Fractures represent vulnerable discontinuities formed under stress conditions, with their three-dimensional morphological parameters serving as pivotal indicators for assessing ice shelf dynamic stability. The current fracture monitoring system primarily focuses on two-dimensional feature analysis, and there is insufficient three-dimensional systematic monitoring of vertical extension processes. Based on REMA DEM data, this study integrates deep learning semantic segmentation with hydrological terrain analysis methods to construct a framework for extracting fracture depth information. For the first time, a comprehensive dataset of fracture depths across the Antarctic ice shelves is created, and based on this dataset, the three-dimensional extent of ice shelf damage is quantified and evaluated. The study shows that the average depth of fractures in ice shelves is 8.17 meters, with differences between ice shelves reaching up to ten times. Notable spatial variations in fracture depth are also observed within ice shelves. The depth distribution of fractures exhibits significant spatial coupling with the stretching stress field of the ice shelf. The three-dimensional morphological parameters of the ice shelf (average depth, area density, volume density, and penetration rate) exhibit significant spatial heterogeneity. This study fills the gap in the vertical dimension of fracture 3D modeling, providing essential data support for ice shelf stability research.

The 11-month data gap between Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) hinders monitoring long-term ice mass change and its further analysis. While many attempts have been made to bridge water storage gaps, few unified frameworks exist to bridge the ice mass change gaps for both Greenland Ice Sheet (GrIS) and Antarctic Ice Sheet (AIS). This study combined partial least squares regression (PLSR) and the Sparrow Search Algorithm optimized back propagation (SSA-BP) to fill this gap in GrIS and AIS. During this process, seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), and multiple linear regression (MLR) were introduced as comparison. PSLR is utilized to select key variables for constructing predictive models. We found SSA-BP outperformed SARIMAX and MLR, with correlation coefficients and root mean square error at 0.99 and 39.22 Gt for GrIS, and 0.95 and 189.85 Gt for AIS within the testing period. SSA-BP demonstrated a reasonable mass change trend with less noise than other methods. SSA-BP reconstructed result shows superiority than other researches. And the reconstructed seasonal signals highlight the importance of filling the gap, showing decreased mass loss for GrIS and continuous mass loss acceleration for AIS post-2016.

The nominally coarse spatial resolution ($300\sim ~400$km) of gravity recovery and climate experiment (GRACE) and a 11-month data gap with GRACE follow-on (GRACE-FO) limits applications at the individual ice sheet drainage basin scale and complicates the evaluation of regional ice sheet mass changes. While numerous works have downscaled GRACE-estimated water storage, research on downscaling ice mass change in Antarctica is limited. This study employs joint partial least-squares regression (PSLR) and support vector machine (SVM) method to reconstruct GRACE-derived spatiotemporal data for the Antarctic ice sheet (AIS). The pixel-temporal downscaling (PTD) of random forest (RF) and pixel-spatial downscaling (PSD) of multiscale geographically weighted regression (MGWR) enhance spatial resolution of ice mass changes from 0.25° (~120 km) to 1.92 km. The downscaled results show consistent temporal variation and reduced noise compared to other reconstruction methods. Both RF and MGWR results exhibit high consistency with original GRACE data, with MGWR achieving a correlation coefficient (CC) of 0.99. The MGWR model effectively captures finer signals related to ice flow velocity. When compared to independent free air gravity anomalies, MGWR outperforms RF with improvements of 41.51% and 56.25% in mean correlation for group 1 and group 2 observation points, respectively. In addition, MGWR shows improvements of 16.90%/29.69% for flight Line A and 11.84%/19.72% for flight Line B compared to RF and original GRACE results. The enhanced spatial resolution offers valuable insights into ice dynamic changes within the Western AIS and Eastern AIS and smaller regions such as the Antarctic Peninsula.

The segmentation of crevasses in remote sensing images plays a pivotal role in diverse domains, including crevasse change monitoring, analysis of ice shelf surface water systems, and investigations into ice shelf stability. In response to the limitations in existing crevasses segmentation methods, which struggle to concurrently capture global structures while preserving local details, this letter introduces CREVNet. CREVNet is designed to achieve precise crevasse segmentation, comprising two integral components: the Transformer Path for enhanced local and global feature extraction, and the Convolutional Path for detailed depiction of crevasses. Evaluation on crevasses dataset, created through the integration of optical remote sensing imagery and laser altimetry data, reveals impressive results. CREVNet achieves F1-score, MIoU, and OA values of 80.40%, 80.98%, and 95.24%, respectively. Notably, CREVNet surpasses the performance of prominent deep learning methods, including Unet, DeepLabV3Plus, DFANet, FPN, MobileViT, and TransUnet. These outcomes underscore CREVNet’s practical potential for effective crevasses segmentation.

Traditional ionospheric models were mostly constructed based on a single layer assumption from Global Navigation Satellite System (GNSS) observations, while it cannot capture vertical information of the ionosphere. This study proposes a new method to construct a double-layer ionospheric model based on constraints from a three-dimensional ionospheric model, whereby the bottom and topside ionospheric TEC can be represented by two spherical harmonic (SH) functions. The new improved model allows two SH functions to capture the spatiotemporal TEC variations across the vertical range of the ionosphere. The determination of the two thin layer heights (TLHs) in the double-layer model is achieved through minimum mapping function error. Moreover, the performance of the new model is validated using GPS, BDS, and Galileo data from the International GNSS Server (IGS) Network, and compared with the global ionospheric map (GIM). During the experiment period, the results indicate that (1) the TLHs of the bottom and topside ionosphere exhibit distinct spatiotemporal trends with the optimal global heights as 350 km and 650 km, respectively; (2) the average relative accuracies of the bottom and topside ionospheric models are up to 86.80 % and 85.33 %, respectively; (3) the new model demonstrates an improvement of approximately 20–27 % in terms of TEC when compared to the GIM model, with the RMS better than 4.64 TECU, 2.99 TECU, and 3.61 TECU in the low, middle, and high latitudes, respectively; and (4) with the increase of geomagnetic activity, the performance of the double-layer model shows a slight decline, but its relative accuracy can still reach over 84.8%.

Basal channels are the expression of basal melting in detail. In extreme cases, they will cause the ice shelf calving and seriously threaten the ice shelf stability, which has attracted wide attention. In this study, we used reference elevation model of Antarctica (REMA) digital elevation models (DEMs) data, IceBridge data, and Amery Ice Shelf thickness data to accurately identify the distribution location of Antarctic Ice Shelf Basal Channel (AISBC). In addition, we counted the basal channels length (BCL) and basal channels concentration (BCC) and analyzed the main formation mechanism of basal channels in different sea. The accuracy of identifying basal channels was at least more than 90%. Our identification results were more accurate compared with Alley et al. (2016), and there were especially basal channels on Amery Ice Shelf and Larsen-C Ice Shelf. AISBC network is developed, and AISBC’s total length is about 16965 km. In particular, the transverse basal channel has a potential impact on the ice shelf calving. BBC along the coast of Dumont D’Urville Sea was the highest, which is closely related to the active degree of the formation mechanism of the basal channels. The types of AISBC show obvious regional characteristics, which were mainly affected by the formation mechanism of basal channels. Our research results can provide scientific reference data for studying ice shelf stability in different sea regions.

Due to the severe speckle noise and complex local deformation in synthetic aperture radar (SAR) images, robust pixelwise registration with high accuracy is an important problem but is far from being resolved. The core of this problem is how to establish a precise deformation field that maps every pixel to its corresponding pixel with high accuracy. To address this problem, a novel SAR dense-matching algorithm, which includes high-accuracy landmark generation and a precise deformation field parameter estimation, is proposed in this article. First, a strategy for generating enough well-distributed landmarks is proposed by designing patch matching of improved scale-invariant feature transform features based on phase correlation and the gradient method. Furthermore, a multiscale coherent point drift (MCPD), powered by iterative residual map minimization, is designed to reliably match landmarks and estimate precise field parameters. Both simulated deformed SAR images and real SAR images are utilized to evaluate the performance of the proposed method, and the experimental results demonstrate that the proposed method provides better registration performance than previous methods in terms of both accuracy and robustness.

Global Navigation Satellite System (GNSS) ionospheric tomography is a typical ill-posed problem. Joint inversion with external observation data is one of the effective ways to mitigate the problem. In this article, by fusing 3-D multisource ionospheric data, and improving the stochastic model, an improved GNSS tomographic algorithm MFCIT [computerized ionospheric tomography (CIT) using mapping function] is presented. The accuracy of the algorithm is validated by selected data under different geomagnetic and solar conditions acquired in Europe. The results show that the estimated, statistically significant uncertainty for each of the layers is about 0.50-3.0TECU, with the largest absolute error within 6.0TECU. The advantage of the MFCIT is that it is based on the Kalman filter, which enables efficient near real-time 3-D monitoring of ionosphere. The temporal resolution can reach ~1 min level. Here, we apply the ionospheric tomography inversion to the magnetic storm on January 7, 2015, in the European region, and quantified the evolution of the storm. The results show that the difference of the core region between the MFCIT and CODE GIM is less than 1TECU. More importantly, during the initial phase of the storm, when the ionospheric disturbance is not evident in the single layer CODE GIM model, the MFCIT shows obvious positive disturbances in the upper ionosphere, although there is no disturbance in the F2 layer. The MFCIT further tracks the evolution of the magnetic storm that the ionospheric disturbance expands from the upper to the lower ionosphere layers, and at UT12:00, the disturbance continues to spread to the F2 layer.

The influence of the ionosphere on spaceborne SAR signals can be significant, predominantly at the L-band and lower frequencies. In particular, low band polarimetric SAR's applications are mainly limited by Faraday rotation (FR) effects. In this paper a new FR estimator is proposed from linearly polarized coherency matrix data and validated by ALOS PALSAR full-pol data processing. FR angles computed by the new estimator are in good consistent with FR angles computed by physical model. The estimator is also used to assess the impact on sea ice. The results show that a FR value exceeding 2° could reduce the accuracy of geophysical parameter recovery of sea ice in the cross-polarized channels.