Preprints
https://doi.org/10.5194/egusphere-2024-1473
https://doi.org/10.5194/egusphere-2024-1473
19 Jun 2024
 | 19 Jun 2024

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.
Share

Journal article(s) based on this preprint

06 Nov 2024
Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024,https://doi.org/10.5194/tc-18-5015-2024, 2024
Short summary
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Remotely sensed properties of snow are dependent on accurate terrain information, which for a...
Share