We present two novel experimental setups for automated measurement of coherent backscatter enhancement (also known as the coherent backscatter opposition effect – CBOE) of Ku-band radio waves in seasonal snow cover. The experimental setups were developed in order to enable fast and repeatable measurements of the effect’s properties, through quickly altering the bistatic baseline of the radar system between zero and eight meters. The experimental setups were tested in December 2023 at the High Altitude Research Station Jungfraujoch in Switzerland, yielding a novel dataset exploring the properties of the early-winter snow cover on top of the Great Aletsch Glacier, as well as allowing a comparison of the benefits and drawbacks of the two experimental setups. We present preliminary results of these experiments, as well as discuss the potential of such measurements for improving our understanding of radio wave scattering in snow.

We present the results of recent observations of terrestrial snow cover with KAPRI, a Ku-band polarimetric-interferometric radar system with bistatic capabilities. The bistatic configuration allows observation of phenomena which are not observable in the monostatic regime. Observations of polarization phase differences of snow cover on top of the Great Aletsch Glacier revealed that the behaviour of these parameters markedly varies between different times of year, as well as between the monostatic and bistatic acquisition modes. The bistatic capabilities of KAPRI were also recently used to characterize the coherent backscatter opposition effect (CBOE) in seasonal snow. We discuss the potential of such bistatic observations to observe quantities which are unobservable in the monostatic regime, and which could serve as new inputs for snow parameter retrieval methods.

Today, more and more velocity observations are available online or on-demand. However, this amount of data is complex to analyze since velocity observations span different temporal baselines. Velocities obtained from a small temporal baseline are close to the derivative of the displacement but are more likely to be contaminated by noise. Velocities obtained from a long temporal baseline approximate the mean velocity between two dates but can be affected by temporal decorrelation. Having short and long temporal baselines provides a data redundancy that needs to be properly considered. In this article, we propose a method that aims to extract short-term velocity time series with regular temporal sampling from all available displacement observations. The proposed method relies on a temporal inversion based on an improved temporal closure of the displacement observation network. Two criteria are proposed to determine the optimal temporal sampling to study short-term variations. To take the unequal data uncertainty into account, the temporal inversion is done by an iterative reweighted least square using a well-established weighting function, without preprocessing. The proposed method results in velocity time series with an optimal temporal sampling, improved temporal coverage, reduced uncertainty, and no redundancy. The studied area is the Kyagar glacier, in the North of the Karakoram range that is characterized by strong velocity variations originated from a glacier surge and additional seasonal variability.

SAR offset tracking, applied on areas with strong temporal decorrelation, requires relatively large image templates for cross-correlation to compensate for incoherent radar speckle. Template edge lengths of 64–512 pixels are common. Furthermore, velocity maps are often incomplete because weakly visible features are obscured by uncorrelated speckle. To improve SAR offset tracking, we propose a new robust method which can significantly enhance both the spatial completeness and the resolution of velocity products by assuming a stationary velocity field. The method minimizes the motion blur of moving features which occurs when SAR backscatter time series are multilooked in time. Our velocity-adaptive temporal multilooking strongly reduces speckle without losing spatial resolution which makes the cross-correlation much more robust even for template sizes as small as 30 x 30 pixels. We demonstrate the method by generating a high resolution velocity map of Great Aletsch Glacier in Switzerland.

South Rimo Glacier, one of the largest glaciers in Karakoram, showed a glacier surge event during 2018–2020. To characterize the dynamics of the surge, we assessed surface height changes by differencing a time-series of DEMs produced from high-resolution TanDEM-X radar data. In addition, we evaluated the evolution of surface velocities with offset tracking using Sentinel-1 imagery. DEM differences show that the glacier started gaining height in the middle stream since 2013 where a bulge built-up and reached a maximum observed height of 28 m in 2017. After surge activation in 2017, the velocity increased by 2–3 order of magnitude with a peak velocity of 11 m/d in August 2019. Our work highlighted the value of high resolution DEM products and velocity maps in pre-identifying glacier surge and mitigating related hazards.

Snow avalanches cause a sudden change of surface roughness which makes them detectable with synthetic aperture radar (SAR). However, in mountainous terrain the slant imaging geometry of SAR sensors complicates detection because the spatial resolution and the backscatter characteristics vary with the local incidence angle relative to the terrain. To improve detection we 1) characterize avalanche backscatter characteristics using a manually created dataset of 914 drawn avalanche outlines and 2) apply local resolution weighting (LRW) to merge Sentinel-1 (S1) acquisitions from ascending and descending orbits. We show that avalanches appear brightest at incidence angles of 55 ± 20°; such angles are weighted considerably stronger by LRW. For avalanche segmentation with LRW we obtain a higher F1 score (0.75) compared to an unweighted orbit average (F1 = 0.68) or single orbits (F1 = 0.5) when applying a fixed threshold on the backscatter difference.

In February 2018, we identified an unnamed glacier in the West Kunlun Shan mountains which showed crevasses extremely similar to the detachment patterns of two glaciers on the Western Tibetan Plateau near lake Aru Co before their catastrophic collapse. Here we present an analysis of this unnamed glacier addressing its similarity to the Aru glaciers, its potential to cause a massive ice avalanche, and the potential run-out distance. Similar to the Aru glaciers, we show that the glacier has a polythermal regime and that soft-bed sediments favor a collapse. Digital elevation data from 2000 to 2014 and velocity estimates reveal glacier dynamics very similar to the Aru glaciers. However, ERA-Interim climate data indicate only a limited liquid water input, which might be insufficient for a complete detachment from the glacier bed. Additionally, a broad glacier tongue might stabilize the glacier.

Single pass radar interferometry (sp-InSAR) is a well established technique for generation of digital elevation models (DEM). Differencing two DEMs acquired at different times can reveal topographic changes. However snow depth estimation by DEM differencing is still an ongoing topic in radar research: in contrast to snow free surfaces, the snow surface elevation is difficult to detect either because of microwave penetration into dry snow or because of the weak backscatter return from wet snow which significantly decorrelates the interferometric signal. In this study we demonstrate first results of wet snow depth estimation by differencing sp-InSAR DEMs acquired by the TanDEM-X satellite mission. The results show, in contrast to dry snow, a clear sensitivity to wet snow. However, additionally to a high vertical sensitivity of a few ten centimeters a very low noise-equivalent-sigma-zero (NESZ) is crucial for successful snow depth estimation.

Grosser Aletschgletscher, the largest glacier in the European Alps, contains 20% of the entire Swiss ice mass. However, mass balance simulations based on current climate models predict 90% mass loss by 2100. The glacier is a super-testsite for the TanDEM-X satellite mission and data series starting from 2011 are available. In this study we analyzed almost 100 digital elevation models computed from bistatic radar in-terferograms and compare the derived height loss with model predictions. Additionally, we used patch-based offset tracking to determine a new velocity map of the complete glacier. The results show that the current height loss of 3.3 meters per year is close to that mass balance simulation which uses the ensemble median of common European climate models which predict, relative to 1999, a warming of 4.3°C by 2100.

Temporally resolved, 3-dimensional surface elevation data provide valuable insight into glacier dynamics, e.g. glaciers surges: short periods of rapid acceleration and mass transport which can pose significant hazard potential. Particularly in remote locations, such hazards can develop unobserved by the downstream communities at risk. This paper demonstrates the relevance of single-pass InSAR missions with two examples: Glacier lake outburst floods (GLOFs) of Kyagar Glacier in the Chinese Karakorum and the AruCo twin glacier collapse in Tibet. Periodic surging of Kyagar Glacier causes GLOFs affecting communities over 500km downstream. Near AruCo, the mysterious but catastrophic collapses of two neighboring glaciers were also preceded by surge-like processes. In both cases typical signs of glacier surging (unusual advance) were absent in satellite images, but TanDEM-X InSAR DEMs revealed characteristic, surface elevation changes. Here, we summarize which information can be gained from surface elevation data to assess surge-related hazard potentials and to understand the underlying processes.

Dry snow can be considered as a transparent but refractive medium which causes a phase delay in the reflected signal of active radar remote sensing systems. Here, we analyze the phase delay to estimate Snow Water Equivalent (SWE), the depth of fresh snow and the anisotropic orientation of ice grains in the snow volume. SWE is determined from the integrated phase shift measured by differential interferometry. The temporal evolution of the snow anisotropy could be observed because different microwave polarizations show different propagation speeds in anisotropic snow. The depth of fresh snow as well as snow metamorphosis is discussed with respect to characteristic phase-shifts in the co-polar phase difference. Ground based radar observations from the Snow-scat instrument installed at a test site near Sodankylä, Finland, form the data basis for this paper.

The combination of interferometry and polarimetry (PolInSAR) is a promising candidate for estimating the snow volume using the Random Volume over Ground model (RVoG). The model was originally developed for forest height determination using L-band [1]. The adaption of the RVoG model to snow is not straight forward due to the thin volume of snow and the highly varying penetration depth of X-band microwaves. In this paper we show parameter ranges and recommendation where the RVoG model might work, together with results of interferometry (InSAR) and polarimetric SAR (Pol-InSAR) using TerraSAR-X and TanDEM-X SAR acquisitions.