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.

Satellite altimetry is a primary method for monitoring dynamic changes in polar ice and snow. However, recent studies on extreme melting events have revealed the notable impact of radar altimeter signals penetrating through snow, challenging previous assumptions that monitoring imparted negligible effects. Current approaches for mitigating penetration errors offer only partial alleviation instead of complete elimination. This study estimates the penetration values of Greenland satellite monitoring from February 2004 to November 2020 by comparing the temporal sequence of elevation changes obtained from laser altimetry and radar altimetry. We investigated the temporal and spatial evolution characteristics of penetration, particularly focusing on the extreme melting event in 2019. Interestingly, laser altimeter signals are also determined to penetrate water bodies, introducing a potential source of inaccuracy in laser altimeter measurements during the melting season. Furthermore, the analysis estimated the impact of time-variable penetration depths on Greenland’s mass balance, revealing that regions with severe mass loss have the potential to exceed loss rates of 10 Gt/yr. These findings enhance our understanding of the impact of penetration errors in satellite altimetry and emphasize the crucial need for their correction to ensure accurate mass balance calculations in Greenland.

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.