Algorithms to estimate Antarctic sea ice algal biomass from under-ice irradiance spectra at regional scales

• Published in: Marine Ecology Progress Series
• Main Authors: Melbourne-Thomas, Jessica, Meiners, Klaus M., Mundy, C. J., Schallenberg, Christina, Tattersall, Katherine L., Dieckmann, Gerhard S.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Inter-Research 2015
• Subjects: Antarc*; Antarctic; Antarctica; East Antarctica; ice core; Sea ice; Weddell Sea
• Online Access: https://epic.awi.de/id/eprint/39094/; https://epic.awi.de/id/eprint/39094/1/m536p107%281%29.pdf; https://hdl.handle.net/10013/epic.46300; https://hdl.handle.net/10013/epic.46300.d001

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 chla 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 vari- ance 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.