# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 5 Conclusions  
The investigation of retrieving snow depth on Antarctic sea ice using passive microwave data is of significant importance for observing Antarctic sea ice and snow variations in the context of global warming. In this study, we explored the retrieval of snow depth on Antarctic sea ice using HY-2B SMR data based on three snow depth retrieval models (the [PERSON] model, Comiso03 model, and Shen22 model). The accuracy of these models was verified using the ship-based snow depth observational data of the Chinese 35 th Antarctic Scientific Expedition. The retrieved HY-2B SMR snow depths were com  
Figure 7: Time-series of snow depth on Antarctic sea ice and its various sea areas derived from HY-2B SMR data in 2019. The red solid line is the daily HY-2B SMR snow depth and the blue dashed line is the monthly average HY-2B SMR snow depth. pared with the GCOM-W1 AMSR-2 snow depth product, followed by an analysis of the spatiotemporal distribution of snow depth on Antarctic sea ice in 2019. The utilization of passive microwave remote sensing data from HY-2B SMR demonstrates a certain feasibility in retrieving snow depths on Antarctic sea ice. Compared to the Markus98 and Shen22 models, the Comiso03 model produces a relatively better performance for snow depth retrieval on Antarctic sea ice. There is good consistency between the snow depth derived from HY-2B SMR and the GCOM-W1 AMSR-2 snow depth product. However, notable differences primarily occur during the melting sea ice season, with a deviation of 5.0 cm. The spatial differences mainly concentrate in the Bellinghausen and Amundsen seas and the Southern Pacific Ocean. Overall, the snow depth on Antarctic sea ice derived from HY-2B SMR tends to be higher than the GCOM-W1 AMSR-2 product, which mitigates the underestimation phenomenon for the retrieval of snow depth on sea ice from passive microwave data in previous studies. In 2019, the monthly snow depth on Antarctic sea ice reached its peak in January (36.2 cm) and decreased to its lowest in May (15.3 cm). Snow depth on Antarctic sea ice is influenced by both seasonal and geographical regions. Thicker snow cover throughout the year was primarily distributed in the Weddell, Bellinghausen, Amundsen, and Ross seas, while thinner snow on sea ice was found in the Southern Indian Ocean and the Southern Pacific Ocean. This study presents a potential possibility for applying HY-2B SMR data to retrieve snow depths on Antarctic sea ice, which is significant for observing the long-term variations of Antarctic sea ice and its surface snow. It should be noted that the preferred Comiso03 model for HY-2B SMR data in this study is also the retrieval model used in the AMSR-2 snow depth product released by the NSIDC. However, it should be underscored that the AMSR-2 sensor aboard the GCOM-W1 satellite, while instrumental to the snow depth retrieval, has been operational for over 12 years, exceeding the designed lifespan of the satellite, which may impact the reliability of its data for long-term continuous observing purposes. Additionally, the retrieval capability of the Comiso03 model is limited to deep snow (greater than 60 cm). Thus, the snow depth retrieval model for the passive microwave data still needs to be improved. In future work, it is recommended to explore the construction of a novel model for retrieving snow depths on Antarctic sea ice using HY-2B satellite data both from high and low-frequency channels, which have better penetration capabilities for detecting thick snow. Additionally, the deep learning techniques could be applied to construct a more efficient and accurate snow depth retrieval model. This would provide long-term information on the snow depth on Antarctic sea ice, thereby supporting continuous observing of Antarctic sea ice and its surface snow changes in the context of global warming. Conceptualization, Q.J; methodology, Q.J. and N.L. ; software, M.Y. and Z.X. ; analysis, Q.J. and X.P. ; writing, Q.J. and N.L. ; visualization, Z.Z. and M.Y. ; and funding acquisition, Q.J.