Abstract
Abstract. Snow cover (SC) and timing of snowmelt are key regulators of a wide range of Arctic ecosystem functions. Both are strongly influenced by the amplified Arctic warming and essential variables to understand environmental changes and their dynamics. This study evaluates the potential of Sentinel-1 (S-1) synthetic aperture radar (SAR) time series for monitoring SC depletion and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over northeastern Greenland and 94 over southwestern Greenland in the interferometric wide swath mode from the years 2017 and 2018. Comparison of S-1 intensity against SC fraction maps derived from orthorectified terrestrial time-lapse imagery indicates that SAR backscatter can increase before a decrease in SC fraction is observed. Hence, the increase in backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel empirical approach based on the temporal evolution of the SAR signal to identify start of runoff (SOR), end of snow cover (EOS) and SC extent for each S-1 observation date during melt using backscatter thresholds as well as the derivative. Comparison of SC with orthorectified time-lapse imagery indicates that HV polarization outperforms HH when using a global threshold. The derivative avoids manual selection of thresholds and adapts to different environmental settings and seasonal conditions. With a global configuration (threshold: 4 dB; polarization: HV) as well as with the derivative, the overall accuracy of SC maps was in all cases above 75 % and in more than half of cases above 90 %. Based on the physical principle of SAR backscatter during snowmelt, our approach is expected to work well in other low-vegetation areas and, hence, could support large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 d) with high accuracy.
Highlights
1.1 Snow in Arctic environmentsSnow cover (SC) has been identified as an essential climate variable (GCOS-WMO, 2020) covering about 40 % to 50 % of the Northern Hemisphere during winter (Dietz et al, 2012; Rees, 2005; Tsai et al, 2019b)
We present a fast and simple approach for mapping snow cover (SC) and timing of snowmelt based on Sentinel-1 (S-1) synthetic aperture radar (SAR) time series
Using the distinct seasonal signal of backscatter intensity above snow, the approach employs user-defined thresholds based on the seasonal backscatter minimum as well as the derivative of the time series to (i) identify start of runoff (SOR) and end of snow cover (EOS) as day of year (DOY), (ii) detect start-of-season snow-free areas and end-of-season snow-covered patches, and (iii) derive a SC extent map for each S-1 observation date during SC depletion
Summary
Snow cover (SC) has been identified as an essential climate variable (GCOS-WMO, 2020) covering about 40 % to 50 % of the Northern Hemisphere during winter (Dietz et al, 2012; Rees, 2005; Tsai et al, 2019b). The timing of snowmelt is highly variable in space and time and influenced by snow accumulation, redistribution and ablation The former two depend on the climatic conditions, e.g., latitudinal and altitudinal position and continentality, as well as on the local topography that affects transport of snow due to wind and gravitational redistribution. Hock et al (2019) state that knowledge about SC distribution is still limited, especially at small spatiotemporal scales
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