Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements
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...
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ftcopernicus:oai:publications.copernicus.org:egusphere120321 2024-09-15T18:39:49+00:00 Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements Wilder, Brenton A. Meyer, Joachim Enterkine, Josh Glenn, Nancy F. 2024-06-19 application/pdf https://doi.org/10.5194/egusphere-2024-1473 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1473/ eng eng doi:10.5194/egusphere-2024-1473 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1473/ eISSN: Text 2024 ftcopernicus https://doi.org/10.5194/egusphere-2024-1473 2024-08-28T05:24:22Z 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). Text Tundra Copernicus Publications: E-Journals |
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Copernicus Publications: E-Journals |
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English |
description |
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). |
format |
Text |
author |
Wilder, Brenton A. Meyer, Joachim Enterkine, Josh Glenn, Nancy F. |
spellingShingle |
Wilder, Brenton A. Meyer, Joachim Enterkine, Josh Glenn, Nancy F. Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements |
author_facet |
Wilder, Brenton A. Meyer, Joachim Enterkine, Josh Glenn, Nancy F. |
author_sort |
Wilder, Brenton A. |
title |
Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements |
title_short |
Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements |
title_full |
Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements |
title_fullStr |
Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements |
title_full_unstemmed |
Optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements |
title_sort |
optimally solving topography of snow-scaped landscapes to improve snow property retrieval from spaceborne imaging spectroscopy measurements |
publishDate |
2024 |
url |
https://doi.org/10.5194/egusphere-2024-1473 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1473/ |
genre |
Tundra |
genre_facet |
Tundra |
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eISSN: |
op_relation |
doi:10.5194/egusphere-2024-1473 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1473/ |
op_doi |
https://doi.org/10.5194/egusphere-2024-1473 |
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1810484155679506432 |