Abstract. Arctic coastal sea ice phenology is a critical climate indicator, yet sub-kilometer long-term observations remain scarce, limiting our understanding of complex local freeze-thaw dynamics. This study investigates the long-term evolution and thermodynamic drivers of coastal sea ice using a continuous, 20-year (2003–2023) high-resolution dataset derived from ground-based GNSS Interferometric Reflectometry (GNSS-IR) at Tuktoyaktuk in the Beaufort Sea. By employing a physics-based Amplitude Integration Factor (AIF) method, we successfully bridged decadal hardware discrepancies to extract an uninterrupted, thermodynamically consistent climatological record. Trend analysis of the 20-year record reveals a statistically significant shortening of the continuous ice season by 4.63 days per decade (p = 0.04). This climatological decline is profoundly asymmetric, driven primarily by a substantially delayed autumn freeze-up (+3.40 days per decade) rather than an advanced spring breakup (−1.42 days per decade), underscoring the dominant influence of enhanced summer oceanic heat uptake and thermal memory. The physical reliability of these localized observations is corroborated by their strong coupling with accumulated Freezing Degree-Days (R² = 0.74). Crucially, cross-scale comparisons demonstrate that GNSS-IR detects autumn freeze-up onset 5.5 ± 3.7 days earlier than 4-km gridded satellite products (IMS). This systemic lead time confirms the unique capability of GNSS-IR to resolve initial nearshore frazil ice formation – a critical sub-grid thermodynamic process typically diluted in coarse-resolution remote sensing. Ultimately, this work provides an essential high-resolution baseline for validating regional climate models.

The fundamental principles of Beidou navigation satellite system(BDS)/global navigation satellite system(GNSS)and interferometric synthetic aperture radar(InSAR)technology were outlined,with a focused review of their theoretical developments since the 21st Ccentury.The latest research advances in their integrated application for deformation monitoring were analyzed in depth.Core issues and potential challenges currently faced by the combined BDS/GNSS and InSAR technology in monitoring engineering deformation and ensuring safety prevention for high-steep slopes were summarized.It is suggested that this integrated approach can achieve centimeter-level accuracy while enabling full-area coverage,significantly enhancing the capability for high-steep slope deformation monitoring.Furthermore,it is pointed out that future efforts should focus on deepening the integration of BDS and domestic SAR satellite data based on an integrated space-air-ground monitoring network,alongside the development of industry-specific large language models tailored for high-steep slope monitoring.

Long-term coastal sea-level monitoring in polar regions relies on bottom-mounted pressure tide gauges (PTGs), which are less vulnerable to sea-ice damage but remain difficult to reference to a stable vertical datum. In addition, conventional pressure-to-height conversion commonly assumes constant seawater density, potentially introducing steric biases in dynamic Arctic estuaries. This study develops a non-intrusive geodetic calibration framework for Arctic PTGs using two decades of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) altimetry. Continuous Signal-to-Noise Ratio (SNR) observations from the TUKT GNSS station in the Canadian Arctic during 2003-2023 were processed to retrieve geometric sea levels during open-water seasons and evaluate a collocated submerged PTG. The GNSS-IR/PTG comparison reveals a persistent datum offset of -0.111 m. After the 2015 receiver modernization, retrieval precision stabilized below 5 cm RMSE, supporting GNSS-IR as an independent geometric constraint for remote tide-gauge calibration. The residuals further show a repeatable nonlinear intra-seasonal decline, inconsistent with simple monotonic datum drift. Regional riverlevel and ocean-temperature records suggest that this pattern is associated with early-summer halosteric contraction, a midsummer thermosteric rebound, and autumn cooling-induced thermosteric contraction. These results demonstrate that longterm GNSS-IR altimetry can support geodetic calibration of Arctic PTGs and provide diagnostic information on densityrelated biases in pressure-derived sea-level records, thereby improving the vertical consistency of polar observing networks where repeated leveling is logistically constrained.

Traditional methods for the construction of intertidal digital elevation models (DEMs) require the integration of long-term multi-sensor datasets and struggle to capture the spatiotemporal variation caused by ocean dynamics. The SWOT (surface water and ocean topography) mission, with its wide-swath interferometric altimetry technology, provides instantaneous full-swath elevation data in a single pass, offering a revolutionary data source for high-precision intertidal topographic monitoring. This study presents a framework for SWOT-based intertidal DEM extraction that integrates data preprocessing, topographic slope map construction, and tidal channel masking. The radial sand ridge region along the Jiangsu coast is analyzed using SWOT L2 LR (Low Resolution) unsmoothed data from July 2023 to December 2024. Multisource validation data are used to comprehensively assess the accuracy of sea surface height (SSH) and land elevation derived from LR products. Results show that the root mean square error (RMSE) of SSH at Dafeng, Yanghe, and Gensha tide stations is 0.25 m, 0.19 m, and 0.32 m, respectively. Validation with LiDAR data indicates a land elevation accuracy of ~0.3 m. Additionally, the topographic features captured by LR products are consistent with the patterns observed in the remote sensing imagery. A 16-month time-series analysis reveals significant spatiotemporal variations in the Tiaozini area, particularly concentrated in the tidal channel areas. Furthermore, the Pearson correlation coefficient for the DEMs generated from SWOT data decreased from 0.94 over a one-month interval to 0.84 over sixteen months, reflecting the persistent impact of oceanic dynamic processes on intertidal topography.

Snowy weather makes the multipath effects of receivers in BDS/GPS short baseline applications more complex. Traditional multipath hemispherical map (MHM) relies on stable observation environments, while the multipath period of the BDS MEO constellation is as long as seven days. Random walk multipath (RWM) utilizes multipathcorrected GPS observations to estimate multipath errors of other systems, providing advantages in challenging environments. We evaluated the performance of MHM and RWM in the BDS/GPS combined system, and the results showed that only the RWM meets the application requirements. Moreover, adopting multi-period modeling data may lead to a decrease in positioning accuracy. Further experiments showed that GPS satellites exhibited higher multipath correlation coefficients than BDS satellites. Considering the limited modeling data, three different RWM schemes are designed and evaluated. Positioning results from two consecutive weeks at Jurong Power Station indicate that the scheme of using GPS to assist all BDS satellites (RWM-G) is more recommended. This was further validated with a week's data from another reservoir. In addition, a detailed evaluation of RWM-G revealed significant suppression of low-frequency multipath and some suppression but limited of high-frequency ones, with an average residual reduction rate of about 45 % for all satellites. During the initial snowfall period with better observation environments, the improvement of daily solutions in the vertical direction is more significant. In the snow accumulation stage with poor observation environments, the RWM-G can correct positioning offsets, ensuring accurate and reliable results.

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), utilizing a 532 nm green laser, provides critical data for high-precision depth measurements in shallow water areas. However, due to the high sensitivity of photon detection to solar radiation noise and complex underwater topography, the data contain significant noise, making precise extraction of underwater terrain a major challenge. Vertical segmentation methods are ineffective in accurately capturing steep terrain features. This article proposes a denoising algorithm based on point cloud gridding, which converts discrete photon data into a raster grid, emphasizing the spatial distribution of the water surface and underwater terrain. Through multiscale density-adaptive processing, the algorithm effectively separates photons above, at, and below the water surface, accurately locating underwater terrain points, making it particularly suitable for photon data analysis in complex underwater environments. Experimental validation across multiple regions shows that the proposed algorithm significantly outperforms density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS) in terms of accuracy and robustness. Experimental results show that the proposed algorithm achieves a coefficient of determination ( $R<^>{2}$ ) of 0.99 across different regions, with root mean square error (RMSE) ranging from 0.27 to 0.38 m, and mean absolute error (MAE) ranging from 0.21 to 0.33 m. Compared to traditional methods, the proposed algorithm effectively removes noise points in sparse photon regions while preserving the continuity and details of underwater terrain in photon-dense areas. The experimental results demonstrate that the proposed algorithm accurately extracts the underwater contours of complex terrain and shows strong adaptability and stability in various aquatic environments. This method demonstrates outstanding precision and denoising capabilities in underwater terrain extraction in shallow water areas, providing a reliable and efficient approach for high-precision depth measurement of complex underwater topography.

Accurate shallow-water depth information for island areas is crucial for maritime safety, resource exploration, ecological conservation, and offshore economic activity. Traditional approaches such as shipborne sounding and airborne bathymetric light detection and ranging (lidar) surveys are expensive, time-consuming, and constrained in politically sensitive regions. Moreover, satellite-altimetry-predicted depths exhibit large errors over shallow waters. In contrast, satellite-derived bathymetry (SDB), estimated from multispectral imagery, provides a rapid, open-source, and cost-effective technique for comprehensively characterizing the bathymetry of a region. Given the scarcity of in situ water depth data for the South China Sea (SCS), a shallow-water depth model, HHU24SWDSCS (Hohai University 2024 Shallow-Water Depth Model of South China Sea), was developed using a linear band model by integrating 1298 Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) tracks with 70 Sentinel-2 multispectral images. The model covers over 120 islands and reefs in the SCS region at a resolution of 10 m. Validation against independent ICESat-2 depth data yielded a root mean square error for the model of 0.53–1.24 m (< 5 % of the maximum depth). Further validation using independent airborne lidar bathymetry data in the Lingyang Reef demonstrated an accuracy of 1.01 m. Comparisons with existing bathymetry models revealed the superior performance of the developed model. While traditional bathymetry models exhibit errors of up to tens of meters or larger over island regions and should therefore be used with caution, HHU24SWDSCS demonstrated good accuracy in shallow waters across the SCS. This model thus provides a reference for mapping shallow-water depth close to islands and provides fundamental support for research in oceanography, geodesy, and other disciplines. The HHU24SWDSCS data are freely available at https://doi.org/10.5281/zenodo.13852568 (Wu et al., 2024a).

Accurate beach topography mapping is crucial for understanding coastal dynamics and mitigating climate change impacts. However, traditional methods such as airborne LiDAR have limitations, leading to substantial gaps in national-scale elevation data. This study presents an innovative framework to reconstruct missing elevation data along New Zealand's (NZL) coastline by integrating airborne LiDAR, Sentinel-2 optical imagery, and geometric features (distance) using machine learning (ML) methods. Our results show that artificial neural network (ANN) emerged as the best model (test set: R ${}<^>{2} =0.79$ , root mean squared error (RMSE) = 0.91 m; validation set: 0.79, RMSE = 0.93 m), outperforming other models in accuracy. The produced 10-m digital elevation model (DEM) for national-scale sandy beaches expands area coverage by 286.6% (114.15 km2), filling gaps in 1249 beaches, including remote areas such as Stewart Island. This novel framework offers a scalable solution for improving the comprehensiveness and accuracy of beach topography. It provides essential support for inundation prediction, habitat management, and the development of climate adaptation strategies, thereby facilitating more informed decision-making in coastal zone management and climate change mitigation efforts.

Bathymetry provides instrumental information for studying sedimentary processes, global climate change, and benthic morphologies. The advantages and applicabilities of different techniques for bathymetry detection vary. We propose a framework for bathymetry enhancement from multisource data based on spherical radial basis functions (SRBFs). A case study is conducted over the Paracel Islands in South China Sea (SCS), where Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) measurements, airborne gravimetric observations, echo soundings, and the reference model DTU18BAT are merged. Numerical results suggest that the fusion of ICESat-2 observations dramatically enhances the quality of the computed bathymetry model near the islands, the root-mean-squared error (RMSE) of which is reduced by 45.35%-67.95% compared to existing models when validated against the satellite-derived bathymetry (SDB) with decimeter-level accuracy. By additionally fusing the airborne gravimetric data, bathymetry is further enhanced by similar to 22.49%, particularly over islands with sparse ICESat-2 trajectories. Comparisons with surveyed airborne bathymetric lidar data over the northern Antelope Reef yielded results consistent with those obtained from the SDB, suggesting that SDB is possible to serve as control data in waters devoid of ground truth data. Further analysis reveals that the models constrained by echo soundings performed better than existing models in deep waters, with reductions of 17.79%-44.99% in terms of RMSE. By fusing airborne gravity data, bathymetry is improved by similar to 10%, highlighting the utilization of airborne gravimetry in both shallow and deep waters. The proposed SRBF approach offers an effective way to merge heterogeneous data for high-quality bathymetry determination.

Multipath errors can significantly impact the performance of precise point positioning (PPP). Previous studies popularly leverage the spatial repeatability of satellite orbits for multipath mitigation. The multipath hemispherical map (MHM) is a widely used discrete grid- based model. Besides, various refined methods such as multipath stacking (MPS) and trend-surface analysis (T-MHM) have been proposed to enhance the performance of the discrete grid-based model. This paper proposed an improved discrete grid-based model named multipath central grid (MCG) aimed at further utilizing the spatial correlation of multipath errors. Specifically, the modified method uses distance weighting to calculate the multipath correction of the target satellite adaptively. This paper systematically demonstrated the feasibility of the MCG for PPP. Simultaneously, some typical discrete grid-based models are tested for comparison. Tests were conducted for kinematic and static PPP using observations from two reservoirs with different observation environments. The results indicate differences between these refined models. Furthermore, both models can enhance performance, and the refined models T-MHM and MCG outperform the original MHM. In addition, the high-resolution congruent MCG model is recommended as one that can provide more robust results. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Concerns have been raised about the sustainability of large-scale afforestation in semi-arid regions due to potential water constraints. This study investigated whether increased humidity in the semi-arid regions of northwest China could sustain the continued expansion of afforestation efforts. Using multi-source remote sensing data, we found that between 2012 to 2020, annual cumulative precipitation increased by 2.5 millimeters per year, while annual average carbon sequestration in afforested areas declined by 0.002 kilograms of carbon per square meter per year, indicating asynchronous trends. This disparity was primarily attributed to the trade-off between vegetation photosynthesis and transpiration in response to external water conditions, which led to a decline in the water use efficiency of afforested vegetation. The effect of water use efficiency on carbon sequestration was driven by gross primary productivity, rather than evapotranspiration. These findings underscore the importance of targeted afforestation in semi-arid regions, considering local water resource sustainability. In the China Loess Plateau, the annual precipitation increased while average carbon sequestration in afforested areas declined due to the trade-off between vegetation photosynthesis and transpiration, according to an analysis that uses the remote sensing data and a statistical approach.

Gravity anomaly over shallow waters is one of the fundamental data sources for studying sea level change, ocean currents, and water exchanges between coastal areas and open seas. However, the acquirement of gravity data over shallow waters faces multiple challenges due to the degraded quality of satellite altimetry data and scarcity of surveyed gravimetric observations. To alleviate this problem, we establish a framework for marine gravity anomaly refinement by using satellite-derived bathymetry (SDB). We use a cosine-tapered band pass filter to extract high-frequency gravity signals from the SDB data, which compensate for the unresolved signals in satellite altimetric gravity data. Numerical experiments over the Discovery Reef and an offshore region near the Port Hedland demonstrate that the utilization of SDB effectively strengths marine gravity anomaly. By combining the SDB data, the fits between the enhanced gravity anomaly models and surveyed airborne gravity data are improved, by 5.3–15.7% in comparison to an altimetric gravity model DTU21GRA. The SDB calculated from the linear band model has slightly better performances in gravity anomaly modeling than that computed from the band ratio model and physical-based approach, agreeing well with the SDB validation results. Our results verify the feasibility of using the SDB computed from the physical-based approach for gravity anomaly augmentation, which is of great value in areas devoid of ground truth depths. This study cements a way for the augmentation of marine gravity anomaly worldwide, especially in remote regions characterized by the scarcity of ground-based gravity data.

We study the role of airborne gravimetry for seamless bathymetry modeling over the Paracel Islands in the northern South China Sea (SCS) and investigate the possibility of using Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data and Satellite-derived bathymetry (SDB) to evaluate bathymetry models over shallow waters. We use ICESat-2 data for training Sentinel-2 imagery and derive the SDB data with a root-mean-squared error (RMSE) of 0.29–0.50 m, which is lower than 10% of the maximum depths. The local bathymetry is modeled by using a modified version of the S&S bandpass filter, and a partition-wise scheme is applied for determining the scaling factors. Numerical experiments verify the feasibility of using ICESat-2 and SDB data to assess bathymetry models. By utilizing the airborne gravity data, the fit between the computed bathymetry and the SDB data is significantly improved, by 18.7%–58.0% over different shallow waters compared with recently released bathymetry models. The bathymetry predicted from the airborne data has also higher performance in deep water areas, which performs best in all these depth ranges from 500 to 3000 m. In comparison with the existing models, the RMSEs of the misfits between the computed bathymetry and the National Oceanic and Atmospheric Administration depths are reduced by tens to hundreds of meters in different depth ranges. Our study highlights that using airborne gravimetry for bathymetry modeling over island areas is advantageous, in both shallow and deep waters, and that ICESat-2 and SDB data can largely alleviate the lack of in situ depths over shallow waters.

Seafloor topography over seamount areas is crucial for studying plate motions, seafloor seismicity, and seamount ecosystems. However, seamount bathymetry modeling is difficult due to the complex hydrodynamic environment, biodiversity, and scarcity of shipborne echo sounding data. The use of satellite altimeter-derived gravity data is a complementary way of bathymetry computation; in particular, the incorporation of synthetic aperture radar (SAR) altimeter data may be useful for seamount bathymetry modeling. Moreover, the widely used filtering method may have difficulty in combing different bathymetry data sets and may affect the quality of the computed bathymetry. To mitigate this issue, we introduce a Kalman fusion method for weighting and combining gravity-derived bathymetry data and the reference bathymetry model. Numerical experiments in the seamount regions over the Molloy Ridge show that the use of SAR-based altimetric gravity data improves the local bathymetry model, by a magnitude of 14.27 m, compared to the result without SAR data. In addition, the developed Kalman fusion method outperforms the traditionally used filtering method, and the bathymetry computed from the Kalman fusion method is improved by a magnitude of 9.34 m. Further comparison shows that our solution has improved quality compared to a recently released global bathymetry model, namely, GEBCO 2022 (GEBCO: General Bathymetric Chart of the Oceans), by a magnitude of 34.34 m. The bathymetry model in this study may be substituted for existing global bathymetry models for geophysical investigations over the target area.

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography. Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions. The Synthetic Aperture Radar (SAR) altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution. In this study, we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry, where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated. Furthermore, we study the effects on bathymetry modeling by using different scale factor calculation approaches, where a partition-wise scheme is implemented. The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SAR-based altimetric gravity data improves local coastal bathymetry modeling, compared with the model calculated without SAR altimetry data by a magnitude of 3.55 m within 10 km of offshore areas. Moreover, by using the partition-wise scheme for scale factor calculation, the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method. These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion.

Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (RMSE) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with RMSEs of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB.

As flourishing space geodesy technologies, InSAR and BeiDou/GNSS have advantages in land surface deformation monitoring. By applying the two tools to carry out integrated measurements, their complementarity can be fully exploited to achieve the unification of high temporal resolution of BeiDou/GNSS and high spatial resolution of InSAR. This paper firstly introduces the basic principles of InSAR and BeiDou/GNSS for land surface deformation monitoring, focusing on the theoretical development of InSAR in the past two decades. Secondly, the latest progress of InSAR and BeiDou/GNSS integration and data fusion are reviewed. Then, the key issues and challenges faced by the land surface deformation monitoring applications are summarised. Finally, the future outlook of the deformation monitoring method integrating InSAR and BeiDou/GNSS is discussed.

Multipath effect is a main source of error in relative positioning, which cannot be eliminated or mitigated by differential algorithm. We discuss the topic of mitigating multipath at a static station in observation domain with GPS and BDS systems. At present, the sidereal filtering as one of the most commonly used multipath mitigating methods relies on the repetition period of multipath, which cannot be accurately estimated influenced by the maneuver of satellite orbit and the time interval of satellite ephemeris. The window matching method as a real-time method was proposed to reduce this effect. However, this method is affected by similarity measures in the process of real-time window matching. We propose a near real-time window matching method based on sidereal filtering. In the modified method, the satellite single difference residual is divided into segments and the cross-correlation method is used to obtain the multipath repeat time. At the same time, the second segment series overlaps with the previous segment to ensure a near real-time performance. Based on the obtained repeat time, the template window and matched window are formed by epochs in the segment and then an affine transformation is applied to determine the value of multipath correction between the two windows. Tests were conducted for GPS and BDS systems respectively using the baseline observations at static stations in the Ha-Jia high-speed railway. The experimental results show that the modified method can mitigate the multipath error in double difference observation, and finally provide higher positioning results than methods without model and traditional model. In practice, application of the modified method in near real-time baseline positioning can effectively mitigate the multipath error.

The spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) offers versatile Earth surface observation. While the accuracy of the computed geometry, required for the implementation of the technique, degrades when Earth’s surface topography is complicated, previous studies ignored the effects of the local terrain surrounding the ideal specular point at a suppositional Earth reference surface. The surface slope and its aspect have been confirmed that it can lead to geolocation-related errors in the traditional radar altimetry, which will be even more intensified in tilt observations. In this study, the effect of large-scale slope on the spaceborne GNSS-R technique is investigated. We propose a new geometry computation strategy based on the property of ellipsoid to carry out forward and inverse calculations of path geometries. Moreover, it can be extended to calculate unusual reflected paths over versatile Earth’s topography by taking the surface slope and aspects into account. A simulation considering the slope effects demonstrates potential errors as large as meters to tens kilometers in geolocation and height estimations in the grazing observation condition over slopes. For validation, a single track over the Greenland surface received by the TechDemoSat 1 (TDS-1) satellite with a slope range from 0% to 1% was processed and analyzed. The results show that using the TanDEM-X 90 m Digital Elevation Model (DEM) as a reference, a slope of 0.6% at an elevation angle of 54 degrees can result in a geolocation inaccuracy of 10 km and a height error of 50 m. The proposed method in this study greatly reduces the standard deviation of geolocations of specular points from 4758 m to 367 m, and height retrievals from 28 m to 5.8 m. Applications associated with topography slopes, e.g., cryosphere could benefit from this method.