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.

In this study, sea ice thickness (SIT) and sea ice extent (SIE) in the Bohai Sea from 2000 to 2016 were investigated. A surface heat balance equation was applied to calculate SIT using ice surface temperatures estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data with input from air temperature and wind speed from reanalyzing weather data. No trend was found in SIT during 2000–2016. The mean SIT and SIE during this period were 5.58±0.86 cm and 23×10³±8×10³ km², respectively. The largest SIT and SIE periods were observed during the second half of January and the first half of February, respectively. The Spearman correlation coefficient between mean ice thickness and average air temperature from 21 automatic weather stations around the Bohai Sea was –0.94 (P < .005), and the coefficient between median ice extent and negative accumulated temperature was –0.503 (P < .001). The rate of increase in air temperature around the Bohai Sea is 0.271°C per decade in winter for 1979–2016 (P < .05), which is much lower than that in northern polar area (0.648°C per decade). This rate has not resulted in a decreasing trend in SIT and SIE for the past 16 years in the Bohai Sea.

Sea ice adversely impacts nautical, logistical and scientific missions in polar regions. Ship navigation benefits from up-to-date sea ice analyses at both regional and local scales. This study presents a satellite-based sea ice navigation system (SatSINS) that integrates observations and scientific output from remote sensing and meteorological data to develop optimum marine navigational routes in sea ice-covered waters, especially in areas where operational ice information is usually scarce. The system and its applications are presented in the context of a decision-making process to optimize the routing of the RV Xuelong during her passage through Prydz Bay, East Antarctica during three trips in the austral spring of 2011–2013. The study assesses scientifically-generated remote sensing ice parameters for their operational use in marine navigation. Evaluation criteria involve identification of priority parameters, their spatio-temporal requirements in relation to navigational needs, and their level of accuracy in conjunction with the severity of ice conditions. Coarse-resolution ice concentration maps are sufficient to delineate ice edge and develop a safe route when ice concentration is less than 70%, provided that ice dynamics, estimated from examining the cyclonic pattern, is not severe. Otherwise, fine-resolution radar data should be used to identify and avoid deformed ice. Satellite data lagging one day behind the actual location of the ship was sufficient in most cases although the proposed route may have to be adjusted. To evaluate the utility of SatSINS, deviation of the actual route from the proposed route was calculated and found to range between 165 m to about 16.0 km with standard deviations of 2.8–6.1 km. Growth of land-fast ice has proven to be an essential component of the system as it was estimated using a thermodynamic model with input from a meteorological station.

The temporal and spatial variability of the Antarctic coastline is a clear indicator of change in extent and mass balance of ice sheets and shelves. In this study, the Canny edge detector was utilized to automatically extract high-resolution information of the Antarctic coastline for 2005, 2010, and 2017, based on optical and microwave satellite data. In order to improve the accuracy of the extracted coastlines, we developed the Canny algorithm by automatically calculating the local low and high thresholds via the intensity histogram of each image to derive thresholds to distinguish ice sheet from water. A visual comparison between extracted coastlines and mosaics from remote sensing images shows good agreement. In addition, comparing manually extracted coastline, based on prior knowledge, the accuracy of planimetric position of automated extraction is better than two pixels of Landsat images (30 m resolution). Our study shows that the percentage of deviation (<100 m) between automatically and manually extracted coastlines in nine areas around the Antarctica is 92.32%, and the mean deviation is 38.15 m. Our results reveal that the length of coastline around Antarctica increased from 35,114 km in 2005 to 35,281 km in 2010, and again to 35,672 km in 2017. Meanwhile, the total area of the Antarctica varied slightly from 1.3618 × 10⁷ km² (2005) to 1.3537 × 10⁷ km² (2010) and 1.3657 × 10⁷ km² (2017). We have found that the decline of the Antarctic area between 2005 and 2010 is related to the breakup of some individual ice shelves, mainly in the Antarctic Peninsula and off East Antarctica. We present a detailed analysis of the temporal and spatial change of coastline and area change for the six ice shelves that exhibited the largest change in the last decade. The largest area change (a loss of 4836 km²) occurred at the Wilkins Ice Shelf between 2005 and 2010.

The velocity of ice flow in the Antarctic is a crucial factor to determine ice discharge and thus future sea level rise. Feature tracking has been widely used in optical and radar imagery with fine resolution to retrieve flow parameters, although the primitive result may be contaminated by noise. In this paper, we present a series of modified post-processing steps, such as SNR thresholding by residual, complex Butterworth filters, and triple standard deviation truncation, to improve the performance of primitive results, and apply it to MODIS-based Mosaic of Antarctica (MOA) datasets. The final velocity field result displays the general flow pattern of the peripheral Antarctic. Seventy-eight out of 97 streamlines starting from seed points are smooth and continuous. The RMSE with 178 manually selected tie points is within 60 m·a⁻¹. The systematic comparison with Making Earth System Data Records for Use in Research Environments (MEaSUREs) datasets in seven drainages shows that the results regarding high magnitude and large-scale ice shelf are highly reliable; absolute mean and median difference are less than 18 m·a⁻¹, while the result of localized drainage suffered from too much tracking error. The relative differences from manually selected and random points are controlled within 8% when speed is beyond 500 m·a⁻¹, but bias and uncertainty are pronounced when speed is lower than that. The result through our accuracy control strategy highlights that coarse remote-sensed images such as Moderate Resolution Imaging Spectrophotometer (MODIS) can still offer the capability for comprehensive and long-term continental ice sheet surface velocity mapping.