A predictive model for the spectral “bioalbedo” of snow
We present the first physical model for the spectral “bioalbedo” of snow, which predicts the spectral reflectance of snowpacks contaminated with variable concentrations of red snow algae with varying diameters and pigment concentrations and then estimates the effect of the algae on snowmelt. The bio...
Published in: | Journal of Geophysical Research: Earth Surface |
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Online Access: | https://pure.au.dk/portal/en/publications/429f6c1a-6407-4b23-847b-dcb389dce1ab https://doi.org/10.1002/2016JF003932 |
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ftuniaarhuspubl:oai:pure.atira.dk:publications/429f6c1a-6407-4b23-847b-dcb389dce1ab 2024-04-28T08:21:56+00:00 A predictive model for the spectral “bioalbedo” of snow Cook, J. M. Hodson, A. J. Taggart, A. J. Mernild, S. H. Tranter, Martyn 2017-02-17 https://pure.au.dk/portal/en/publications/429f6c1a-6407-4b23-847b-dcb389dce1ab https://doi.org/10.1002/2016JF003932 eng eng https://pure.au.dk/portal/en/publications/429f6c1a-6407-4b23-847b-dcb389dce1ab info:eu-repo/semantics/restrictedAccess Cook , J M , Hodson , A J , Taggart , A J , Mernild , S H & Tranter , M 2017 , ' A predictive model for the spectral “bioalbedo” of snow ' , Journal of Geophysical Research: Earth Surface , vol. 122 , no. 1 , pp. 434-454 . https://doi.org/10.1002/2016JF003932 albedo biooptics life detection melt radiative transfer spectral reflectance article 2017 ftuniaarhuspubl https://doi.org/10.1002/2016JF003932 2024-04-10T23:46:40Z We present the first physical model for the spectral “bioalbedo” of snow, which predicts the spectral reflectance of snowpacks contaminated with variable concentrations of red snow algae with varying diameters and pigment concentrations and then estimates the effect of the algae on snowmelt. The biooptical model estimates the absorption coefficient of individual cells; a radiative transfer scheme calculates the spectral reflectance of snow contaminated with algal cells, which is then convolved with incoming spectral irradiance to provide albedo. Albedo is then used to drive a point-surface energy balance model to calculate snowpack melt rate. The model is used to investigate the sensitivity of snow to algal biomass and pigmentation, including subsurface algal blooms. The model is then used to recreate real spectral albedo data from the High Sierra (CA, USA) and broadband albedo data from Mittivakkat Gletscher (SE Greenland). Finally, spectral “signatures” are identified that could be used to identify biology in snow and ice from remotely sensed spectral reflectance data. Our simulations not only indicate that algal blooms can influence snowpack albedo and melt rate but also highlight that “indirect” feedback related to their presence are a key uncertainty that must be investigated. Article in Journal/Newspaper Greenland Aarhus University: Research Journal of Geophysical Research: Earth Surface 122 1 434 454 |
institution |
Open Polar |
collection |
Aarhus University: Research |
op_collection_id |
ftuniaarhuspubl |
language |
English |
topic |
albedo biooptics life detection melt radiative transfer spectral reflectance |
spellingShingle |
albedo biooptics life detection melt radiative transfer spectral reflectance Cook, J. M. Hodson, A. J. Taggart, A. J. Mernild, S. H. Tranter, Martyn A predictive model for the spectral “bioalbedo” of snow |
topic_facet |
albedo biooptics life detection melt radiative transfer spectral reflectance |
description |
We present the first physical model for the spectral “bioalbedo” of snow, which predicts the spectral reflectance of snowpacks contaminated with variable concentrations of red snow algae with varying diameters and pigment concentrations and then estimates the effect of the algae on snowmelt. The biooptical model estimates the absorption coefficient of individual cells; a radiative transfer scheme calculates the spectral reflectance of snow contaminated with algal cells, which is then convolved with incoming spectral irradiance to provide albedo. Albedo is then used to drive a point-surface energy balance model to calculate snowpack melt rate. The model is used to investigate the sensitivity of snow to algal biomass and pigmentation, including subsurface algal blooms. The model is then used to recreate real spectral albedo data from the High Sierra (CA, USA) and broadband albedo data from Mittivakkat Gletscher (SE Greenland). Finally, spectral “signatures” are identified that could be used to identify biology in snow and ice from remotely sensed spectral reflectance data. Our simulations not only indicate that algal blooms can influence snowpack albedo and melt rate but also highlight that “indirect” feedback related to their presence are a key uncertainty that must be investigated. |
format |
Article in Journal/Newspaper |
author |
Cook, J. M. Hodson, A. J. Taggart, A. J. Mernild, S. H. Tranter, Martyn |
author_facet |
Cook, J. M. Hodson, A. J. Taggart, A. J. Mernild, S. H. Tranter, Martyn |
author_sort |
Cook, J. M. |
title |
A predictive model for the spectral “bioalbedo” of snow |
title_short |
A predictive model for the spectral “bioalbedo” of snow |
title_full |
A predictive model for the spectral “bioalbedo” of snow |
title_fullStr |
A predictive model for the spectral “bioalbedo” of snow |
title_full_unstemmed |
A predictive model for the spectral “bioalbedo” of snow |
title_sort |
predictive model for the spectral “bioalbedo” of snow |
publishDate |
2017 |
url |
https://pure.au.dk/portal/en/publications/429f6c1a-6407-4b23-847b-dcb389dce1ab https://doi.org/10.1002/2016JF003932 |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
Cook , J M , Hodson , A J , Taggart , A J , Mernild , S H & Tranter , M 2017 , ' A predictive model for the spectral “bioalbedo” of snow ' , Journal of Geophysical Research: Earth Surface , vol. 122 , no. 1 , pp. 434-454 . https://doi.org/10.1002/2016JF003932 |
op_relation |
https://pure.au.dk/portal/en/publications/429f6c1a-6407-4b23-847b-dcb389dce1ab |
op_rights |
info:eu-repo/semantics/restrictedAccess |
op_doi |
https://doi.org/10.1002/2016JF003932 |
container_title |
Journal of Geophysical Research: Earth Surface |
container_volume |
122 |
container_issue |
1 |
container_start_page |
434 |
op_container_end_page |
454 |
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1797583942633652224 |