## 4 Discussion
Overestimated values are mostly located at the transitions between first-year and multi-year ice, and where high contrasts in SIT due to local variability are given. Therefore, the accuracy of SIT estimations while considering smaller scales is limited by the local heterogeneity of sea ice. The actual resolution of the FMPL-2 observations also depends on the orientation between the flight direction relative to the contrasts in surface structures of sea ice. Thus, it would be feasible to deploy a network of FMPL-2 like sensors in a constellation of CubeSats to shorten the revisit time. The T\({}_{B}\) discontinuities between land and sea ice close to coastal areas can lead to oscillations in the image reconstruction process (Gibbs phenomenon). T\({}_{B}\) observations over land are sensitive to the soil moisture content, and values at L-band for circular-polarized near-nadir observations can range between around 175 K to 275 K [56]. This often results in higher T\({}_{B}\) and an overestimation of SIT in land-sea contaminated areas. The accompanied artifacts can be corrected using a Gibbs algorithm similar to that which is used to derive T\({}_{B}\) maps from SMOS data, but has not yet been implemented in the FMPL-2 T\({}_{B}\) retrievals [57]. Regarding the range and density of values of Arctic training data and Antarctic prediction data, the application of the full-range model to predict Antarctic SIT was considered to be reliable during the study period. Because the Arctic and Antarctic regions represent different environments, accurate models require greater understanding on the temporal variability, sensitivity, and uncertainty contributions of the selected input features with respect to SIT estimations. So far, there is no validated product of Antarctic-wide SIT available using a process-based approach, mainly because these models require information on the physical properties, including snow cover. The signatures of radiometry measurements at L-band are sensitive to the intrinsic ice-physical properties, which change over the course of a year, and the conversion of satellite altimeter observations of Pb to SIT requires accurate knowledge of snow cover. Snow depth has been successfully estimated using approaches that are based on both emission models and NNs [29; 30]. The uncertainty of SIT retrievals is largely determined by the uncertainty of current snow products, and the development of statistics-based models based on a appropriate set of features is promising, but it still remains challenging. Because the seasons of freeze up and melting in the Northern Hemisphere are the opposite of those in the Southern Hemisphere and sea ice is located towards lower latitudes in the Antarctic, it largely consists of first-year ice throughout the year, which is generally shallower than in the Arctic. This is in agreement with the predictions of the full-range model, which result in an average SIT of 1.25 m for the Arctic and 0.67 m for the Antarctic, respectively. In the beginning of the Arctic freeze up, sea ice mainly consists of newly-formed thin ice and thick multi-year ice, agreeing with the range of thicker ice between 2 m and 2.5 m, which was only predicted over the Arctic. Unlike in the Arctic, parts of Antarctic sea ice surface temperatures reach the melting point during the observed period, which limits the reliability of the full-range model predictions for these particular values. In case the period of available training data was longer than the observed two months, the model training set should be periodically updated with new data, once any of the input features of the Antarctic prediction dataset reaches the limits of the training set.