Automation of Ice Fractures and Calving Events Monitoring Using Medical Imaging Ridge Detection Algorithms

Ice shelves, i.e. the floating extension of the AIS, are playing an active role in controlling ice loss from the Antarctic ice sheet. Laterally constraint in embayment or by ice rises, they are participating as regulators of the ice discharge, by exerting a back stress to the ice flow. When losing mass, these ice shelves lose their gatekeeper property, with potential local destabilization of the AIS. Losing mass from calving is a sophisticated process that is rarely coupled with observations in ice sheet models. However, calving and damages are visible in SAR remote sensing products. In this study, we built the hypothesis that state-of-the-heart ridge detection techniques from the medical imaging field can be transposed to the cryosphere field. Looking at the local Hessian matrix in SAR acquisitions, we analyzed the eigenvectors that indicate the presence of ridges. Over ice shelves, these edges correspond to the calving front of the ice shelf, or crevasses. Using time series, we can monitor the evolution of crack propagation and calving events. Results over Pine Island Glacier and the Brunt Ice Shelf show a precise delineation of calving events, as well as the damaged areas. These encouraging results support the idea of the integration of ice damage detection from SAR remote sensing into ice sheet models

Discussing an extreme mock/what-if scenario over the antarctic peninsula: the effect of intense melt on surface mass balance

peer reviewedThis discussion paper interprets the findings of a recent study comparing melt estimates from the regional atmospheric model MAR, those derived from Automatic Weather Stations (AWS), and microwave remote sensing images over the Antarctic Peninsula from 2019 to 2021. Our interpretation reveals a paradox: MAR overestimates melt when compared to AWS-based melt estimates, yet underestimates melt when compared to satellite imagery. This discrepancy underscores a fundamental gap in our understanding of surface processes. To illustrate the potential implications of this gap, we present a fictional (“what-if”) scenario that explores an extreme case of melting, based on parametrizations from Kittel et al., 2022, and the outliers of Dethinne et al., 2023. We examine the potential impact on the ice sheet's surface mass balance (SMB), drawing parallels with the situation in Greenland during the 1990s, where increased melt production had cascading effects on SMB. Moreover, we highlight that the presence of liquid water at the surface of the snowpack can be a precursor to significant destabilization processes over ice shelves, although this aspect is not the focus of our current paper. By opening a debate on the accuracy and interpretation of melt modeling, we aim to draw attention to the potential consequences of extreme melting events on the Antarctic Ice Sheet's SMB and stability

Plug-In of CSL InSAR Suite (CIS) Functionalities into the SentiNel Application Platform (SNAP) Software

For more than 20 years, the Centre Spatial de Liège (CSL) has developed Synthetic Aperture Radar software solutions in a suite called CIS (CSL InSAR Suite). CIS is a command-line software written in C, dedicated to the processing of synthetic aperture radar data, allowing the production of analysis-ready outputs such as displacement maps, flood extend, or fire monitoring. Advanced methods are also included, making CIS distinct from other competing SAR suites. With more than 500 000 registered users, the open-access SentiNel Application Platform - SNAP, developed since 2015 by Brockmann Consult (Hamburg, Germany), has become the standard tool for processing remote sensing data. It was originally tailored to Sentinel 1-3 images, but now accommodates data from most common satellite images, including non-ESA missions (e.g., ICEYE, NOVASAR). The largest part of SNAP users belongs to the radar (Sentinel-1) community. SNAP integrates classical operators of remote sensing, including data reading, co-registration, calibration, raster algebra, and so on. A major particularity of practical and strategical interest is that SNAP is available is golden-open-access, allowing to access directly to the core codes and modify it. Moreover, SNAP supports the inclusion of plugins with a cookbook to developers. This abstract reports the work of progressive inclusion of the CIS software modules into the SNAP open-source software, as plugins. To fulfill this objective, we are using the Standalone Tool Adapter of SNAP to include external command-line functionalities. The objective of the tool adapter is to create the paths that will link the external application to the SNAP software. We started the migration using a series of simple to complex tasks in different programming languages (C/C++, Python, and Matlab). CIS plugins in SNAP will be accessible from a new dedicated menu in the user interface. Currently, we integrated the coherence tracking and multiple aperture interferometry to the SNAP software. Additional tools will be included in future developments. During the event, a presentation of the different tools will be performed. Interested scientists are invited to contact directly the authors to request help with the installation of the plugin at the session.SAOCOM Post Launc

Empirical Removal of Tides and Inverse Barometer Effect on DInSAR From Double DInSAR and a Regional Climate Model

Ice shelves-the floating extensions of the Antarctic ice sheet-regulate the Antarctic contribution to sea-level rise by restraining the grounded ice flowing from upstream. Therefore, ice-shelf change (e.g., ice-shelf thinning) results in accelerated ice discharge into the ocean, which has a direct effect on sea level. Studying ice-shelf velocity allows the monitoring of the ice shelves' stability and evolution. Differential synthetic aperture radar interferometry (DInSAR) is a common technique from which highly accurate velocity maps can be inferred at high resolution. Because ice shelves are afloat, small sea-level changes-i.e., ocean tides and varying atmospheric pressure (aka inverse barometer effect) lead to vertical displacements. If not accounted for in the interferometric process, these effects will induce a strong bias in the horizontal velocity estimation. In this article, we present an empirical DInSAR correction technique from geophysical models and double DInSAR, with a study on its variance propagation. The method is developed to be used at large coverage on short timescales, essential for the near-continuous monitoring of rapidly changing areas on polar ice sheets. We used Sentinel-1 SAR acquisitions in interferometric wide and extra -wide swath modes. The vertical interferometric bias is estimated using a regional climate model (MAR) and a tide model (CATS2008). The study area is located on the Roi Baudouin Ice Shelf in Dronning Maud Land, East Antarctica. Results show a major decrease (67 m·a -1 ) in the vertical-induced displacement bias.Fil: Glaude, Quentin. Université Libre de Bruxelles; Bélgica. Université de Liège; BélgicaFil: Amory, Charles. Universite de Liege. Faculty Of Applied Sciences.; BélgicaFil: Berger, Sophie. Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung; Alemania. Université Libre de Bruxelles; BélgicaFil: Derauw, Dominique Maurice. Universidad Nacional de Río Negro. Sede Alto Valle. Instituto de Investigaciones en Paleobiología y Geología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pattyn, Frank. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Barbier, Christian. Université de Liège; BélgicaFil: Orban, Anne. Université Catholique de Louvain; Bélgic

Coherence Tracking and its Adaptation to TOPSAR Acquisition Mode - Study case over Antarctic Ice Shelves

Synthetic Aperture Radar (SAR) Remote Sensing already proved as an ideal solution to determine surface displacements, thanks to its day-and-night and cloud-free characteristics. Furthermore, more and more acquisitions are becoming available for users (Radarsat Constellation Mission, Cosmo Skymed, SAOCOM, Sentinel-1, and so forth). To detect displacements, SAR has several techniques. Using SAR images at different times from slightly different points of view, we can observe surface movements by phase shift measurements between acquisitions. These techniques belong to the branch of differential interferometry (DInSAR, SBAS, MSBAS, PSI, MAI, BOI, and so forth). DInSAR can determine displacements according to the line of sight of the sensor with an accuracy that can go below the centimeter, with a sensor at several hundreds of kilometers distance. This partly explained the success of DInSAR. Then, to reconstruct the bi- or tri-dimensional displacements, we need other measures from other orbits. Combining a great number of images from several orbits, we can reconstruct the full vectorial components of the surface movement. Unfortunately, this abundance of orbits is far from achievable everywhere on Earth. In particular, Antarctica has many geographical areas where only a limited number of orbits is available. Besides, techniques based on SAR interferometry are limited by other factors. Among them, the magnitude of the displacements can introduce a decorrelation such that the wavefronts combination emitted from two different times does not give a coherent signal. This temporal decorrelation is particularly remarkable in coastal regions of Antarctica, where the revisit time of Sentinel-1 (6 or 12 days, depending on the region) allows the scatterers to move from one picture element to another. In these cases, it is possible to employ another family of techniques, based on the tracking of feature elements at the surface. In SAR remote sensing, we talk of speckle tracking. In speckle tracking, the technique uses two SAR images at different acquisition times. According to a defined spatial sampling, we search in the second image a translation in picture elements that maximizes the local correlation. From this translation is deduced a bi-dimensional displacement and, in fine, a velocity. This technique is less precise than InSAR-based methods but is less impacted by temporal decorrelation, while also directly brings a 2D velocity vector. The limits of applicability between InSAR and Speckle tracking are not fixed and, when the two options are possible, we would always opt for phase-based measurements thanks to their incredible accuracy. It is in this context that coherence tracking was born by taking the best of the two approaches. Coherence tracking determines bidimensional displacements by maximizing the quality of the interferogram at a local scale, through the coherence estimation. The use of the phase in a tracking approach allows recovering the location of ground scatterers in the second image. Then, it is possible to determine a tracked interferogram, that contains the displacement along the line-of-sight, with good accuracy. Coherence tracking is one way to circumvent the issue of temporal decorrelation induced by fast-moving areas. Nevertheless, the TOPSAR acquisition mode introduces a phase bias to be taken into account before the processing. By steering its sensor during the acquisition, Sentinel-1 contains in its signal a strong azimuthal phase ramp. While this phase ramp can be canceled out in classical interferometry, this is not the case in a tracked interferogram. In this research, we present the coherence tracking technique and the added-value of the phase information in offset tracking methods. Then we explain how to adapt the approach in TOPSAR data, in particular with Sentinel-1. Results are related to the study of Ice Shelves in East Antarctica. More precisely, we are focusing on the Roi Baudouin Ice Shelf, in Dronning Maud Land. Results are finally compared to traditional approaches

Interest of the Assimilation of Surface Melt Extent Derived From Passive and Active Microwave Satellites Into the Regional Climate Model MAR Over the Antarctic Peninsula

editorial reviewedMelting ice sheets are a major contributor to the rising sea level. At the Liège University, the Regional Climate Model MAR (Modèle Atmosphérique Régional) has been developed to monitor and study the current and future evolution of various properties of ice sheets. However, uncertainties remain on the surface melt extent upon Antarctic ice sheets as models are subject to error propagation and need some external data to model the climate. In Antarctica, unlike Greenland, the produced surface meltwater does not leave the ice sheet through visible rivers in which the quantity of meltwater can be estimated. Remote sensing is then the only product able to provide an estimation of the surface melt extent with a satisfying spatial and temporal coverage. The assimilation of melt spatial extent estimated by remote sensing allows the mitigation of the uncertainties linked to the models as well as a better quantification of the melt quantity. In this research, active (Sentinel-1) and passive (AMSR2 & SSMIS) microwave satellite data are assimilated into MAR model over the Antarctic Peninsula, where surface melt has caused hydrofracturing and destabilization of ice shelves in the past. The assimilation of the different satellite products is also conducted to study the effect of spatial resolution on melt detection, Sentinel-1 having a pixel size of a few meters while passive satellites are at the 10km scale. This difference can be crucial upon the Peninsula as Foehn effects are occurring locally and can generate local surface melt, not detectable while using a coarser resolution

Comparison Between Surface Melt Estimation From Sentinel-1 Synthetic Aperture Radar and a Regional Climate Model. Case Study Over the Roi Baudouin Ice Shelf, East Antarctica

peer reviewedAntarctica is the largest potential contributor to sea-level rise and needs to be monitored. It is also one of the first victims of global warming. However, it is often difficult to obtain high-resolution data on this vast and distant continent. Thanks to the Copernicus space program providing free and open access to high-quality data, this paper aims to show the complementarity between Sentinel-1 images and Modèle Atmosphérique régional (MAR) data over Antarctica. This study is conducted over Roi Baudouin Ice Shelf. The complementarity between the two datasets is established by a quantitative, temporal, and spatial comparison of the amplitude information of the radar signal and several variables modelled by MAR. Comparisons show strong spatial correlations between MAR variables representing melt and the backscatter coefficient recorded by the satellite. While temporal and quantitative analyses also give impressive results, further investigations are required to explain contrasting behaviors in other different areas of the ice shelf

Sentinel-1 azimuth subbanding for multiple aperture interferometry - Test case over the Roi Baudoin ice shelf, east Antartica

peer reviewedAs an extension of Synthetic Aperture Radar Interferom- etry, Multiple Aperture Interferometry (MAI) is a spectral diversity technique that allows the determination of azimuth displace- ments from phase shift measurements. This is made possi- ble through the creation of backward- and forward-looking Single-Look-Complex (SLC) data. Then, the phase differ- ence between the backward and forward-looking interfero- gram is translated into a displacement. Using SLC data, MAI requires a proper azimuth splitband operator. Different tech- niques exist to split the azimuth band, but they are often too briefly described in the MAI literature. In this conference pa- per, we analyze the signal properties of the Sentinel-1 TOPS acquisition mode and define an azimuth subbanding proto- col. In particular, we look at the role of de-apodization and apodization in the band filtering operation. We focus our anal- ysis on Sentinel-1 data in Interferometric Wideswath mode over the Roi Baudouin Ice Shelf, East Antarctica.MIMO (Monitoring melt where Ice Meets Ocean); SMAIAD (Sentinel-1 Multiple Aperture Interferometry for Azimuth Displacement Retrieval

Sensitivity of the MAR regional climate model snowpack to the parameterization of the assimilation of satellite-derived wet-snow masks on the Antarctic Peninsula

peer reviewedAbstract. Both regional climate models (RCMs) and remote sensing (RS) data are essential tools in understanding the response of polar regions to climate change. RCMs can simulate how certain climate variables, such as surface melt, runoff and snowfall, are likely to change in response to different climate scenarios but are subject to biases and errors. RS data can assist in reducing and quantifying model uncertainties by providing indirect observations of the modeled variables on the present climate. In this work, we improve on an existing scheme to assimilate RS wet snow occurrence data with the “Modèle Atmosphérique Régional” (MAR) RCM and investigate the sensitivity of the RCM to the parameters of the scheme. The assimilation is performed by nudging the MAR snowpack temperature to match the presence of liquid water observed by satellites. The sensitivity of the assimilation method is tested by modifying parameters such as the depth to which the MAR snowpack is warmed or cooled, the quantity of water required to qualify a MAR pixel as “wet” (0.1 % or 0.2 % of the snowpack mass being water), and assimilating different RS datasets. Data assimilation is carried out on the Antarctic Peninsula for the 2019–2021 period. The results show an increase in meltwater production (+66.7 % on average, or +95 Gt), along with a small decrease in surface mass balance (SMB) (−4.5 % on average, or −20 Gt) for the 2019–2020 melt season after assimilation. The model is sensitive to the tested parameters, albeit with varying orders of magnitude. The prescribed warming depth has a larger impact on the resulting surface melt production than the liquid water content (LWC) threshold due to strong refreezing occurring within the top layers of the snowpack. The values tested for the LWC threshold are lower than the LWC for typical melt days (approximately 1.2 %) and impact results mainly at the beginning and end of the melting period. The assimilation method will allow for the estimation of uncertainty in MAR meltwater production and will enable the identification of potential issues in modeling near-surface snowpack processes, paving the way for more accurate simulations of snow processes in model projections