The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics.

The propagation and scattering of vortex light beams in complex media have significant implications in the fields of laser imaging, optical manipulation, and communication. This paper investigates the scattering characteristics of vortex light beams from a random rough surface. Firstly, a two-dimensional Gaussian rough surface is generated using the Monte Carlo method combined with the linear filtering method. Subsequently, the vortex beams are decomposed into the superposition of infinite plane waves, and the scattering of each plane wave from the rough surface is calculated using the Kirchhoff Approximation method. Numerical results of the angle distribution and spatial distribution of OAM scattering Laser Radar Cross Section (LRCS) are presented, varying with different surface roughness parameters for a rough aluminum surface and the beam’s parameters. The results demonstrate that the scattering of vortex beams is influenced by the beam’s parameters, such as Orbital Angular Momentum (OAM) mode number and elevation angle, which may bring new insights into vortex wave-matter interactions and their applications in high resolution imaging.

The backscatter characteristics of ocean surfaces are of great importance in active marine remote-sensing fields. This paper presents the high spatial and temporal resolution dual co-polarized (VV and HH) and cross-polarized (HV) Ka-band sea-surface backscattering measurements taken from the Yellow Sea research platform at incidence angles ranging from 30° to 50° and in the wind speed range from 5.8 to 8.6 m/s. The experimental results show that the backscattering coefficient in HH polarization is close to (or even surpassing) that in VV polarization within a wind speed range of 7.1 to 8.6 m/s for Ka band under high resolution at medium incidence angles (30°–50°). Further analysis of the 10-ms short-time observation samples found that the sea surface echoes in VV polarization are more sensitive to wave motions, exhibiting more complex scattering characteristics such as multi-peaks and reducing scattering energy, especially at high wind speeds and large incident angles. The Doppler velocity analysis also confirms that rapid ocean wave changes can be detected within a short observation period, especially in VV polarization. The research in this article not only demonstrates the high spatial and temporal resolution capabilities of Ka-band radar for ocean surface observation but also reveals its great potential in interpreting and inversing rapidly evolving marine phenomena.

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications.

High-precision and high-resolution scattering data play a crucial role in remote sensing applications, including ocean environment monitoring, target recognition, and classification. This paper proposes a deep learning-based model aimed at enhancing and reconstructing the spatial resolution of Sentinel-1 backscatter NRCS (Normalized Radar Cross Section) data for China’s offshore seas, including the Bohai Sea, Yellow Sea, East China Sea, Taiwan Strait, and South China Sea. The proposed model innovatively integrates a Self-Attention Feature Fusion based on the Weighted Channel Concatenation (SAFF-WCC) module, combined with the Global Attention Mechanism (GAM) and High-Order Attention (HOA) modules. The feature fusion module effectively regulates the proportion of each feature during the fusion process through weight allocation, significantly enhancing the effectiveness of multi-feature integration. The experimental results show that the model can effectively enhance the fine structural features of marine targets when the resolution is doubled, though the enhancement effect is slightly diminished when the resolution is quadrupled. For high-resolution data reconstruction, the proposed model demonstrates significant advantages over traditional methods under a scale factor of 2 across four key evaluation metrics, including PSNR, SSIM, MS-SSIM, and MAPE. These results indicate that the proposed deep learning-based model is not only well-suited for scattering data from China’s offshore seas but also provides robust support for subsequent research on ocean target recognition, as well as the compression and transmission of SAR data.