Topography-Enhanced Multilevel Residual Attention U-Net Model for Sea Ice Concentration Spatial Super-Resolution Prediction

High-resolution (HR) sea ice concentration (SIC) is essential for polar ocean monitoring and Arctic research. Super-resolution (SR) techniques improve the spatial resolution of SIC data, but existing methods often fail to capture fine-scale structures, perform poorly at ice edges, and lack accuracy near sea-land boundaries. To address these challenges, we propose a Topography-Enhanced Multi-Level Residual Attention U-Net (TE-MRAU-Net) for SIC downscaling. To address these challenges, this paper proposes a topography-enhanced multi-level residual self-attention U-Net downscaling model (TE-MRAU-Net). TE-MRAU-Net integrates three innovative modules: the HR Topography Feature (HRTF) module, which introduces static topographic constraints to effectively improve reconstruction accuracy along sea-land boundaries; the Multi-Level Residual (MLRes) module, which enhances the model's ability to extract fine-scale sea ice features in super-resolution predictions; and the Spatial Attention Connector (SAC) module, which strengthens spatial modeling and structural consistency, particularly improving reconstruction performance in marginal sea ice edges and lower-latitude Arctic regions. Experimental results indicate that, in the pan-Arctic region, TE-MRAU-Net outperforms other models in various evaluation metrics and spatial distribution reconstruction. For the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5\times$</tex-math></inline-formula> SR prediction task, the model achieves a mean absolute error (MAE) of 0.0049, a root mean square error (RMSE) of 0.0081, a correlation coefficient (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula>) of 0.9993, a peak signal-to-noise ratio (PSNR) of 40.21dB, a structural similarity index measure (SSIM) of 0.9764, a balanced accuracy (BACC) of 0.9428, a mean opinion score (MOS) of 4.63, and a subjective image quality assessment (SIQA) of 9.75, greatly enhancing the model's ability to capture detailed SIC features.