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Thermodynamic parameters play a crucial role in determining polar sea ice thickness (SIT); however, modeling their relationship is difficult due to the complexity of the influencing mechanisms. In this study, we propose a self-attention convolutional neural network (SAC-Net), which aims to model the relationship between thermodynamic parameters and SIT more parsimoniously, allowing us to estimate SIT directly from these parameters. SAC-Net uses a fully convolutional network as a baseline model to detect the spatial information of the thermodynamic parameters. Furthermore, a self-attention block is introduced to enhance the correlation among features. SAC-Net was trained on a dataset of SIT observations and thermodynamic data from the 2012–2019 freeze-up period, including surface upward sensible heat flux, surface upward latent heat flux, 2 m temperature, skin temperature, and surface snow temperature. The results show that our neural network model outperforms two thermodynamic-based SIT products in terms of accuracy and can provide reliable estimates of SIT. This study demonstrates the potential of the neural network to provide accurate and automated predictions of Arctic winter SIT from thermodynamic data, and, thus, the network can be used to support decision-making in certain fields, such as polar shipping, environmental protection, and climate science. [ABSTRACT FROM AUTHOR]

Sea ice leads in the Arctic Ocean and Antarctic Ocean are of great significance to polar ecology, climate change, and ship navigation. While rule-based remote-sensing classification methods, such as the threshold method, have been widely used for surveying and mapping polar sea ice leads, they have difficulty in overcoming the problems of noise and poor generalization. In this article, we presented an automated, deep learning sea ice open lead detection network in low wind speed conditions, entropy-weighted network (EW-Net). In EW-Net, a dense block was introduced into a U-Net baseline network to strengthen feature propagation, and an entropy sampling structure was designed to improve the pertinence of the network to leads and alleviate the influence of noise. Furthermore, an entropy-weighted feature fusion block was designed to better restore features. EW-Net was trained on the Sentinel-1 synthetic aperture radar (SAR) dataset in order to obtain lead maps of different polar regions from Sentinel-1 images. The results showed that the proposed EW-Net model was more effective and more generalizable for polar sea ice lead detection than the threshold method and the deep learning methods U-Net and DeepLab V3 Plus, with an overall accuracy (OA) exceeding 0.97. In addition, EW-Net showed advantages in terms of outstanding generalization from cross-sensor image monitoring of leads. The method proposed in this article could be beneficial for providing accurate maps of polar sea ice leads in open water and contribute to further research on polar science. [ABSTRACT FROM AUTHOR]

ICESat-2 altimeter data could be used to estimate sea ice freeboard and thickness values with a higher measuring accuracy than that achievable with data provided by previous altimeter satellites. This study developed an improved algorithm considering variable lead proportions based on the lowest point method to derive the sea surface height for the retrieval of Antarctic sea ice freeboard and thickness values from ICESat-2 ATL-07 data. We first collocated ICESat-2 tracks to corresponding Sentinel-1 SAR images and calculated lead (seawater) proportions along each track to estimate the sea surface height in the Antarctic Ocean. Then, the Antarctic sea ice freeboard and thickness were estimated based on a local sea surface height reference and a static equilibrium equation. Finally, we assessed the accuracy of our improved algorithm and ICESat-2 data product in the retrieval of the Antarctic sea ice thickness by comparing the calculated values to ship-based observational sea ice thickness values acquired during the 35th Chinese Antarctic Research Expedition (CHINARE-35). The results indicate that the Antarctic sea ice freeboard estimated with the improved lowest point method was slightly larger than that estimated with the ICESat-2 data product algorithm. The root mean squared error (RMSE) of the improved lowest point method was 35 cm with the CHINARE-35 measured sea ice thickness, which was smaller than that determined with the ICESat-2 data product algorithm (65 cm). Our improved algorithm could provide more accurate data on the Antarctic sea ice freeboard and thickness, thus supporting Antarctic sea ice monitoring and the evaluation of its change under global effects. [ABSTRACT FROM AUTHOR]