Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to comprehensively assess UAS-SfM survey capability in fast ice environments, and could serve as a reference for future improvements.

Ice doline is a particular kind of ice morphology, usually scattered on ice streams which are mostly far from the existing research bases. For this reason, glaciologists rarely have opportunities to document its developments in detail. Satellite observations are too coarse to capture such fine features, whereas unmanned aerial vehicle (UAV)-based structure-from-motion (SfM) and light detection and ranging (LiDAR) technologies have revolutionized geosciences research, especially in less accessed polar regions. We developed two bespoke UAV systems for glaciological investigation and carried out four campaigns during two consecutive Chinese Antarctic expeditions in 2017 and 2018. Founded on manual co-registration and accuracy assessment, a successful application to characterize a doline's spatio-temporal evolution is presented in this article. The overlying weight of surface melting directly triggered the collapse event on Jan 30, 2017 near the Dalk Glacier , and thenthe newborn doline grew for another 8135.6 m $^2$ in area and 280 303.38 m $^3$ in volume by early 2018. The UAV-based results revealed the doline's changes during a year, showing a maximum horizontal extension of 50 m and vertical subsidence of more than 10 m. Furthermore, we evaluate the photogrammetry and LiDAR systems and find the former is cost-effective and time-efficient on a large-scale survey, while the latter enjoys a better capability to characterize ice morphological details. Based on systematic comparisons, other pros and cons of the two techniques are discussed. To achieve the best performance for relevant applications in similar scenarios, we recommend adopting an integrated approach, in which the LiDAR restores the fine features on the basis of extensive SfM coverage.

Mass loss from the Antarctic Ice Sheet (AIS) is an important contributor to global sea level rise. To examine the recent ice loss, we estimated the mass budget of the AIS from 2000 to 2020 using multiple ice velocity datasets, state-of-the-art ice thickness datasets, and extended surface mass balance (SMB) records. The AIS lost mass at an average rate of −89 ± 99 Gt/yr over the study period. The East Antarctic Ice Sheet (EAIS) showed a slightly positive mass balance, while the West Antarctic Ice Sheet (WAIS) experienced a significant acceleration in mass loss. The ice discharge from the AIS increased from 1792 ± 47 Gt/yr in 2000 to 1940 ± 37 Gt/yr in 2017–2020, with the increase in the discharge from the WAIS being three to four times higher than that from the EAIS. Moreover, the average mass balance for 2017–2020 was −99 ± 93 Gt/yr, slightly more negative than the average for the early 21st Century. During this recent period, the ice discharge decreased in the East Indian Ocean sector, in contrast to its rapid increase from 2000 to 2013–2017. However, the discharge in the Amundsen Sea sector still greatly increased from 2013–2017 to 2017–2020. Overall, our results are in agreement with recent mass balance estimates for the AIS based on gravimetry and altimetry. Our assessments of the recent AIS mass balance with the mass budget method (input-output method) will contribute to the understanding of ice dynamic processes and provide insights into the stability of the AIS.

Ice Pathfinder (Code: BNU-1), launched on 12 September 2019, is the first Chinese polar observation microsatellite. Its main payload is a wide-view camera with a ground resolution of 74 m at the subsatellite point and a scanning width of 744 km. BNU-1 takes into account the balance between spatial resolution and revisit frequency, providing observations with finer spatial resolution than Terra/Aqua MODIS data and more frequent revisits than Landsat-8 OLI and Sentinel-2 MSI. It is a valuable supplement for polar observations. Geolocation is an essential step in satellite image processing. This study aims to geolocate BNU-1 images; this includes two steps. For the first step, a geometric calibration model is applied to transform the image coordinates to geographic coordinates. The images calibrated by the geometric model are the Level1A (L1A) product. Due to the inaccuracy of satellite attitude and orbit parameters, the geometric calibration model also exhibits errors, resulting in geolocation errors in the BNU-1 L1A product. Then, a geometric correction method is applied as the second step to find the control points (CPs) extracted from the BNU-1 L1A product and the corresponding MODIS images. These CPs are used to estimate and correct geolocation errors. The BNU-1 L1A product corrected by the geometric correction method is processed to the Level1B (L1B) product. Although the geometric correction method based on CPs has been widely used to correct the geolocation errors of visible remote sensing images, it is difficult to extract enough CPs from polar images due to the high reflectance of snow and ice. In this study, the geometric correction employs an image division and an image enhancement method to extract more CPs from the BNU-1 L1A products. The results indicate that the number of CPs extracted by the division and image enhancements increases by about 30% to 182%. Twenty-eight images of Antarctica and fifteen images of Arctic regions were evaluated to assess the performance of the geometric correction. The average geolocation error was reduced from 10 km to ~300 m. In general, this study presents the geolocation method, which could serve as a reference for the geolocation of other visible remote sensing images for polar observations.

Depth hoar (DH) forms when there is a strong temperature gradient in the snowpack. In the Antarctic Ice Sheet (AIS), DH mostly forms during summer insolation. The seasonal regularity provides an age marker for each internal snow/firn layer, which is necessary for estimating surface mass balance using ice penetrating radar (IPR) and ice core. However, little is known about DH inside the AIS because ice drill and snow pit observations are inefficient and sparse. The deployment of the Operation IceBridge (OIB) airborne snow radar has significantly enhanced the field observation dataset. So far, the spatial distribution of DH remains unclear due to the lack of DH extraction over the AIS from OIB snow radar. Based on instance segmentation SOLOv2 and self-attention mechanism Swin Transformer, a DH layer automatic extraction algorithm ST-SOLOv2 is proposed, with AP ₅₀ of 0.9 and F1-score of 0.83, outperforms other commonly used instance segmentation networks (SOLOv2, Mask R-CNN, BlendMask, YOLACT, CondInst). After the proposed preprocessing pipeline, we conduct the ST-SOLOv2 to locate each DHs near the surface. Our results suggest DH number increases with elevation and decreases with slope angle from coast to inland. And DH number decreases with surface melting days as melting/freezing cycles obscure snow layering. We present a method for efficiently monitoring the DH distribution that can be used in further studies of snow radar SMB estimation.