Context and objectives
Snow water equivalent (SWE), or the amount of water stored in snow, ranks among the most uncertain variables in hydrology. Estimates from the interpolation of local measurements are unrealistic where they are sparse, whereas weather models poorly estimate snowfall. Similarly, current remote sensing observations have inherent limitations, especially in mountain areas, where most of the terrestrial snow is located. Airborne LiDAR systems seem to be the most accurate to date, but are costly and limited by areal coverage and weather conditions. Passive microwave observations typically exclude mountains because their coarse footprints (~25 km) cannot resolve the spatial variability and the observations saturate in shallow snow (< 0.8 m). Some alternative methods have shown promise, albeit at the local scale, for instance: structure-from-motion, X-/Ku-band Synthetic Aperture Radar (SAR), and L-band SAR interferometry. A community effort to compare some of the above-mentioned remote sensing techniques is currently ongoing within the NASA SnowEx activity that should ultimately lead to a space mission designed for snow. New and robust satellite observations at the global scale are critically needed to fill the mountain-snow observation gap.
C-SNOW will demonstrate the ability of the ESA and Copernicus Sentinel-1 mission to map ~weekly snow mass at 1 km² resolution over the Northern Hemisphere mountains. A baseline retrieval algorithm has recently been developed and evaluated by the team at the regional scale. This will be extended over the Northern Hemisphere, and improved via dedicated field experiments with tower-mounted radar measurements (cfr. the Sentinel-1 configuration) over mountain snow-sites. The end goal is to make optimized Sentinel-1 snow mass observations over the Northern Hemisphere mountains available to the community. The potential societal benefits of high-resolution SWE observations in mountainous areas are manifold. Worldwide, more than 1.2 billion people rely on seasonal snow for most of their drinking water. Snow meltwater provides water supply not only to households, but also to agriculture, industry, and hydropower generation. Besides its use as a water resource, snowmelt is important for disaster management, with snow-related hazards such as floods and avalanches. Snow also impacts our climate, by the high albedo (reflecting most of the incoming solar radiation back to the atmosphere), by its insulating properties (reducing the heat exchange between the ground and the atmosphere), and by dissipating energy during melt. There is recent evidence that global warming impacts the presence and amount of snow, which on its turn can reinforce climate change. Consequently, the monitoring of SWE has a high priority in the recent Decadal Survey from the United States National Academies and in ESA’s Earth Observation Science strategy, and relates to several scientific challenges outlined in the ESA Living Planet programme.
To address a critical science goal (i.e., estimating the amount of water stored in terrestrial snow on Earth), C-SNOW aims at mapping snow depth (SD) and SWE from Sentinel-1 at high spatial (1 km²) and temporal (<weekly) resolutions in mountain areas across the Northern Hemisphere. Two primary science objectives (SO1 and SO2) are addressed in the proposal:
- SO1: Map snow depth and SWE and their accuracy in Northern Hemisphere mountain areas
- SO2: Unravel the physical mechanisms impacting C-band radar sensitivity to snow
Two secondary science objectives (SO2a and SO2b) further support currently ongoing activities (e.g. SnowEx) that investigate the optimal method/configuration for SWE retrieval (of particular interest to space agencies):
- SO2a: Compare the radar backscatter sensitivity to snow at Ku-band and C-band
- SO2b: Compare the retrieval of snow from radar backscatter and phase (interferometry) information