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SeasonalSnow

IceSat-2 Snowdepth and SWE over mountains: a workflow using satellite laser altimetry to resolve topography/SWE(snow water equivalent) over complex terrain.

General Objective

Compare and evaluate ICESat-2 data with high resolution DEMs (airborne lidar/satellite stereo) collected at lower latitudes, charecterize accuracy and explore potential of seasonal SWE estimation. We will be building upon 2019 ICESat-2 HackWeek projects, namely topohack and .

Collaborators

Team Lead

Nina Argon

Data Science Leads

Joachim Meyer

Shashank Bhushan

DataSets

Tools

  • Python Scientific Analysis Stack, Geospatial Stack
  • pdal
  • icepyx

High-level Goals (Tentative)

  • Learn how to access ICESat-2 data efficiently with icepyx.
  • Evaluate/Compare the topography resolved by ICESat-2 ATLO6 and ATL03 profiles along steep mountains with topographic profiles returned from high-resolution DEMs.
  • Classify IceSat-2 ATL06 or ATL03 measurements as snow-on or snow-off using remote sensing product such as C-SNOW.
  • Get a sense of snow accumulation (depth) by comparing Snow-off DEM over Grand Mesa and other ASO sites with winter (October to February) IceSat-2 collects.
  • Snow-depth to SWE conversion.
  • Evaluate the accuaracy of the derived SWE.
  • Compare ATL03 with ATL06. Quantify uncetainty in snow volume estimates using snow depth derived using ATL03 vs ATL06.
  • Breakdown/analyse/process ATLO3 pointcloud using pdal.
  • Explore ATL03 and ATL07/ATL06 products over Sea-ice
  • Seasonal snow over sea-ice
  • Convert ATL03 data to entwine (cloud-optimised point cloud format) and visualise on potree
  • Bulk snodas download scripts
  • Compare ATL06 elevations with raster DEM

Study Sites:

  • Areas with ground truth LiDAR/SWE estimates avalible (eg. Airborne Snow Observatory(ASO) sites)
  • TBD

Team Contribution Workflow:

We will strive to follow the workflow proposed by the Hackweek team. This should make our life easier, free of any merge conflicts.

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