Using synthetic aperture radar (SAR) to study our snowy mountains! Presentation: https://docs.google.com/presentation/d/1DUppjKOwNZNKDlO4dlhpo6nDn-u-VNyIH8W_XW7pXp8/edit#slide=id.p

• Eric Gagliano
• Seth Vanderwilt

• We want to see what information about snow depth, coverage, and other properties we can extract from SAR data
• Advantage of SAR- we can see through clouds, which is great for Washington state/anywhere with stormy winters
• High-resolution SAR imagery will be available soon (Capella Space, ICEYE, ...) and we want to take advantage of this daily/hourly flood of great data!

• Can we track snow melt dynamics over a season, especially in WA when have rain-on-snow & other events?
• Does SAR/InSAR give us enough signal to estimate snow depth over time?
  • Can compare CSnow versus S1 RTC AWS public dataset
• Backscatter vs interferograms for our tasks - which are more informative?
  • Snow melt - easy to identify using backscatter imagery
  • Can use phase differences to get at SWE!
  • Snow depth?
• General note: snow is challenging - material properties (water content) affect returns; but possible to extract information with some simple assumptions
• Snow melt dynamics in B.C. with google earth engine (Darychuk et al.) has nice visualizations
• DEM effects - is the same terrain dataset used in processing CSnow at 1km resolution (compromise in order to run globally), how do things compare to running at higher res (say 90m WA state)

• Sentinel-1 SAR on AWS
  • could use ASF processing (Eric has done)
• UAVSAR? maybe
• C-SNOW through 04/2019
• Grand Mesa data TBD
• WA state data including snotel TBD

• Scott Henderson's visualization tool to start https://github.com/scottyhq/sentinel1-rtc
• geopandas
• holoviz tools like HoloViews and Datashader
• rioxarray
• dinosar (InSAR processing for given area of interest on AWS) if our InSAR analysis is too intensive/too much data to run locally
• ISCE
  • Scott: run topsApp.py for single pair of image SLCs from ASF on UWGDA hub. Run processing in /tmp directory and save final outputs in /merged to your home directory

• Start by loading the data & carefully visualizing
• See if we can reproduce some interferograms like these previews
• not sure what else we'll look at yet

• We will have built some reusable tools for SAR processing & visualization that we can keep using in our research group!

• (AGU 2020 Cryosphere session) Darychuk, S.E., et al. Snow Melt Dynamics from Satellite Observations in the Lajoie Basin, British Columbia
• Lievens, H., Demuzere, M., Marshall, HP. et al. Snow depth variability in the Northern Hemisphere mountains observed from space. Nat Commun 10, 4629 (2019). https://doi-org.offcampus.lib.washington.edu/10.1038/s41467-019-12566-y
• Marin, C., Bertoldi, G., Premier, V., Callegari, M., Brida, C., Hürkamp, K., Tschiersch, J., Zebisch, M., and Notarnicola, C.: Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions, The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, 2020.
  • https://sentinel.esa.int/web/sentinel/missions/sentinel-5/news/-/article/sentinel-1-satellites-observe-snow-melting-processes page broken right now
• https://github.com/isce-framework/isce2-docs/blob/master/Notebooks/UNAVCO_2020/SAR%20Processing/SAR_Processor.ipynb great SAR notebook according to David
• NISAR science handbook