Estimation of Antarctic Land‐Fast Sea Ice Algal Biomass and Snow Thickness From Under‐Ice Radiance Spectra in Two Contrasting Areas. Issue 3 (10th March 2018)

• Record Type: Journal Article
• Authors: Wongpan, P.; Meiners, K. M.; Langhorne, P. J.; Heil, P.; Smith, I. J.; Leonard, G. H.; Massom, R. A.; Clementson, L. A.; Haskell, T. G.
• Is Part Of: Journal of geophysical research. Volume 123:Issue 3(2018)
• Page Start: 1907
• Page End: 1923
• Publication Date: 2018-03-10
• Subjects: light transmittance -- sea ice properties -- ice algae -- chlorophyll a -- Antarctica -- snow thickness; Oceanography -- Periodicals; 551.4605
• DOI: 10.1002/2017JC013711
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: Fast ice is an important component of Antarctic coastal marine ecosystems, providing a prolific habitat for ice algal communities. This work examines the relationships between normalized difference indices (NDI) calculated from under‐ice radiance measurements and sea ice algal biomass and snow thickness for Antarctic fast ice. While this technique has been calibrated to assess biomass in Arctic fast ice and pack ice, as well as Antarctic pack ice, relationships are currently lacking for Antarctic fast ice characterized by bottom ice algae communities with high algal biomass. We analyze measurements along transects at two contrasting Antarctic fast ice sites in terms of platelet ice presence: near and distant from an ice shelf, i.e., in McMurdo Sound and off Davis Station, respectively. Snow and ice thickness, and ice salinity and temperature measurements support our paired in situ optical and biological measurements. Analyses show that NDI wavelength pairs near the first chlorophyll a (chl a ) absorption peak (≈440 nm) explain up to 70% of the total variability in algal biomass. Eighty‐eight percent of snow thickness variability is explained using an NDI with a wavelength pair of 648 and 567 nm. Accounting for pigment packaging effects by including the ratio of chl a ‐specific absorption coefficients improved the NDI‐based algal biomass estimation only slightly. Our new observation‐based algorithms can be used to estimate Antarctic fast ice algal biomass and snow thickness noninvasively, for example, by using moored sensors (time series) or mapping their spatial distributions using underwater vehicles. Key Points: New algorithms to estimate Antarctic land‐fast sea ice algal biomass and snow thickness from under‐ice radiance spectra The algorithms were developed for high biomass regimes in columnar and incorporated platelet ice Inclusion of chlorophyll a ‐specific ice algal absorbance and pigment packaging slightly affect biomass predictability