The seasonal snowpack holds a fundamental significance for hydrological, climatic, and safety considerations in mountainous regions. A deep and accurate understanding of its dynamics is needed to evaluate the quantity of available freshwater and to create avalanche risk reports. Despite the traditional manual analysis still being the standard method, technological instruments, particularly those employing microwave frequencies, have been used lately for snowpack monitoring purposes. However, these approaches exhibit some limitations and may lead to ambiguous results unless supplemented with additional sources of information or more sophisticated techniques. This article introduces a recent development in snowpack monitoring utilizing a dual-receiver microwave radar system. The instrument, previously validated for dry snow conditions, demonstrates high precision in retrieving both the depth and dielectric properties of a snowpack. A preliminary attempt to monitor wet snow has been made in the past, investigating the presence of water. In this work, a more systematic analysis of real data has been conducted by implementing a multiband configuration and exploiting different kinds of wetness conditions. The tests carried out on wet snow are compared to manual analysis outcomes. Furthermore, this new configuration has been used to efficiently monitor the melting-freeze cycle over both daily and seasonal periods. The results reported in this article highlight the instrument’s capability to provide accurate data in diverse snow conditions, thanks to its multiband feature, offering a promising way for enhanced snowpack research and monitoring practices.

Monitoring the internal structure of the snowpack is imperative for managing snow-related hazards like avalanches and snowmelt floods. The surge in availability of cost-effective, low-power, and low-profile 24 GHz frequency-modulated continuous-wave (FMCW) radars, originally designed for the automotive sector, has opened new possibilities. This paper illustrates the application of a compact and economical FMCW radar to enhance snowpack studies by swiftly providing the dielectric properties of snow and potentially assessing density and liquid water content (LWC). The radar functions as a snowpit instrument, creating expedited snow profiles of dielectric properties, aiming to overcome the drawbacks of slower, operator-dependent traditional density cutters. Initial results showcase the real part of the relative dielectric permittivity in actual snow conditions. Results are compared with manual measurements directly taken in the snowpit and with the bulk measurements taken with a well-established multi-band radar.

This research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

Glaciers are nowadays becoming a more and more important topic to investigate, due to their close relationship with the climate change and impact on people living in mountainous areas. For this reason, the researches on new, faster, non-destructive and valuable techniques to monitor such natural bodies became necessary. In this framework, an already-existed radar dual-receiver architecture, used in recent years for snowpack monitoring, has been tailored to be used for the first time for glacier monitoring. After some improvements of the system, the radar architecture was tested in the Italian Alps, more precisely at the Cherillon glacier (Valle d'Aosta). The preliminary results show a good agreement with the traces collected by a Ground Penetrating Radar (GPR) in 2019 for what concerns glacier depth, when a speed for the wave in the ice is assumed. However, the dual-receiver architecture demonstrated that it was capable of estimating independently not only the glacier depth, but also the wave speed, opening in addition the analysis to further possibilities.

Snow is an important environmental variable and a primary water resource in many areas of the world. Monitoring seasonal snowpack properties is also crucial for properly managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, an innovative radar architecture, based on the use of two receivers, has been proposed for snowpack monitoring for the case of dry snow, where the snowpack depth and bulk density can be calculated with one single radar measurement, without any kind of external aid. This article presents the extension of this innovative radar architecture for the case of wet snow. The approach to determine, not only the snowpack depth and bulk density but also the liquid water content, is outlined and discussed in detail, along with the experimental validation of the operating principle for two cases.

The backscattered signal collected by space-born radars, such as Sentinel-1, for area covered by snow is affected by the condition of the snow itself, in particular during melting. However, the potential evidence of a relationship between the different melting phases and the amplitude of the backscattered signal still remains a difficult phenomenon to be described quantitatively. This paper proposes a preliminary approximated model, built upon i) a first-order simulation based on plane-wave incidence on stratified media that account for the bulky physical parameters of the snowpack, such as depth, liquid water content, density, and ii) a full-wave simulation to include the effect of the surface roughness. The model is tested against experimental data for a site in the Italian Alps (Malga Fadner), where data from Sentinel-1, as well as in-situ data about the composition of the snowpack, are available for the winter season 2017/18, showing good general agreement.