# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 4 Results and Discussion  
### Assessment of the Retrieved HY-2B SMR Snow Depth Based on Different Models  
Based on commonly used remote sensing models for the retrieval of snow depth on sea ice, namely Markus98 [15], Comiso03 [17], and Shen22 [23], this study employed HY-2B SMR data to retrieve the snow depth for Antarctic sea ice. Figure 4 presents the snow depth retrievals based on the three different models, with each model's results indicated by a separate trace (taking 1 February, 14 February, and 28 February in 2009 as examples). Generally, the retrieved snow depth retrieved from HY-2B based on Markus98, Comiso03, and Shen22 models has similar spatial distribution, with thicker snow concentrated in the Weddell Sea and Bellinghausen and Amundsen seas, which are mainly in multi-year ice areas [35]. Due to the melting sea ice season in February, only thin snow existed on the remaining sea ice in the Southern Indian and Southern Pacific oceans. Among the differences in snow depth derived from the three models, the snow depth retrieved from HY-2B SMR data based on the Comiso03 model was relatively thicker than that retrieved using the Shen22 and Markus98 models.  
The retrieved snow depths were compared and validated against ship-based observational snow depths during the 35 th Chinese Antarctic Research Expedition (CHINARE-35), with the assessment results presented in Table 2. Compared to the ship-based snow depth measurements, the retrieved HY-2B SMR snow depth using the three models exhibited a certain negative deviation. This finding is consistent with previous studies that reported an underestimation of snow depth using the AMSR-E brightness temperature data [19; 33]. This underestimation phenomenon is influenced by a myriad of factors, including sensor performance, data processing strategies, and environmental conditions. When employing passive microwave remote sensing techniques to retrieve snow depth on Antarctic sea ice, the physical properties of snow, such as grain size and moisture content, significantly affect the penetration and scattering of microwave signals, posing challenges to the accurate determination of snow depth [36].  
In comparison of the accuracy of snow depth retrieval based on the three models, it was observed that the Comiso03 model exhibited a better performance relative to the Markus98 and Shen22 models, with the smallest MD of \(-1.9\) cm and the lowest MAD and RMSD. Therefore, given its feasibility, we applied the Comiso03 model to retrieve the snow depth on Antarctic sea ice from HY-2B SMR passive microwave data for the entire year of 2019.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MD/cm** & **MAD/cm** & **RMSD/cm** \\ \hline Markus98 & \(-5.6\) & 7.0 & 9.9 \\ Comiso03 & \(-1.9\) & 6.1 & 8.2 \\ Shen22 & \(-3.5\) & 7.0 & 9.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of snow depths retrieved from HY-2B SMR using different snow depth models against ship-based snow depth measurements.  
Figure 4: Spatial distribution of the retrieved HY-2B snow depth based on the [PERSON] model (**a**), Comiso03 model (**b**), and Shen22 model (**c**).