The roughness calculation of the basal boundary for the ice-sounding data collected at Princess Elizabeth Land (PEL)

In this paper, we calculated the roughness of the basal boundary collected at Princess Elizabeth Land (PEL) to evaluate the topographic structure via the ice-sounding data collected during 32nd and 33rd Chinese Antarctic Research Expeditions (CHINARE 32 and 33). The calculation is achieved by a two-parameter roughness index method, which could differentiate different classes of subglacial landscape, in particular between erosional and depositional settings. Finally, the calculation results of partial regions of PEL are illustrated to describe the roughness of the detected regions

Detecting and Searching for subglacial lakes through airborne radio-echo sounding in Princess Elizabeth Land (PEL), Antarctica

Over 400 subglacial lakes were discovered in Antarctica through radio-echo sounding (RES) method and remote sensing. Subglacial lakes have significance in lubricating ice-bedrock interface and enhancing ice flow. Moreover, ancient lives may exist in the extreme environment. Since 2015, the “Snow Eagle 601” BT-67 airborne platform has been deployed and applied to map ice sheet and bedrock of Princess Elizabeth Land. One of great motivations of airborne surveys is to detect and search for subglacial lakes in the region. In this paper, we provided preliminary results of RES over both old and new discovered lakes, including Lake Vostok, a potential second large subglacial lake and other lakes beneath interior of the ice sheet in Antarctica

Multi-branch-DeepBedMap-DEM

<p>Antarctica bed Digital Elevation Model (250 m spatial resolution) in GeoTiff format, using Antarctic Polar Stereographic Projection (EPSG:3031).</p&gt

End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery

Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliable analysis of their future evolution. Newer deep learning techniques greatly outperform the traditional techniques based on hand-crafted feature engineering. Therefore, we propose an efficient end-to-end network for the automatic classification of ice sheet subsurface targets in radar imagery. Our network uses bilateral filtering to reduce noise and consists of ResNet module, improved Atrous Spatial Pyramid Pooling (ASPP) module, and decoder module. With radar images provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011 as our training and testing data, experimental results confirm the robustness and effectiveness of the proposed network in radargram

Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters

Distribution of Shallow Isochronous Layers in East Antarctica Inferred from Frequency-Modulated Continuous-Wave (FMCW) Radar

During the 32nd Chinese National Antarctic Research Expedition, the Frequency-Modulated Continuous-Wave (FMCW) radar was used for the first time to obtain the distribution of shallow isochronous layers within the East Antarctic region extending from Zhongshan Station to Kunlun Station. Taking a typical area as a case study, this article describes the complete workflow used in radar data processing, including signal processing and extraction of isochronous layers. The wave velocity model is established according to an empirical formula to calculate the depth of the layer, and the result is compared and corrected with the volcanic record in ice core DT263; the relative error of depth is only approximately 5%. The echograms of the isochronous layers in three regions are presented, including the area around the Dome A, the area 100 km from the Dome A and the area in the Lambert Glacier. A comparison of the echograms within the three regions shows that the isochronous layers are relatively stable in the Dome A and change more intensely in the Lambert Glacier, while the folding of the layer occurs in a concentrated area near Dome A. This folding may be due to the local layer mixing and compression caused by the ice flow and wind-driven processes. The analysis of the distribution of the shallow isochronous layers and age-depth information from different regions provides important data that support the calculation of large-scale accumulation rates and flow history in the Antarctic