Algorithms to estimate Antarctic sea ice algal biomass from under-ice irradiance spectra at regional scales
The presence of algal pigments in sea ice alters under-ice irradiance spectra, and the relationship between these variables can be used as a non-invasive means for estimating ice-associated algal biomass on ecologically relevant spatial and temporal scales. While the influence of snow cover and ice...
| Published in: | Marine Ecology Progress Series |
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| Main Authors: | , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Inter-Research
2015
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| Subjects: | |
| Online Access: | https://doi.org/10.3354/meps11396 http://ecite.utas.edu.au/103249 |
| Summary: | The presence of algal pigments in sea ice alters under-ice irradiance spectra, and the relationship between these variables can be used as a non-invasive means for estimating ice-associated algal biomass on ecologically relevant spatial and temporal scales. While the influence of snow cover and ice algal biomass on spectra transmitted through the snow-ice matrix has been examined for the Arctic, it has not been tested for Antarctic sea ice at regional scales. We used paired measurements of sea ice core chl a concentrations and hyperspectral-transmitted under-ice irradiances from 59 sites sampled off East Antarctica and in the Weddell Sea to develop algorithms for estimating algal biomass in Antarctic pack ice. We compared 4 approaches that have been used in various bio-optical studies for marine systems: normalised difference indices, ratios of spectral irradiance, scaled band area and empirical orthogonal functions. The percentage of variance explained by these models ranged from 38 to 79%, with the best-performing approach being normalised difference indices. Given the low concentrations of integrated chl a observed in our study compared with previous studies, our statistical models performed surprisingly well in explaining variability in these concentrations. Our findings provide a basis for future work to develop methods for non-invasive time series measurements and medium- to large-scale spatial mapping of Antarctic ice algal biomass using instrumented underwater vehicles. |
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