Preprints
https://doi.org/10.5194/tc-2021-78
https://doi.org/10.5194/tc-2021-78

  13 Apr 2021

13 Apr 2021

Review status: this preprint is currently under review for the journal TC.

Sentinel-1 time series for mapping snow cover and timing of snowmelt in Arctic periglacial environments: Case study from the Zackenberg Valley, Greenland

Sebastian Buchelt1, Kirstine Skov2, and Tobias Ullmann1 Sebastian Buchelt et al.
  • 1Department Physical Geography, Institute of Geography and Geology, University of Wuerzburg, 97072 Wuerzburg, Germany
  • 2Department of Bioscience, Arctic Research Center, Aarhus University, 4000 Roskilde, Denmark

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 and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over north-eastern 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 an increase of the SAR intensity before a decrease of SC fraction is observed. Hence, increase of backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel approach using backscatter intensity thresholds to identify start and end of snowmelt (SOS and EOS), perennial snow and wet/dry SC based on the temporal evolution of the SAR signal. Comparison of SC with orthorectified time lapse imagery indicate that HV polarization outperforms HH when using a global threshold. With a global configuration (Threshold: 4 dB; polarization: HV), the overall accuracy of SC maps was in all cases above 75 % and in more than half cases above 90 % enabling a large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 days) with high accuracy.

Sebastian Buchelt et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-78', Anonymous Referee #1, 04 May 2021
    • AC1: 'Reply on RC1', Sebastian Buchelt, 12 Aug 2021
  • RC2: 'Comment on tc-2021-78', Anonymous Referee #2, 09 May 2021
    • AC2: 'Reply on RC2', Sebastian Buchelt, 12 Aug 2021
  • RC3: 'Comment on tc-2021-78', Anonymous Referee #3, 17 May 2021
    • AC3: 'Reply on RC3', Sebastian Buchelt, 12 Aug 2021
  • RC4: 'Comment on tc-2021-78', Anonymous Referee #4, 18 May 2021
    • AC4: 'Reply on RC4', Sebastian Buchelt, 12 Aug 2021

Sebastian Buchelt et al.

Sebastian Buchelt et al.

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Short summary

In this paper, we present a threshold-based approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start and end of snowmelt as well as perennial snow and wet/dry SC with high spatio-temporal resolution. We believe that this approach could be applied to monitor distribution patterns and hydrological cascading effects of snow from the scale of a catchment up to pan-Arctic observations.