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Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice su...

Sentinel-1 C-band synthetic aperture radar (SAR) images can be used to observe the drift of icebergs over the Southern Ocean with around 1–3 d of temporal resolution and 10–40 m of spatial resolution. The Google Earth Engine (GEE) cloud-based platform allows processing of a large quantity of Sentinel-1 images, saving time and computational resources. In this study, we process Sentinel-1 data via GEE to detect and track the drift of iceberg B43 during its lifespan of 3 years (2017–2020) in the Southern Ocean. First, to detect all candidate icebergs in Sentinel-1 images, we employ an object-based image segmentation (simple non-iterative clustering – SNIC) and a traditional backscatter threshold method. Next, we automatically choose and trace the location of the target iceberg by comparing the centroid distance histograms (CDHs) of all detected icebergs in subsequent days with the CDH of the reference target iceberg. Using this approach, we successfully track iceberg B43 from the Amundsen Sea to the Ross Sea and examine its changes in area, speed, and direction. Three periods with sudden losses of area (i.e., split-offs) coincide with periods of low sea ice concentration, warm air temperature, and high waves. This implies that these variables may be related to mechanisms causing the split-off of the iceberg. Since the iceberg is generally surrounded by compacted sea ice, its drift correlates in part with sea ice motion and wind velocity. Given that the bulk of the iceberg is under water (∼30–60 m freeboard and ∼150–400 m thickness), its motion is predominantly driven by the westward-flowing Antarctic Coastal Current, which dominates the circulation of the region. Considering the complexity of modeling icebergs, there is a demand for a large iceberg database to better understand the behavior of icebergs and their interactions with surrounding environments. The semi-automated iceberg tracking based on the storage capacity and computing power of GEE can be used for this purpose.

Observing sea ice by very high-resolution (VHR) images not only improves the quality of lower-resolution remote sensing products (e.g., sea ice concentration, distribution of melt ponds and pressure ridges, sea ice surface roughness, etc.) by providing details on the ground truth of sea ice, but also assists sea ice fieldwork. In this study, two fieldwork-based methods are proposed, one for the practical acquisition of VHR images over drifting Arctic sea ice using low-cost commercial off-the-shelf (COTS) sensors equipped on a helicopter, and the other for quantifying the compensating effect from continuously drifting sea ice that reduces geolocation uncertainty in the image mosaicking procedure. The drifting trajectory of the target ice was yielded from that recorded by an icebreaker that was tightly anchored to the floe and was then used to reversely compensate the locations of acquired VHR images. After applying the compensation, three-dimensional geolocation errors of the VHR images were decreased by 79.3% and 24.2% for two pre-defined image groups, respectively. The enhanced accuracy of the imaging locations was affected by imaging duration causing variable drifting distances of individual images. Further applicability of the mosaicked VHR image was discussed by comparing it with a TerraSAR-X synthetic aperture radar image containing the target ice, suggesting that the proposed methods can be used for precise comparison with satellite remote sensing products.

The outer Ross Sea continental shelf has experienced large variations in ice sheet extent over the Pleistocene that are theorized to be largely driven by changes in the westward-flowing Antarctic Slope Current (ASC) at the continental shelf break. This current regulates southward incursions of warm modified Circumpolar Water, and it is thought to have triggered past marine ice sheet retreat. Additionally, expansions of grounded ice sheets on the continental shelf have fundamentally altered the Ross Sea water mass formation processes, influencing surface water salinity, sea ice cover, nutrient utilization, deep-water ventilation, and primary productivity. Here, we report the geochemical, physical properties, grain size, bulk δ15N, and diatom records during the late Pleistocene from two sediment cores from the Iselin Bank on the outermost continental shelf in the Ross Sea. These core sites were not overridden by grounded ice sheets during the late Pleistocene glacial-interglacial cycles, allowing for a continuous archive of glacimarine environments that were influenced by the ASC. Interglacial periods are typically characterized by high surface water productivity and nutrient utilization, with Chaetoceros resting spores indicating nutrient limitation under open ocean conditions, and glacial periods are typically characterized by low surface water productivity and nutrient utilization, with sea ice diatoms and planktonic foraminifers indicating light limitation under extensive sea ice/ice margin proximal conditions. A grain size analysis indicates coarse-skewed distributions and winnowing in the Iselin Bank region during cold periods. The winnowing may be related to enhanced ASC flow instead of density driven shelf water outflow.

Unmanned aerial vehicles (UAVs), so-called ‘drones’, have been widely used to monitor wild animals. Here, we tested a UAV with red, green, and blue (RGB) and thermal cameras to detect free-living birds in a high Arctic region in North Greenland and in a restricted area in the Republic of Korea. Small flocks of molting pink-footed geese (Anser brachyrhynchus) near sea ice and incubating common ringed plovers (Charadrius hiaticula) in the Arctic environment were chosen for the RGB and thermal image studies. From the acquired images, we built mosaicked RGB images and coregistered thermal images, and estimated the animal shapes. Our results showed that geese were discriminated in both RGB and thermal images with water and sea ice backgrounds. Incubating plover bodies were not distinguished in RGB images due to their cryptic coloration, but they were detected in thermal images with cold background areas in the Arctic environment. We further conducted a blind survey in a restricted area under military control in Korea near the breeding sites of black-faced spoonbill (Platalea minor), which is an endangered species. From UAV flights with RGB and thermal cameras operated out of the restricted area, we acquired images of white objects in the mudflats and verified that the objects were resting spoonbills by watching the birds. We suggest that thermal cameras and UAVs can be applied to monitor animals in extreme environments and in restricted areas and help researchers find cryptic wader nests.

Backscattering coefficients of Sentinel-1 synthetic aperture radar (SAR) data of drifting multi-year sea ice in the western Beaufort Sea during the transition period between the end of melting and onset of freeze-up are analyzed, in terms of the incidence angle dependence and temporal variation. The mobile sea ice surface is tracked down in a 1 km by 1 km region centered at a GPS tracker, which was installed during a field campaign in August 2019. A total of 24 Sentinel-1 images spanning 17 days are used and the incidence angle dependence in HH- and HV-polarization are −0.24 dB/deg and −0.10 dB/deg, respectively. Hummocks and recently frozen melt ponds seem to cause the mixture behavior of surface and volume scattering. The normalized backscattering coefficients in HH polarization gradually increased in time at a rate of 0.15 dB/day, whereas the HV-polarization was relatively flat. The air temperature from the ERA5 hourly reanalysis data has a strong negative relation with the increasing trend of the normalized backscattering coefficients in HH-polarization. The result of this study is expected to complement other previous studies which focused on winter or summer seasons in other regions of the Arctic Ocean.

Sea ice motion and deformation have generally been measured using low-resolution passive microwave or mid-resolution radar remote sensing datasets of daily (or few days) intervals to monitor long-term trends over a wide polar area. This feasibility study presents an application of high-resolution optical images from operational satellites, which have become more available in polar regions, for sea ice motion and deformation measurements. The sea ice motion, i.e., Lagrangian vector, is measured by using a maximum cross-correlation (MCC) technique and multi-temporal high-resolution images acquired on 14–15 August 2014 from multiple spaceborne sensors on board Korea Multi-Purpose Satellites (KOMPSATs) with short acquisition time intervals. The sea ice motion extracted from the six image pairs of the spatial resolutions were resampled to 4 m and 15 m yields with vector length measurements of 57.7 m root mean square error (RMSE) and −11.4 m bias and 60.7 m RMSE and −13.5 m bias, respectively, compared with buoy location records. The errors from both resolutions indicate more accurate measurements than from conventional sea ice motion datasets from passive microwave and radar data in ice and water mixed surface conditions. In the results of sea ice deformation caused by interaction of individual ice floes, while free drift patterns of ice floes were delineated from the 4 m spatial resolution images, the deformation was less revealing in the 15 m spatial resolution image pairs due to emphasized discretization uncertainty from coarser pixel sizes. The results demonstrate that using multi-temporal high-resolution optical satellite images enabled precise image block matching in the melting season, thus this approach could be used for expanding sea ice motion and deformation dataset, with an advantage of frequent image acquisition capability in multiple areas by means of many operational satellites.

Satellite remote sensing can be used effectively with a wide coverage and repeatability in large-scale Arctic sea-ice analysis. To produce reliable sea-ice information, satellite remote-sensing methods should be established and validated using accurate field data, but obtaining field data on Arctic sea-ice is very difficult due to limited accessibility. In this situation, digital surface models derived from aerial images can be a good alternative to topographical field data. However, to achieve this, we should discuss an additional issue, i.e., that low-textured surfaces on sea-ice can reduce the matching accuracy of aerial images. The matching performance is dependent on the matching cost and search window size used. Therefore, in order to generate high-quality sea-ice surface models, we first need to examine the influence of matching costs and search window sizes on the matching performance on low-textured sea-ice surfaces. For this reason, in this study, we evaluate the performance of matching costs in relation to changes of the search window size, using acquired aerial images of Arctic sea-ice. The evaluation concerns three factors. The first is the robustness of matching to low-textured surfaces. Matching costs for generating sea-ice surface models should have a high discriminatory power on low-textured surfaces, even with small search windows. To evaluate this, we analyze the accuracy, uncertainty, and optimal window size in terms of template matching. The second is the robustness of positioning to low-textured surfaces. One of the purposes of image matching is to determine the positions of object points that constitute digital surface models. From this point of view, we analyze the accuracy and uncertainty in terms of positioning object points. The last is the processing speed. Since the computation complexity is also an important performance indicator, we analyze the elapsed time for each of the processing steps. The evaluation results showed that the image domain costs were more effective for low-textured surfaces than the frequency domain costs. In terms of matching robustness, the image domain costs showed a better performance, even with smaller search windows. In terms of positioning robustness, the image domain costs also performed better because of the lower uncertainty. Lastly, in terms of processing speed, the PC (phase correlation) of the frequency domain showed the best performance, but the image domain costs, except MI (mutual information), were not far behind. From the evaluation results, we concluded that, among the compared matching costs, ZNCC (zero-mean normalized cross-correlation) is the most effective for sea-ice surface model generation. In addition, we found that it is necessary to adjust search window sizes properly, according to the number of textures required for reliable image matching on sea-ice surfaces, and that various uncertainties due to low-textured surfaces should be considered to determine the positions of object points.

Quantitative analysis of CaO in limestone mining is mandatory, not only for ore exploration, but also for grade control. A partial least squares regression (PLSR) CaO estimation technique was developed for limestone mining. The proposed near-infrared spectroscopy (NIR)-based method uses reflectance spectra of the rock sample surface in the visible to short-wave infrared wavelength regions (350–2500 nm (4000–28,571 cm−1)) without the need to crush and pulverize the rock samples. The root mean square (RMS) error of CaO estimation using limestone ore fragment was 1.2%. The CaO content estimated by the PLSR method was used to predict average CaO content of composite samples with a sample size of 15, which resulted in an RMS error of 0.3%. The prediction accuracy with moisture on sample surfaces was also examined to find out if the NIR-based method showed a similar RMS error. Results suggest that the NIR technique can be used as a rapid assaying method in limestone workings with or without the presence of groundwater.