Recent Advances in Snow Monitoring from Local to Global Scales

Purpose of Review: Snow-related processes occur over a large range of spatial scales. Studying these processes therefore requires observation techniques with different spatial coverages, from short-range (up to several meters) to medium-range (several meters to several kilometers) and long-range (several kilometers to hundreds of kilometers). However, integration of observations with different coverages remains challenging because these have different spatial resolutions, making it difficult to ensure the representativeness across distinct techniques. Although snow observations have become more accurate in recent years, many challenges still prevent the retrieval of detailed and robust snow observations in mountainous and high-latitude regions. This review provides an overview of the most recent advances in techniques used to improve snow monitoring at different spatial coverages during the last 5 years. Recent Findings: Although the use of in-situ methods has improved observations of the Snow Water Equivalent (SWE), these observations are still limited by the presence of liquid water and the need for secondary variables. Promising developments include Global Navigation Satellite Systems (GNSS) -based antennas for measurement of SWE and microwave permittivity for measurement of snow density. Similarly, further studies are needed to examine the impact of the forest canopy on snow detection and the presence of Light-Absorbing Particles (LAP) on the snow surface and their effect on snowpack albedo. Airborne technologies, including unmanned aerial vehicles (UAVs) with Light Detection and Ranging (LiDAR) or stereo optical imagery, have provided better observations of snow depth over large areas and have higher reliability than earlier technologies. Space-borne sensors can also provide advanced monitoring of snow, including measurements of snow depth distribution using satellite photogrammetry and monitoring of SWE using radar technologies. Summary: Future efforts should focus on measurements of SWE and snow density from local to global scales, improve observations in forested areas, develop distributed data for snowpack variables, and integrate observations from techniques with different coverages. We also underscore that long-term monitoring from satellites must be maintained to achieve better forecasts of changes in snow, improve the management of snow resources, and provide a deeper understanding of the dynamics of climate change. Machine learning offers new opportunities for refining the techniques used for snow observations and integration of different techniques.