TerraSAR-X Time Series Fill a Gap in Spaceborne Snowmelt Monitoring of Small Arctic Catchments—A Case Study on Qikiqtaruk (Herschel Island), Canada

Abstract

Abstract:The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX-and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt.

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

“…Wet snow is typically identified using change detection applied to the backscatter ratio between an image with wet snow and a reference image with only dry snow or no snow. A constant threshold of -2 dB or -3 dB is often used to generate a wet snow mask [9,11,13,14]. Soft thresholds, as a function of the backscatter ratio, have been used to derive the probability of wet snow [15,16,17].…”

Potential of X-band polarimetric SAR co-polar phase difference for Arctic snow depth estimation

“…for each vegetation class as well as the confidence interval (95%). The period with presence of snow was set between mid-September and mid-May based on prior observations (Burn and Zhang, 2009;Stettner et al, 2018). Figure 5c shows the monthly average temperature and cumulative monthly precipitation on Qikiqtaruk-Herschel Island.…”

Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic

“…Like open water, wet snow is characterized by low backscatter. Wet snow typically absorbs the microwave signal and reduces the backscatter intensity significantly (further TerraSAR-X examples in similar settings: Antonova et al, 2016;Mora et al, 2017;Stettner et al, 2018), which caused the false classification result.…”

“…They found that the separability between sand and water backscatter in Cband strongly depends on the incidence angle and polarization. Differences in applicability of certain wavelengths are also to be expected due to their varying sensitivity to waves on the water and surface roughness (modified by vegetation and snow; examples from Arctic sites in Stettner et al, 2018;Bartsch et al, 2020) on land, which leads to ambiguities. Wet snow appears similar to water (low backscatter), as do radar shadow areas.…”