Long-term ice dynamics of the Prince Harald system in Lützow-Holm Bay (LHB), East Antarctica, particularly before 1990, remain poorly investigated, hindering assessments of its historical contribution to regional mass balance. Here, we reconstructed high-resolution ice velocity fields (1973-1989) for its main components – Prince Harald 1, Prince Harald 2, and Prince Harald 3 – leveraging historical Landsat imagery with a robust photogrammetric method, a systematic framework that includes geometric correction, orthorectification, and hierarchical feature matching. Our integrated analysis, combining these velocities with assessments of basal melt, surface elevation change, ice front dynamics, and Passive Shelf Ice (PSI) behavior, reveals a stable state of the system. We find that this stability was characterized by consistent flow velocities, persistent surface thickening, minimal basal melt, and calving events predominantly within the PSI zone. These results indicate an overall trend of stability and net mass accumulation for the Prince Harald system during this period.

Understanding basal processes across ice-sheet grounding lines is crucial in accurately modeling ice-sheet dynamics and estimating global sea-level rise. The grounding line, which demarcates the specific boundary between a grounded ice sheet and a floating ice shelf, is notoriously challenging to locate precisely. Existing methods for determining grounding line location rely on indirect methods, such as tide-induced vertical ice-shelf motion (the point of flexure determining the grounding line) and ice-surface slope change (the sharp change in gradient toward being flat indicating the grounding line). In this study, we utilize ice-penetrating radar data to extract grounding line information from the Lambert-Amery glacier system. By incorporating ice bed topography and the reflection amplitude differences between ice-water and ice-bedrock interfaces, we establish an automated method to extract grounding line positions from radar survey lines. From 53 radar survey lines, we identified 85 grounding points. The comparison with the positions from an existing satellite InSAR-based grounding line product shows an average difference of 0.69±0.70 km. Tidally-induced migration of grounding lines at different points of the tidal cycle, and advance/retreat of grounding line with the evolution of the ice shelf, are the main reasons for the discrepancy between the radar-derived results and the existing grounding line products. In general, the results demonstrate the feasibility of ice-penetrating radar in confirming grounding line positions, and show great potential in constraining indirect satellite remote sensing or modelling evaluations at both regional and continental scales. Our work facilitates an ongoing effort of the Scientific Committee on Antarctic Research (SCAR)’s RINGS programme to develop gapless coverage of bed topography in the coastal regions around Antarctica.

The Pine Island Glacier (PIG) is becoming increasingly unstable as a result of global warming, with profound implications for Antarctic mass loss and sea level rise. To study the mass balance of the Antarctic Ice Sheet, accurate ice flow velocity data are essential. However, ice flow velocity products for this glacier are mainly available after the 1990s. We proposed an ice flow velocity estimating method that includes image pre-processing, image orthorectification and hierarchical matching based on Landsat and KH-9 Hexagon images. In this study, a map of the ice flow velocity of the PIG from 1973 to 1975 is generated using these images, allowing the time span of the PIG ice flow to be extended to the 1970s. The results show that the ice flow velocity has increased and in an accelerated state since the 1970s. The results offer a perspective for the study of PIG dynamics and provide a basis for estimating changes in the mass balance.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimetry System (ATLAS), enhances ice surface elevation change estimations. ATLAS significantly improves elevation accuracy from over 10 cm, as seen in ICESat’s full-waveform technique, to 2-4 cm. ICESat-2 data’s elevation quality is currently validated at the single-pulse level using the corner cube retro-reflector (CCR) method, a terrain-independent technique insensitive to ice surface conditions. This study introduces a cost-effective CCR system for validating ICESat-2 elevation data from Antarctica’s firn surface, complementing the ICESat-2 Science Team’s approach. Through three validation campaigns in Antarctica and Shanghai, China, we enhanced our CCR system in design of the prism hardware, alignment of the CCR array, and determination of valid photon data. We implemented a self-adaptive window determination strategy for valid CCR-returned signal photons to minimize the impact of inadequately returned photons from the Fraunhofer diffraction pattern’s lobes and two closely elevated CCRs. The accurate elevations of single pulses from each CCR were determined and analyzed using Global Navigation Satellite Systems (GNSS) in-situ observations during the ICESat-2 overpasses in 2020–2022. Our refined CCR validation system shows that ICESat-2’s pulse elevation has a bias of less than 3.0 cm and a precision of ±1.7 cm in the latest experiment on the Nansen ice shelf, East Antarctica.

Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) carries a photon-counting laser altimeter with an unprecedented elevation accuracy of 2–4 cm. Since its data availability in 2018, there has been a challenge for the establishment of a new data processing model that can take advantage of this satellite for accurately estimating volumetric changes in Antarctica and associated contribution to global sea level rise (GSLR). We introduce an innovative multitemporal elevation change estimation model (MECEM) that separates precipitation effects from topographic influences to eliminate their correlations and estimates the elevation change rates effectively through a spatiotemporal iterative procedure. The MECEM results are validated by using GNSS in situ observations, snow stakes measurements, and airborne altimetric survey data. The results are also compared with those from ICESat and ESA multimission radar altimetric dataset. It is demonstrated that the model is capable of estimating small thickening of$1.8~\pm ~0.1$cm yr1 in the Vostok subglacial lake region. Using ICESat-2 ATL06 data from 2019 to 2023, the model is proven to be effective in the estimation of elevation change rates in Antarctic basins of different characteristics. Our results show that an increase of$0.103~\pm ~0.001$m yr1 in thickening is found from 2017–2021 to 2019–2023 in Dronning Maud Land. Furthermore, an accelerated thinning by$- 0.12~\pm ~0.035$m yr1 is witnessed from 2003–2019 to 2019–2023 in the fast-flowing Pine Island Glacial. With more ICESat-2 data acquired, the developed MECEM model can be applied for estimating the contribution of the entire Antarctic ice sheet (AIS) to GSLR.

Hoh Xil is an uninhabited extremity secluded on the Tibetan Plateau hinterland. A complete mapping of ground motion variation in Hoh Xil is essential for in-depth understanding of terrain’s responses to climate change on the Tibetan Plateau. However, the inaccessibility and extremely harsh environment impeded extensive field investigations on landform alteration and its formative process. Such difficulty can be resolved by Interferometric Synthetic Aperture Radar (InSAR), which enables a broad detection of subtle permafrost motions at millimeter precision. This study, for the first time, accomplished a Multi-temporal InSAR (MT-InSAR) deformation mapping from 2015 to 2020 in Hoh Xil, with a wide coverage of about 200,000 km2. 1,592 Sentinel-1 images were processed based on the small baseline subset (SBAS) technique. The results show that Hoh Xil was experiencing dynamic permafrost disturbances. Thawing permafrost with both the linear subsidence rate higher than 2 mm/yr and the periodic amplitude over 2 mm was primarily detected in areas of flat or gentle slopes. The InSAR cumulative deformation is highly correlated with permafrost thawing depth. Significant lag times were identified between seasonal oscillation of InSAR deformation and land surface temperature (LST). Thermokarst landforms of retrogressive thaw slumps and thermokarst lakes broadly formed and dynamically evolved as a consequence of permafrost degradation. Particularly, widespread thawing permafrost characterized by the spatial clustering of thermokarst lakes appeared to occur in areas adjacent to large lakes. The discovered dynamic permafrost disturbances in Hoh Xil manifested even the secluded Tibetan Plateau hinterland was facing the threat of climate change.

The extent of the area covered by polar sea ice is an important indicator of global climate change. Continuous monitoring of Arctic sea ice concentration (SIC) primarily relies on passive microwave images. However, passive microwave images have coarse spatial resolution, resulting in SIC production with significant blurring at the ice–water divides. In this article, a novel multi-image super-resolution (MISR) network called progressive multiscale deformable residual network (PMDRnet) is proposed to improve the spatial resolution of sea ice passive microwave images according to the characteristics of both passive microwave images and sea ice motions. To achieve image alignment with complex and large Arctic sea ice motions, we design a novel alignment module that includes a progressive alignment strategy and a multiscale deformable convolution alignment unit. In addition, the temporal attention mechanism is used to adaptively fuse the effective spatiotemporal information across image sequence. The sea ice-related loss function is designed to provide more detailed sea ice information of the network to improve super-resolution performance and further benefit finer Arctic SIC results. Experimental results demonstrate that PMDRnet significantly outperforms the current state-of-the-art MISR methods and can generate super-resolved SIC products with finer texture features and much sharper sea ice edges. The code and datasets of PMDRnet are available athttps://doi.org/10.5061/dryad.k3j9kd590.

Polar sea ice is one of the key parameters of cryosphere and polar environmental change, which plays an important role in the study of global climate change. High-resolution monitoring of polar sea ice relies mainly on optical satellite imagery and synthetic aperture radar (SAR) data, with limited spatial and temporal coverages for many applications. Passive microwave data is an important data source for continuous observations of polar sea ice, thanks to its working ability in all-sky conditions and its wide coverage. However, it is difficult to achieve high-resolution monitoring of polar sea ice using passive microwave data due to its coarse resolution. In order to solve this problem, super resolution (SR) reconstruction technique is adopted in this paper to improve the spatial resolution of passive microwave images. SR reconstruction technique based on both single-image and multi-image are attempted. AMSR2 level 3 (L3) Products of Brightness Temperatures (BTs) for Arctic sea ice are used as experimental data, and the reconstruction results obtained from different SR methods are compared and discussed.

Surface albedo, defined as the ratio of the upward to downward solar irradiance, is an important climate parameter in the surface energy budget. In the polar region and other snow-covered areas, surface albedo feedback is an essential mechanism that contributes to climate change. The currently available long-term snow albedo products still need some improvements to achieve a higher resolution and accuracy. This paper takes Greenland ice sheet as an example. We implemented an algorithm to enhance the daily albedo product from MOD10A1 by a set of processing steps, including data pre-processing, de-nosing and gap-filling. The experimental albedo product from 2001 to 2018 of Greenland had been validated with ground-truth albedo from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) provided by the Geological Survey of Denmark and Greenland (GEUS). The preliminary validation results showed that the reanalysis albedo product is temporally and spatially continuous with RMSE under 0.05.

Historical optical photographs acquired by CORONA, ARGON and LANYARD missions, known as Declassified Intelligence Satellite Photography (DISP), date back to the satellite remote sensing in Antarctica in early 1960s. They can provide a broader perspective for studying the early Antarctica by recovering geophysical parameters and products. However, the quality of the early optical satellite images is relatively poor due to the imaging technique used, long-term storage of the film and analogue-to-digital scanning processes. Improvements should be applied to processing methods to make these valuable images suitable for analyzing changes in the Antarctica. In this paper, we introduced our methods of geometric position, hierarchical image matching and parallax decomposing, then demonstrated that these images can be successfully applied to applications, such as DEM reconstruction and ice flow mapping.

We propose a new framework of systematic fracture mapping and major calving event prediction for Filchner-Ronne Ice Shelf (FRIS), Antarctica using multisource satellite data, including optical imagery, SAR imagery, altimetric data, and stereo mapping imagery. 2D mapping of fractures in FRIS is conducted using optical and SAR images and 3D information of two large rifts, Rift 1 and 2, are extracted from ZY-3 and WorldView-2 stereo images as well as ICESat data. Based on the results of the 2D and 3D fracture mapping, the spatial and temporal pattern of the overall fracture changes and large rift evolution are estimated and analyzed. The overall fracture observations do not seem to suggest immediate significant impacts on the stability of the shelf. However, the most active regional fracturing activities occurred at the front of Filchner Ice Shelf. The fracture mapping results can be used to improve the reliability of ice shelf modeling and as support for further analyses of ice shelf stability.

A long term assessment of the Antarctic ice sheet elevation change trend from 2003 to 2016 has been carried out using a combination of ICESat (2003-2008), Envisat (2003-2008) and CryoSat-2 (2010-2016). The contemporaneous ICESat and Envisat results showed a consistency in the estimated elevation change rate of Antarctica during 2003 to 2008: 0.1±0.1 cm a-1to 0.3±0.6 cm a-1, respectively, which were then combined as the overall elevation change of Antarctica during 2003 to 2008. The recent CryoSat-2 result suggested an overall elevation change rate of Antarctic ice sheet of from 2010 to 2016. Furthermore, the elevation change of East Antarctica is relatively small, while West Antarctica appears to have an elevation decrease trend. The above is based on our preliminary data processing and analysis results. The high uncertainties in Basin 15 and Antarctic Peninsula need to be further investigated. We will report our improved results at the conference.

Ice flow velocity is used to estimate ice mass changes in glaciers and is a significant indicator of the stability of the Antarctica ice sheet in global change studies. The existing regional Antarctica ice flow speed maps are usually derived from radar or optical satellite observations of modern satellites since the 1970s. This paper presents a new analytical photogrammetric method for estimating Antarctica ice flow velocity fields by using film-based stereo ARGON photographs collected in the 1960s. The key of the proposed innovative method is a parallax decomposition that separates the effect of the terrain relief from the ice flow motion. An innovative implementation strategy is developed by using a framework that involves key techniques of hierarchical stereo image matching, ice flow direction determination, parallax decomposition, and ice flow speed estimation. This method is applied in the Rayner glacier in eastern Antarctica by using two sets of ARGON images with a two-month interval in 1963. The produced digital terrain model and speed map achieved a ground position accuracy of 61 m and a speed accuracy of 70 m a-1. A comparison with recent products from 2000 to 2010 shows no significant topographic changes in the study area. Furthermore, the speed around the grounding line remained at the same level, while the speed in the ice shelf front decreased by 73 m a-1. The ice shelf front advanced by approximately 7 km over more than 40 years. Overall, the observation results indicate favorable conditions for the stability of the Rayner glacier-ice shelf system.

There are a variety of remote sensing products of Antarctica that cover different time periods and published by different research groups. The resolution and accuracy for these products are quite different due to original data sources, extraction methods, etc. The aim of this research is to assess the quality and investigate changes that can be detected in three main categories of Antarctic remote sensing products including groundling lines, coastlines and surface elevation models (DEM). All the products are available at National Snow and Ice Data Center (NSIDC). In order to identify changes and uncertainties of these products, cross-validation strategies are developed for the quality assessment when there is a lack of ground truth data. The quality assessment results for each remote sensing product are presented in the paper.

This paper proposes a novel two-stage method for remote sensing image segmentation. First, initial small segments, also called subobject primitives (sub-OPs), are obtained using edge-constrained watershed segmentation and edge allocation. These segments are gradually merged into a larger segment until the edge-controlled limits are reached, thereby creating the initial OPs. In this stage, a concept of hard-boundary ratio is proposed to control the merge effectively. Second, nonconstrained merging is conducted on the OPs, which results in final segmentation. In addition, a repeatable pairwise segment-merging scheme is utilized. This scheme improves method efficiency and accuracy. Comprehensive experiments comparing this new method with the multiresolution segmentation method of eCognition were conducted. Results show that this new method has the following advantages: higher segmentation accuracy and OP boundary precision and less dependence on the scale parameter.

We present a new method for remote sensing image segmentation, which utilizes both spectral and texture information. Linear filters are used to provide enhanced spatial patterns. For each pixel location, we compute combined spectral and texture features using local spectral histograms, which concatenate local histograms of all input bands. We regard each feature as a linear combination of several representative features, each of which corresponds to a segment. Segmentation is given by estimating combination weights, which indicate segment ownership of pixels. We present segmentation solutions where representative features are either known or unknown. We also show that feature dimensions can be greatly reduced via subspace projection. The scale issue is investigated, and an algorithm is presented to automatically select proper scales, which does not require segmentation at multiple-scale levels. Experimental results demonstrate the promise of the proposed method.

We present a novel method for segmenting images with texture and nontexture regions. Local spectral histograms are feature vectors consisting of histograms of chosen filter responses, which capture both texture and nontexture information. Based on the observation that the local spectral histogram of a pixel location can be approximated through a linear combination of the representative features weighted by the area coverage of each feature, we formulate the segmentation problem as a multivariate linear regression, where the solution is obtained by least squares estimation. Moreover, we propose an algorithm to automatically identify representative features corresponding to different homogeneous regions, and show that the number of representative features can be determined by examining the effective rank of a feature matrix. We present segmentation results on different types of images, and our comparison with another spectral histogram based method shows that the proposed method gives more accurate results.

High-resolution (submeter) orbital imagers have opened up new possibilities for Mars topographic mapping with unprecedented precision. While the typical sensor model for Martian orbiters has been the linear-array charge-coupled device (CCD), the High-Resolution Imaging Science Experiment (HiRISE) instrument is based on a more complicated structure involving a combination of 14 separate linear-array CCDs. To take full advantage of this high-resolution capability without compromising imaging geometry, we have developed a rigorous photogrammetric model for HiRISE stereo image processing in which third-order polynomials are used to model the change in exterior-orientation parameters over time. A coarse-to-fine hierarchical matching approach was developed, and its performance was evaluated based on manually matched image points and manually measured features for a test area at the Mars Exploration Rover Spirit landing site. Using automatically selected tie points, we performed bundle adjustment (BA) to improve the accuracy of image pointing data and remove or reduce inconsistencies between the stereo pair and inconsistencies between overlapping CCDs in the same image mosaic. A method for the incorporation of jitter terms into the BA was developed and proved to be effective. We created a 1-m-resolution digital elevation model and an orthophoto using this methodology and compared them with topographic products from the U.S. Geological Survey.

An automatic method for road extraction from satellite imagery is presented. The core of the proposed method is locally excitatory globally inhibitory oscillator networks (LEGION). The road extraction task is decomposed into three stages. The first stage is image segmentation by LEGION. In the second stage, the medial axis of each segment is computed, and the medial axis points corresponding to narrow regions are selected. The third is the road grouping stage. Alignment-dependent connections between selected points are established, and LEGION is utilized to group well-aligned points, which represent the extracted roads. Due to the selective gating mechanism of LEGION, different roads in an image are grouped separately. Road extraction results on synthetic and real images are presented. A comparison with other methods shows that the proposed method produces very competitive extraction results.

Web service-based workflow management systems have garnered considerable attention for automating and scheduling dependent operations. Such systems often support user preferences, e.g., time of completion, but with the rebirth of distributed computing via the grid/cloud, new challenges are abound: multiple disparate data sources, networks, nodes, and the potential for moving very large datasets. In this paper, we present a framework for integrating QoS support in a service workflow composition system. The relationship between workflow execution time and accuracy is exploited through an automatic workflow composition scheme. The algorithm, equipped with a framework for defining cost models on service completion times and error propagation, composes service workflows which can adapt to user's QoS preferences.

We present an automatic method for road extraction from satellite imagery. The core of the proposed method is locally excitatory globally inhibitory oscillator networks (LEGION). We decompose the road extraction task into three stages. The first stage is image segmentation by LEGION. In the second stage, we compute the medial axis of each segment and select the segments with narrow widths. The third is the road grouping stage. With the medial axes, alignment-dependent connections between medial axis points are established and LEGION is utilized to group the well-aligned medial axes, which represent extracted road segments. Due to the selective gating mechanism of LEGION, different roads in an image are grouped separately. Experimental results on synthetic and real images show the effectiveness of this method.

With the advent of the data grid came a novel distributed scientific computing paradigm known as service-oriented sci-ence. Among the plethora of systems included under this framework are scientific workflow management systems, which enable large-scale process scheduling and execution. To en-sure quality of service, these systems typically seek to min-imize workflow execution time as well as costs for slices of data grid access. The geospatial domain, among other sci-ences, involves yet another optimization factor, the accu-racy of results. The relationship between execution time and workflow accuracy can often be exploited to offer more flexibility in handling user preferences. We present a system which meets user constraints through a dynamic adjustment of the accuracy of workflow results.