Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

Abstract

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of: i) the snow melt onset; ii) the snow gliding and wet-snow avalanches; iii) the release of snow contaminants and iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to be obtained. On the other hand, active microwave sensors such as the Synthetic Aperture Radar (SAR) mounted on board of satellites, are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 days over several places in the world. In this paper we analyze the correlation between the multi-temporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering two hydrological years. We found that the multi-temporal SAR measurements allow the identification of the three melting phases that characterize the melting process i.e., moistening, ripening and runoff. In detail, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water, and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease, but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially-distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

“…The results are consistent with Landsat imagery of the region that is mostly snow-free by the end of May. However, the BSC changes during the snow melt [71] can potentially lead to the temporary underestimation of wet snow areas. Using the summer BSC image as a reference for wet snow mapping can lead to overestimation in areas of densely vegetated deciduous forest and underestimation for dry snow in barren areas with high BSC, both relatively small in Grand Mesa.…”

“…Wet snow attenuation effects consistent with the minimum BSC magnitude independent of slope and aspect (Figure 17a,b) explain the distinct spectral slopes at small scales on May 18, 2018 in the y-direction (Figure 16, bottom row). Nonlinear behavior of heterogeneous snowpacks in the melt season reflects the changing patterns of SCA for scales <360 m (see also [8,71]). (Table S3); the green vertical line marks the scaling break.…”

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

“…As expected the algorithm detects well wet snow above the tree line in the Swiss Alps (Figure 19b Landsat imagery of the region that is mostly snow-free by the end of May. However, the BSC changes during the snow melt [63] can potentially lead to the temporary underestimation of wet snow areas.…”

“…snow and due to wind sintering explain therefore the BSC increase from early spring (smooth wet snow) to late spring and early summer conditions (refrozen crusted wet snow) [63]. In early winter, from December to March, dielectric discontinuities in the snowpack tied to heterogeneous stratigraphy strongly impact the backscattering signal.…”

“…In early winter, from December to March, dielectric discontinuities in the snowpack tied to heterogeneous stratigraphy strongly impact the backscattering signal. In particular, if the event-scale snow accumulation is small, heterogeneous layering in the snowpack leads to strong backscattering [63].…”