In this paper, we demonstrate an application of spaceborne synthetic aperture radar (SAR) constellation data to derive sea surface tidal current at high spatial resolution. The maximum cross correlation (MCC) method, which has been widely applied to optical remote sensing data to derive sea surface velocities, is applied to X-band SAR data from TerraSAR-X (TSX) and TanDEM-X (TDX), which were operating in pursuit monostatic mode. Because of the short temporal interval of TSX and TDX's pursuit acquisitions, temporal de-correlation is minimized to derive tidal current fields that exhibit significant temporal and spatial variations. In addition, we combined data from TDX and another X-band SAR, COSMO-SkyMed, to obtain a virtual SAR constellation data pair, which was also analyzed using the MCC method to derive the tidal current field. Case studies of Hangzhou Bay in the East China Sea and Amrum Island in the North Sea are presented. The SAR-derived tidal current fields were compared to the results of numerical model simulations, high-frequency (HF) radar measurements and in situ measurements. MCC coefficients that are greater than 0.8 are an appropriate threshold for the further derivation of tidal currents. Comparisons to finite volume community ocean model, HF radar and general estuarine transport model results yield root-mean-square errors of 0.13, 0.06, and 0.05 m/s, respectively. Measurements from three field stations were also compared to the MCC SAR retrievals, yielding differences of 0.3, 0.07, and -0.09 m/s.

Sustainable growth should not only be beneficial to the shipping industry in the future, but is also an urgent need to respond to resource and environmental crises and strengthen shipping governance. Maritime traffic in Arctic waters is prone to encounter dangerous ice conditions, and it is essential to study the mechanism of ice collision risk formation in relation to ice conditions. Taking the ship-ice collision risk in Arctic waters as the research object, we propose a dynamic assessment model of ship-ice collision risk under sea ice status dynamic association (SDA) effect. By constructing the standard paradigm of risk factor dynamic association (DA) effect, taking SDA as the key association factor. Combing with other risk factors that affect ship-ice collision accidents, the coupling relationship between risk factors were analyzed. Then, using the Bayesian network method to build a ship-ice collision accident dynamic risk assessment model and combing with the ice monitoring data in summer Arctic waters, we screen five ships’ position information on the trans-Arctic route in August. The risk behavior of ship-ice collision accidents on the selected route under SDA is analyzed by model simulation. The research reveal that the degree of SDA is a key related factor for the serious ice condition and the possibility of human error during ship’s navigation, which significantly affects the ship-ice collision risk. The traffic in Arctic waters requires extra vigilance of the SDA effect from no ice threat to ice threat, and continuous ice threat. According to the ship-ice collision risk analysis under the SDA effect and without SDA effect, the difference in risk reasoning results on the five stations of the selected route are 32.69%, −32.33%, −27.64%, −10.26%, and −30.13% respectively. The DA effect can optimize ship-ice collision risk inference problem in Arctic waters.

Distinguishing sea ice and water is crucial for safe navigation and carrying out offshore activities in ice zones. However, due to the complexity and dynamics of the ice–water boundary, it is difficult for many deep learning-based segmentation algorithms to achieve accurate ice–water segmentation in synthetic aperture radar (SAR) images. In this paper, we propose an ice–water SAR segmentation network, E-MPSPNet, which can provide effective ice–water segmentation by fusing semantic features and edge information. The E-MPSPNet introduces a multi-scale attention mechanism to better fuse the ice–water semantic features and designs an edge supervision module (ESM) to learn ice–water edge features. The ESM not only provides ice–water edge prediction but also imposes constraints on the semantic feature extraction to better express the edge information. We also design a loss function that focuses on both ice–water edges and semantic segmentations of ice and water for overall network optimization. With the AI4Arctic/ASIP Sea Ice Dataset as the benchmark, experimental results show our E-MPSPNet achieves the best performance compared with other commonly used segmentation models, reaching 94.2% for accuracy, 93.0% for F-score, and 89.2% for MIoU. Moreover, our E-MPSPNet shows a relatively smaller model size and faster processing speed. The application of the E-MPSPNet for processing a SAR scene demonstrates its potential for operational use in drawing near real-time navigation charts of sea ice.