Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanche reports from a network of observers. During bad weather, however, information about avalanches that have occurred can be scarce or even be missing completely. To assess the potential of weather-independent radar satellites, we compared manual and automatic change detection avalanche mapping results from high-resolution TerraSAR-X (TSX) stripmap images and medium-resolution Sentinel-1 (S1) interferometric wide-swath images for a study site in central Switzerland. The TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other. Cutoff thresholds of mapped avalanche areas were found with 500 m² for TSX and 2000 m² for S1. S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 d with freely available acquisitions. With costly SPOT-6 images the Alps can even be covered in a single day at meter resolution, at least for clear-sky conditions. For the SPOT-6 and TSX mapping results, we found a fair agreement, but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, the total mapped avalanche area differed by at least a factor of 3 because with radar mainly the avalanche deposition zone was detected, whereas the release zone was very visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of manually mapped new avalanches, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single-orbit TSX change detection results. In a multiorbital S1 mosaic covering all of Switzerland, we manually counted 7361 new avalanches which occurred during an extreme avalanche period around 4 January 2018.

Differential interferometry using ground-based radar systems permits to monitor displacements in natural terrain with high flexibility in location, time of acquisition, and revisit time. In combination with polarimetric imaging, discrimination of different scattering mechanisms present in a resolution cell can be obtained simultaneously with the estimation of surface displacement. In this paper, we present the preprocessing steps and the calibration procedure required to produce high-quality calibrated polarimetric single-look complex imagery with KAPRI, a new portable Ku-band polarimetric radar interferometer. The processing of KAPRI data into single look complex images is addressed, including the correction of beam squint and of azimuthal phase variations. A polarimetric calibration model adapted to the acquisition mode is presented and used to produce calibrated polarimetric covariance matrix data. The methods are validated by means of a scene containing five trihedral corner reflectors. Data preprocessing is assessed by analyzing the oversampled response of a corner reflector, and the polarimetric calibration quality is verified by computing polarimetric signatures and residual calibration parameters.

Terrestrial radar interferometers (TRI) are complimentary to spaceborne synthetic aperture radar systems for deformation monitoring in natural terrain: they permit shorter revisit times and greater flexibility in acquisition mode and timing. The additional diversity offered by polarimetric data can also be beneficial for TRI observations because polarized waves are sensitive to the dielectric and geometrical properties of the scatterers. Polarimetric data helps to distinguish different scattering mechanisms in a resolution cell while at the same time estimating terrain displacements. However, the polarimetric scattering signatures of natural surfaces at Ku-Band are not as well characterized as the ones at longer wavelengths, owing to relative rarity of full polarimetric systems operating in Ku-band. This band is often employed in TRI to obtain a fine azimuth resolution with a limited aperture size. This article aims at assessing the potential of polarimetric measurements in Ku-band TRI through an experimental study of polarimetric scattering signatures of natural surfaces using two datasets acquired over a glacier and in an agricultural and urban scene. The main finding of this analysis is that the Cloude-Pottier entropy is high for all land cover types; it is only observed to be less than 0.5 for scatterers with a large radar cross section. Several plausible hypotheses for this observation are formulated and tested, the most likely assumes a combination of depolarizing scattering from natural surfaces and the effect of the large ratio of wavelength to resolution cell size.

The management of scientific data plays a key role in all research areas and has increased in importance. Providing researchers with customizable data management tools is crucial for effectively managing data according to the FAIR principles. These principles have been defined by Wilkinson et al. in 2016, which describe how scientific data should be managed.[1] To support the specific needs of researchers at Empa, openBIS[2] was chosen as a FAIR compliant data management platform. OpenBIS is an Electronic Laboratory Notebook (ELN) and Laboratory Information Management System (LIMS) developed at ETH. The commissioning of this platform for the case of an analytical chemistry lab presented multiple challenges. In this paper, solutions to adapt openBIS as a digital platform to integrate the laboratory data workflow in chemical analysis and for spectroscopy instruments are presented. Two laboratory projects as case studies are described, consisting of a data pipeline and a complex dashboard for data collection, visualization and interaction. These examples show a successful integration of the data management platform in accordance with the FAIR data guidelines along with maximizing efficiency for laboratory personnel.

Accuracy of radar interferometry is often hindered by the atmospheric phase screen (APS). To address this limitation, the geostatistical approach known as Kriging has been employed to predict APS from sparse observations for compensation purposes. In this article, we propose an enhanced Kriging approach to achieve more accurate APS predictions in ground-based radar interferometry applications. Specifically, the Kriging system is augmented with a time-series measure through correlation analysis, effectively leveraging spatio-temporal information for APS prediction. The validity of the introduced Kriging method in the APS compensation framework was tested with Ku-band ground-based radar datasets collected over two different mountainous sites. A comparison of this method with simple Kriging (SK) reveals a noticeable improvement in APS prediction accuracy and temporal phase stability.