A predictive model for the spectral "bioalbedo" of snow
A predictive model for the spectral "bioalbedo" of snow
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DOI：
10.1002/2016jf003932
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发表时间：
2017-01-01
影响因子：
3.9
通讯作者：
Tranter, M.
中科院分区：
文献类型：
--
作者：
Cook, J. M.;Hodson, A. J.;Tranter, M.
我们提出了第一个物理模型的光谱biopolydo的雪，它预测的光谱反射率的积雪污染与不同浓度的红色雪藻不同的直径和色素浓度，然后估计的影响，藻类对融雪。生物光学模型估计单个细胞的吸收系数;辐射传输方案计算被藻类细胞污染的雪的光谱反射率，然后将其与入射光谱辐照度进行卷积以提供反射率。反照率，然后用来驱动一个点表面能量平衡模型来计算积雪融化速率。该模型被用来调查雪的敏感性藻类生物量和色素沉着，包括地下藻华。然后，该模型被用来重新创建真实的光谱数据从高塞拉（CA，美国）和宽带的光谱数据从Mittivakkat Gletscher（格陵兰东南部）。最后，光谱特征识别，可用于识别生物在雪和冰从遥感光谱反射率数据。我们的模拟不仅表明，藻类水华可以影响积雪的融化速度和融化速度，但也强调，间接反馈相关的存在是一个关键的不确定性，必须进行调查。
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.