High-precision Global Navigation Satellite System (GNSS) observations in Antarctica play an important role in monitoring bedrock deformation, which comprises the viscoelastic response to past ice-load changes (Glacial Isostatic Adjustment, GIA) and the elastic response to present-day ice-mass change. Although multi-GNSS constellations are commonly evaluated collectively, the influence of constellation-specific orbital configurations and signal architectures under extreme polar conditions remains insufficiently characterized. Using observations from seven Antarctic IGS stations (PALM, SCTB, DUMG, CAS1, DAV1, MAW1, and SYOG) spanning 2019-2025, the performance of the Galileo and the BeiDou Navigation Satellite System (BDS) constellations was investigated with respect to orbital geometry, multipath behavior, inter-system bias (ISB) variability, and Precise Point Positioning (PPP) convergence characteristics. Polar skyplot analyses indicated that BDS geostationary (GEO) and inclined geosynchronous orbit (IGSO) satellites remain predominantly below 15 degrees elevation at Antarctic latitudes, contributing disproportionately to low-elevation observation density and elevated multipath susceptibility. When GEO/IGSO observations were excluded, the BDS medium Earth orbit (MEO) sub-constellation exhibited positioning behavior broadly comparable to Galileo's predominantly MEO-based architecture. During austral summer periods, Galileo E5 observations exhibited lower sensitivity to seasonal firn-related reflectivity changes compared to BDS B2a, consistent with the expected robustness of the wideband Alternative Binary Offset Carrier (AltBOC) modulation. Wintertime temperature minima below -50 degrees C were associated with seasonally varying ISB patterns exhibiting apparent lag behavior on the order of several weeks, although the underlying physical mechanisms require further investigation. Static PPP analyses further indicated that multi-GNSS solutions using GPS+GLONASS+Galileo + BDS-MEO achieved shorter median convergence times than standalone Galileo or BDS-ALL solutions under both geomagnetically quiet and elevated solar activity conditions. In addition, MEO-filtered multi-GNSS processing yielded lower vertical velocity uncertainties than BDS-ALL solutions. These findings suggest that constellation-aware processing strategies may improve positioning stability and velocity estimation performance for Antarctic GNSS applications relative to conventional all-in-view approaches.
The rapid global expansion of offshore wind power has created an urgent demand for efficient methods to map the deployment scale of offshore wind turbines (OWTs). Synthetic Aperture Radar (SAR) is well suited to this task due to its wide spatial coverage and frequent revisit capability. However, most existing SAR-based detection methods rely on multi-temporal image stacking to suppress dynamic vessel interference or data-intensive deep learning workflows requiring substantial annotated training data and cloud-computing resources. These dependencies introduce update latencies ranging from months to a year. In this study, we propose a practical method for detecting OWTs from a single SAR image by integrating morphological features and spatial distribution analysis. The method combines adaptive target extraction, morphological filtering, and spatial statistical analysis to identify regularly spaced turbine clusters and derive their geographic coordinates. Case studies in the southern Shandong Peninsula, Jiangsu coast, and German Bight demonstrate that the proposed method achieves reliable detection performance across diverse marine environments. Rather than replacing high-accuracy centralized global datasets or multi-temporal deep learning systems, the proposed method serves as a lightweight alternative strategy, offering a practical solution for timely and on-demand OWT inventory updating, offshore engineering site assessment, and maritime navigation safety management.
Accurate bathymetric mapping is vital for maritime safety and ecosystem management. Conventional satellite-derived bathymetry (SDB) models often struggle in turbid waters due to the inherent spatial non-stationarity of underwater topography. We propose the Spatial-Location Gradient Boosting Machine (SL-GBM), a novel inversion framework that integrates spectral signatures with explicit geospatial coordinates. By incorporating longitude, latitude, and their interaction terms as exogenous variables, SL-GBM effectively captures localized geomorphological variations and mitigates spectral confusion common in heterogeneous aquatic environments. The model was rigorously evaluated at Yantian Port (coastal) and Sancha Lake (reservoir). Results demonstrate that SL-GBM significantly outperforms traditional Stumpf, Log-linear, and Random Forest models. At Yantian Port, it achieved an R2 of 0.95 and RMSE of 0.90 m; at Sancha Lake, it maintained an R2 of 0.87 and RMSE of 2.05 m despite a 24-meter depth gradient. Feature importance analysis revealed that the synergy between spatial priors and spectral derivatives accounts for over 60% of the model gain. These findings suggest that SL-GBM is a robust, spatially-aware framework for high-resolution bathymetric mapping within the evaluated coastal and inland environments. Future cross-regional validation is recommended to firmly establish its broader operational applicability across globally diverse aquatic systems.
In maritime environments, the lack of ground-based Internet limits the application of real-time Precise Point Positioning (PPP), making high-precision ship positioning challenging. Through the PPP-B2b service, BDS-3 can provide real-time precise positioning for users in China and its surrounding areas without relying on Internet. Based on orbit, clock, and differential code bias (DCB) corrections from PPP-B2b, this study extends the BDS-3 undifferenced and uncombined dual-frequency PPP model to the multi-frequency model and applies predicted DCB to correct intra-frequency DCB. A BDS-3/GPS dual-system multi-frequency real-time PPP model is then developed. To support receivers with different signal capabilities, eight positioning strategies are designed, including dual-, triple-, four-, and five-frequency combinations for both BDS-3 single-system and BDS-3/GPS integration. Experiments using 23 days of real maritime data show that the dual-system five-frequency strategy achieves the best performance, with convergence time of 7.5 min and horizontal accuracy of 61.7 mm. Furthermore, simulated kinematic experiments are conducted on 10 static stations using PPP-B2b and post-processed precise products, respectively. The results show that positioning performance using PPP-B2b products still shows a certain gap compared to that of post-processed products. Overall, PPP-B2b service provides fast convergence, high accuracy, and real-time positioning for maritime applications in waters surrounding China.
