Sea ice is vital in the global climate system, ecological balance and polar navigation. Arctic sea ice concentration (SIC) exhibits significant spatial heterogeneity and complex evolutionary patterns. In response to address these challenges, this study proposes a predictive model named sea ice concentration U-Net (SICUNet). SICUNet is a data-driven U-Net model that integrates attention mechanisms and residual structures for short-term prediction of SIC in the Arctic region.

The distance-weighted averaging method was used to calculate the along-orbit sea surface height (SSH) wavenumber spectra of four satellites and to evaluate the along-track resolution capability of the four satellites. The results show that the resolution of Surface Water and Ocean Topography (SWOT) in the Kuroshio region is 25 km, which is twice the resolution of conventional satellites. A parameter was defined using the cross-power-spectrum approach and used to analyse the global ocean.

Our research focuses on using machine learning to enhance the accuracy and efficiency of bathymetric models. In this paper, a multi-layer perceptron (MLP) neural network is used to integrate multi-source marine geodetic data. And a new bathymetric model of the global ocean, spanning 0–360° E and 80° S–80° N, known as the Shandong University of Science and Technology 2023 Bathymetric Chart of the Oceans (SDUST2023BCO), has been constructed, with a grid size of 1′ × 1′.

Seafloor topography, as fundamental geoinformation in marine surveying and mapping, plays a crucial role in numerous scientific studies. In this paper, we focus on constructing a high-precision seafloor topography and bathymetry model for the Philippine Sea (5° N–35° N, 120° E–150° E), based on shipborne bathymetric data and marine gravity anomalies, and evaluate the reliability of the model's accuracy.

We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.