Preprints
https://doi.org/10.5194/egusphere-2024-1473
https://doi.org/10.5194/egusphere-2024-1473
19 Jun 2024
 | 19 Jun 2024
Status: this preprint is open for discussion.

Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements

Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn

Abstract. Accurately modelling snow albedo and specific surface area (SSA) are essential for monitoring the cryosphere in a changing climate and are parameters that inform hydrologic and climate models. These snow surface properties can be modelled from spaceborne imaging spectroscopy measurements but rely on Digital Elevation Models (DEMs) of relatively coarse spatial scales (e.g. Copernicus at 30 m) degrade accuracy due to errors in derived products – like aspect. In addition, snow deposition and redistribution can change the apparent topography and thereby static DEMs may not be considered coincident with the imaging spectroscopy dataset. Testing in three different snow climates (tundra, maritime, alpine), we established a new method that simultaneously solves snow, atmospheric, and terrain parameters, enabling a solution that is more unified across sensors and introduces fewer sources of uncertainty. We leveraged imaging spectroscopy data from AVIRIS-NG and PRISMA (collected within 1 hour) to validate this method and showed a 15 % increase in performance for the radiance-based method versus using the static DEM (from r=0.52 to r=0.60). This concept can be implemented in future missions such as Surface Biology and Geology (SBG) and Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn

Status: open (until 31 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-1473', Alexander Kokhanovsky, 12 Jul 2024 reply
    • CC1: 'Reply on RC1 - p.13, line 159', Brent Wilder, 15 Jul 2024 reply
  • RC2: 'Comment on egusphere-2024-1473', Jeff Dozier, 15 Jul 2024 reply
    • CC2: 'Reply on RC2, Line 159', Brent Wilder, 16 Jul 2024 reply
    • CC3: 'Reply on RC2, Line 410', Brent Wilder, 18 Jul 2024 reply
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn

Model code and software

GOSHAWK Brenton A. Wilder https://zenodo.org/doi/10.5281/zenodo.10652709

Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn

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Short summary
Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones, are often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.