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 snowmelt 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 obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board 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 d over several places in the world. In this paper we analyze the correlation between the multitemporal 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 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, 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 us 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.

Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt

“…Another reason for the long lag length of SAR sensor-based WSCE% is because of the unique way that SAR detects snowmelt processing and shows the temporal offset to the real condition. Based on the in-situ measurements and modeling simulation [11], it is known that the whole snow melting process can be divided into three phases, i.e., moistening, ripening, and runoff. In each phase, the contents of the snow water equivalent (SWE) (the total mass of water, including both liquid and solid water, stored in the form of snow) and the liquid water content (LWC) (the mass of liquid water inside the snowpack) differ, and the backscatter coefficient of SAR also changes accordingly and results in a U-shaped curve (Figure 17).…”

“…In each phase, the contents of the snow water equivalent (SWE) (the total mass of water, including both liquid and solid water, stored in the form of snow) and the liquid water content (LWC) (the mass of liquid water inside the snowpack) differ, and the backscatter coefficient of SAR also changes accordingly and results in a U-shaped curve (Figure 17). In detail, firstly, in the moistening stage, the diurnal melting-freezing cycles gradually increase the LWC and lead to the gentle decreasing of the backscatter coefficient; in the ripening stage, the LWC significantly increases (while the SWE still remains the same), and the backscatter coefficient rapidly decreases and reaches the minimum value at the end of the ripening stage when the snowpack is saturated; and, finally, during the runoff stage, as the snowmelt water releases, both the LWC and SWE decrease, and the backscatter coefficient increases accordingly [11].…”

“…It must be noted that, as the backscatter coefficient of SAR would already slightly drop in the middle of the first moistening stage, the snow would already be detected and regarded as wet snow by the backscattering ratio threshold method [9][10][11]. However, the snowmelt water is actually released only until the final runoff stage starts.…”

“…For evapotranspiration, in both the river and lake cases, the coefficients are negative, which Figure 17. The relationship of SAR-based wet snow detection and the snowmelt processing (modified from [11]). The amount of liquid water content (LWC), snow water-equivalent (SWE), and the released snowmelt runoff together with the SAR signal backscatter coefficient in different snowmelt phases are illustrated.…”

“…For monitoring the snowmelt conditions, as summarized in Tsai et al [9], it is hardly possible to discriminate wet and dry snow with conventional multi-spectral data sources solely based on the reflectance differences, as the spectral characteristics of both snow types share high similarity. SAR data, on the other hand, are excellent for wet SCE (WSCE) mapping: The snowmelt-caused wetness in the snowpack would change the dielectric constant of the snowpack and, thus, dramatically shorten the penetration depth of the SAR signal, which eventually lead to a significant decrease of the backscatter coefficient when the snowpack starts to melt [9][10][11].…”