sar-eric-seth

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

Name(s) of individual or team members

• Eric Gagliano
• Seth Vanderwilt

Some introductory background information

• 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!

Problem statement, question(s) and/or objective(s)

• 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)

Datasets you will use (with links, if available)

• 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

Tools/packages you’ll use (with links)

• 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

Planned methodology/approach

• 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

Expected outcomes

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

References

• (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