MAE with SIT, and the relation between predicted and ground truth SIT are displayed in the Figure 6b-d for thin SIT. This model performs well with the error being widely unbiased up to a SIT of 0.4 m, but it underestimates the values for higher SIT. Predicted values deviate more from ground truth values with increasing SIT, until reaching a maximum error of around 9.5 cm at a SIT of approximately 30 cm. For predictions larger than 0.5 m, the MAE again shows lower values. This could be explained, because the model generally contains a small bias towards higher values and ground truth values beyond 0.6 m were filtered beforehand. In Figure 7, two estimated weekly composite maps that are based on the thin SIT model are compared to the corresponding SIT\({}_{SMOS}\) maps in the periods from 15-21 October and from 12-18 November 2020. In mid-October, sea ice mainly consisted of the remaining thick multi-year ice and regions of newly-formed thin ice were observed around the Beaufort Sea. In this period, only a small amount of under- and overestimated values are present. Positive deviations can be attributed to most-recently formed thin ice. Until mid-November, freeze-up had already advanced in the Arctic ocean, and thin sea ice below 0.6 m became more abundant. The increasing amount of newly-formed thin ice in the East Siberian Sea and the Laptev Sea, which is visible in the SIT\({}_{SMOS}\) product, is in agreement with the values that were obtained from the NN model. In this period, the number of underestimated values increases as sea ice gets thicker. The deviations between the ground truth and the predicted values reveal that underestimated values are located around the 0.6 m threshold, where the model range is limited. They can be related to areas in the Beaufort Sea and the Greenland Sea, where sea ice started to thicken beyond the sensitivity range of T\({}_{B}\) observations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Training** & **Instances** & **Layers** & **Neurons** & **Batch Size** & **Patience** & **Epochs** \\ \hline
**Thin ice model** & 348,009 & 2 & 64 & 1024 & 30 epochs & 198 \\
**Full-range model** & 63,330 & 3 & 64 & 1024 & 40 epochs & 353 \\ \hline
**Prediction** & **Thin Ice Model** & \multicolumn{5}{c}{**Full-Range Model**} \\ \hline
**SIT range [m]** & 0–0.6 & 0–0.5 & 0.5–1.5 & 1.5–2.5 & 0–2.5 \\
**MAE [m]** & 0.065 & 0.160 & 0.275 & 0.149 & 0.237 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of training (top) and prediction (bottom) characteristics corresponding to the thin and full-range SIT models. The training characteristics comprise the model architecture and the adjusted hyperparameters for the optimal fit, including the total trained instances, the number of hidden layers and neurons per layer, batch size, patience interval for Early Stopping, and the number of trained epochs. Regarding the predictions, the obtained Mean Absolute Error (MAE) values are indicated for specific SIT ranges. Figure 8b-d show the evaluation of the full-range SIT model. The model performs well for thin (<0.5 m) and for thicker ice (>1.5 m), in which a substantial amount of values is given. These include the individual sub-ranges, where T\({}_{B}\) and altimetry observations are known to be more sensitive to SIT, and where uncertainties of the SIT\({}_{SMOS}\) and the  
Figure 6: Evaluation of training and prediction of the thin SIT model. (**a**) Validation and training error determined after each epoch; early stopping occurred at 198 iterations and the model resulted in a MAE of 0.065 m; (**b**) prediction error after application of the model to the entire Arctic dataset; (**c**) distribution of the MAE with SIT; and, (**d**) relation between predicted and ground truth SIT\({}_{SMOS}\).