Abstract Measuring the radar backscatter characteristics of glacier ice at different frequencies and incidence angles is fundamental to predicting the glacier mapping performance of a sensor. However, such measurements at 94 GHz do not exist. To address this knowledge gap, we collected 94 GHz radar backscatter data from the surface of Rhônegletscher in Switzerland using the All‐Weather Volcano Topography Imaging Sensor (AVTIS2) real‐aperture Frequency Modulated Continuous Wave radar. We determine the mean normalized radar cross section to be −9.9 dB. The distribution closely follows a log‐normal distribution with a high goodness of fit ( R 2 = 0.99) which suggests that radar backscatter is diffuse and driven by surface roughness. Further, we quantified the uncertainty of AVTIS2 3D point clouds to be 1.30–3.72 m, which is smaller than other ground‐based glacier surface mapping radars. These results demonstrate that glacier surfaces are an efficient scattering target at 94 GHz, hence demonstrating the suitability of millimeter‐wave radar for glacier monitoring.
<p>This study provides an overview of the Earth observation and remote sensing activities of Svalbard Integrated Arctic Earth Observing System (SIOS) undertaken when building an observing system for sustained measurements in and around Svalbard to address Earth System Science (ESS) questions. SIOS research infrastructures are distributed across and around Svalbard for acquiring long-term in situ observations. These in situ measurements are not only useful for various ground-based studies, but also applicable for calibration and validation (Cal/Val) of current and future satellite missions e.g. Copernicus Imaging Microwave Radiometer (CIMR), Radar Observing System for Europe - L-Band (ROSE-L ) or Sentinel-1,2, Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL), Sentinel-5 Precursor, and Copernicus Hyperspectral Imaging Mission for Environment (CHIME). Better integration of in situ and satellite-based measurements is crucial for building a coherent network of observations to fill observational gaps. Additionally. complementing in situ measurements with satellite data is a prime necessity to generate operational reliable geoinformation products using traditional and advanced methods, for example, mapping vegetation extent in Svalbard using Sentinel-2 data complemented with in situ measurements of spectral reflectance collected by SIOS infrastructure. SIOS&#8217;s remote sensing activities are developed in SIOS knowledge centre (SIOS-KC) under the direction of the remote sensing working group (RSWG). This study highlights our current activities, goals for the next five years (2022-2026) and future activities with the intention of attracting potential collaborations to support achieving these goals. The study discusses SIOS&#8217;s present activities, including (1) capacity building e.g., webinar series, online conference, and training courses on EO and RS studies in Svalbard to train the next generation of polar scientists, (2) infrastructure development (like the current infrastructure investment programme SIOS-InfraNor) that can attract Cal/Val activities to Svalbard (3) SIOS&#8217;s airborne remote sensing activities, and (4) SIOS remote sensing service tools for field scientists. Ongoing and future activities include (1) the development of unified platform for satellite data availability for Svalbard, (2) establishing an EO and RS researcher&#8217;s forum on SIOS website, (3) community-based observations e.g. developing a citizen science project model for supporting satellite cal/val activities in Svalbard, (4) ongoing surveys on user requirements, product inventory and citizen science project, and (5) the &#8216;Satellite image of the week campaign&#8217; on social media for outreach. The sustained and coordinated efforts by SIOS to develop a long-term monitoring system are expected to contribute to integrated monitoring, modelling and supporting decision making in Svalbard in the coming decades.</p>
Climate change poses a significant global challenge, with its effects manifesting prominently through melting and retreating glaciers in the Arctic and Antarctic. Understanding the dynamics of glacier flow is imperative for predicting the future evolution of the Polar ice sheets. Crevasses play an important role in regulating ice flow by acting as a conduit for surface meltwater to reach the bed and speed up ice flow, as well as providing the line of weakness through which icebergs detach from tidewater glacier termini. Furthermore, this study delves into the potential of computer vision techniques that use deep learning, leveraging foundation models trained using self-supervised learning like the Segment Anything Model (SAM) and DINOv2 from Meta AI, to automate crevasse mapping on glacial surfaces. Manual mapping crevasses on any glacier is currently labour-intensive and time-consuming without automation. Therefore, automating the process will allow scientists to map crevasses automatically over time in the exact location and over larger areas. Notably, this research addresses the scarcity of image segmentation datasets specifically tailored for mapping crevasses in polar regions and explores alternative deep learning methodologies, such as domain adaptation and few-shot learning, to overcome data limitations. The evaluation of foundation models harnesses high-resolution satellite imagery sourced from open-source remote sensing satellites such as Sentinel-1 and Sentinel-2 provided by the European Space Agency (ESA). Using multiple high-resolution image data modalities (e.g. Synthetic Aperture Radar (SAR) and optical satellite images) will provide insights into how different image data types help deep learning models generalise to crevasse mapping segmentation applications. The study seeks to develop advanced technological solutions to automate the mapping of crevasses tens of metres in width in order to address the knowledge gap of the role that crevasses play in modulating ice flow, particularly in response to climate warming.
Accurate, high-resolution 3D mapping of environmental terrain is critical in a range of disciplines. In this study, we develop a new technique, called the PCFilt-94 algorithm, to extract 3D point clouds from coarse resolution millimetre-wave radar data cubes and quantify their associated uncertainties. A technique to non-coherently average neighbouring waveforms surrounding each AVTIS2 range profile was developed in order to reduce speckle and was found to reduce point cloud uncertainty by 13% at long range and 20% at short range. Further, a Voronoi-based point cloud outlier removal algorithm was implemented which iteratively removes outliers in a point cloud until the process converges to the removal of 0 points. Taken together, the new processing methodology produces a stable point cloud, which means that: 1) it is repeatable even when using different point cloud extraction and filtering parameter values during pre-processing, and 2) is less sensitive to over-filtering through the point cloud processing workflow. Using an optimal number of Ground Control Points (GCPs) for georeferencing, which was determined to be 3 at close range (<1.5 km) and 5 at long range (>3 km), point cloud uncertainty was estimated to be approximately 1.5 m at 1.5 km to 3 m at 3 km and followed a Lorentzian distribution. These uncertainties are smaller than those reported for other close-range radar systems used for terrain mapping. The results of this study should be used as a benchmark for future application of millimetre-wave radar systems for 3D terrain mapping.
Knowledge of seagrass distribution is limited to a few well-studied sites and poor where resources are scant (e.g. Africa), hence global estimates of seagrass carbon storage are inaccurate. Here, we analysed freely available Sentinel-2 and Landsat imagery to quantify contemporary coverage and change in seagrass between 1986 and 2016 on Kenya's coast. Using field surveys and independent estimates of historical seagrass, we estimate total cover of Kenya's seagrass to be 317.1 ± 27.2 km 2 , following losses of 0.85% yr −1 since 1986. Losses increased from 0.29% yr −1 in 2000 to 1.59% yr −1 in 2016, releasing up to 2.17 Tg carbon since 1986. Anecdotal evidence suggests fishing pressure is an important cause of loss and is likely to intensify in the near future. If these results are representative for Africa, global estimates of seagrass extent and loss need reconsidering.
Abstract. During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S/C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. We observed snow strata in ice facies, wet-snow/percolation facies and dry snow facies from radar data. This paper reports seasonal snow depths derived from our observations. We found large variations in seasonal radar-inferred depths assuming a constant relative permittivity for snow equal to 1.89. The majority of the seasonal depths observed in 2018 were between 3.2 m and 4.2 m, and around 3 m in 2021. We also identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ~2.89 m w. e. a-1 between the years 2002 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w. e. a-1, which matches closely to the 2.75 m w. e. a-1 inferred from independent ground-truth measurements made the same year. We also found a linear increasing trend of 0.011 m w. e. a-1 per year between the years 2002 and 2021. With this trend, we extrapolated the snow accumulation back to 1992 and obtained an average accumulation rate of 2.74 w. e. a-1 between the years 1992 and 2004, which agrees well with the value of 2.66 w. e. a-1 for the same period determined from the ice core data retrieved at the caldera in 2004. The results reported here verified the efficacy of our method, its assumption, and the interpretation models.
<p>Calving of tidewater glaciers is a key driver of glacier mass loss as well as a significant contribution towards sea level rise. However, this dynamic process is still challenging to quantify. In addition, there are very few direct measurements of calving activity in Svalbard at daily to sub-daily resolution due to the requirement of continuous human labour at the calving front for field studies. Seismic instruments in the vicinity of glaciers offer the potential to circumvent this issue since they record ground motion signals, including those generated by calving events, with an unprecedented sub-second resolution. Such data sets are not affected by site conditions like poor visibility or darkness and, in the case of permanent regional seismological stations, already offer long-term datasets. Despite this, a knowledge gap remains which prevents making a direct link between precise calving volumes and seismic records. This study presents our effort made towards obtaining an estimate of volumetric ice loss from integrating seismic records with 3D millimetre-wave radar measurements of a tidewater glacier calving front. In the summer of 2021, an 8-day long time series of integrated measurements was acquired at the calving front of Hansbreen, South Spitsbergen. It included remote sensing observations from a millimetre-wave radar (AVTIS2), Terrestrial Laser Scanner and time-lapse cameras correlated with a seismic dataset from two local arrays deployed at direct vicinity of calving front and a closeby regional permanent seismological station in Hornsund. Integrating these datasets brings an opportunity to correlate visual observations of calving including volumetric ice loss derived from radar scans with seismic signatures registered at nearby seismic arrays. We explore various parameters that characterize observed calving events and develop a model linking chosen parameters with ice loss using machine learning techniques. Local arrays were installed for a limited time and the calibrated parameters are expected to change spatially. Therefore, we further transfer our approach and integrate decade long records from nearby permanent seismological station. Limiting data to a single station record reduces both the accuracy of estimated ice volume and spatial resolution. However, it enables us to apply detection algorithm trained using observed calvings to decade long records and, consequently, to revisit a decade long history of Hansbreen's calving.</p>
Mass loss from glaciers and ice caps represents the largest terrestrial component of current sea level rise. However, our understanding of how the processes governing mass loss will respond to climate warming remains incomplete. This study explores the relationship between surface elevation changes (dh/dt), glacier velocity changes (du/dt), and bedrock topography at the Trinity-Wykeham Glacier system (TWG), Canadian High Arctic, using a range of satellite and airborne datasets. We use measurements of dh/dt from ICESat (2003–2009) and CryoSat-2 (2010–2016) repeat observations to show that rates of surface lowering increased from 4 m yr−1 to 6 m yr−1 across the lowermost 10 km of the TWG. We show that surface flow rates at both Trinity Glacier and Wykeham Glacier doubled over 16 years, during which time the ice front retreated 4.45 km. The combination of thinning, acceleration and retreat of the TWG suggests that a dynamic thinning mechanism is responsible for the observed changes, and we suggest that both glaciers have transitioned from fully grounded to partially floating. Furthermore, by comparing the separate glacier troughs we suggest that the dynamic changes are modulated by both lateral friction from the valley sides and the complex geometry of the bed. Further, the presence of bedrock ridges induces crevassing on the surface and provides a direct link for surface meltwater to reach the bed. We observe supraglacial lakes that drain at the end of summer and are concurrent with a reduction in glacier velocity, suggesting hydrological connections between the surface and the bed significantly impact ice flow. The bedrock topography thus has a primary influence on the nature of the changes in ice dynamics observed over the last decade.
There is growing evidence that ice mélange, the granular mixture of sea ice and icebergs at the termini of tidewater glaciers, impacts ice sheet discharge through physical buttressing forces and alterations to fjord circulation via iceberg melting. However, ice mélange is a highly dynamic, fragmented and mobile phenomenon which varies over a range of timescales (e.g. hours, days, weeks) and hence is difficult to monitor using traditional ground-based and spaceborne sensors. In this contribution, we utilise high spatio-temporal satellite imagery acquired from the ICEYE small satellite constellation to assess correlations between ice mélange characteristics and tidewater glacier dynamics. ICEYE is a growing constellation of 20+ small satellites each equipped with an X-band Synthetic Aperture Radar (SAR) and capable of mapping the entire globe at least once a day with fine spatial resolution (1-3 m). We utilised the ICEYE SAR imagery to study the perennial mélange matrix at the terminus of Helheim Glacier in southeast Greenland. ICEYE SAR imagery was acquired during summer and winter to assess how seasonal ice mélange conditions impact tidewater glacier dynamics. Sentinel-1 SAR imagery and ground-based TLS 3D data from two autonomous terrestrial laser scanners (ATLAS) were used to validate remote sensing analysis and provide additional data sources for interpretation of the glaciological processes. We will report on the following: (1) a spatial texture analysis (e.g. Grey Level Co-occurrence Matrix (GLCM), Gabor Transforms) of ice mélange at the terminus of Helheim Glacier using high resolution ICEYE SAR imagery; (2) results of hierarchical and random forest classifiers to map icebergs, sea ice and open water within the ice mélange matrix; (3) quantification of glacier and mélange flow variability at daily to weekly timescales; and (4) the development of observational models correlating ice mélange texture, iceberg distributions, mélange/glacier flow rates, and tidewater glacier stability. Our case study at Helheim Glacier aims to demonstrate a new approach to rapidly monitor ice mélange conditions and tidewater glacier stability using high resolution SAR imagery. In particular, this study pushes forward our Earth Observation capabilities and will help us better understand the complex processes operating at the ice-ocean interface which is critical for improved predictions of how the Greenland Ice Sheet will evolve under a warming climate.
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