A predictive model for the spectral “bioalbedo” of snow

J. M. Cook*, A. J. Hodson, A. J. Taggart, S. H. Mernild, M. Tranter

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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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.

Original languageEnglish
Pages (from-to)434-454
Number of pages21
JournalJournal of Geophysical Research: Earth Surface
Issue number1
Early online date31 Jan 2017
Publication statusPublished - 17 Feb 2017


  • albedo
  • biooptics
  • life detection
  • melt
  • radiative transfer
  • spectral reflectance


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  • Black and Bloom

    Tranter, M.


    Project: Research

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