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

Melbourne-Thomas, Jess; Meiners, Klaus M.; Mundy, C. J.; Schallenberg, Christina; Tattersall, Katherine; Dieckmann, Gerhard

doi:10.3354/meps11396

Cited by 27 publications

(78 citation statements)

References 45 publications

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“…However, in ice of near-uniform thickness and largely free of snow, with imagery to support the perception of biomass patchiness we consider our assumption of a 20 dominant effect of chla consistent with other published observations (e.g. Melbourne-Thomas et al, 2015;Mundy et al, 2007) and a useful demonstration of the utility of AUV-based observations in addressing spatial variability in sea ice algae.…”

Section: Discussionsupporting

confidence: 86%

“…Variability in ice algal biomass has been recognized at a range of length scales (Melbourne-Thomas et al, 2015;Ambrose et al, 2005). Our data are, to our knowledge, unique in that they provide continuous lines of coverage from which the length scales of variance can be estimated.…”

Section: Discussionmentioning

confidence: 87%

“…3). These wavebands correspond to areas of the spectrum that penetrate cold ice relatively well (Perovich, 1990), while 470 nm is close to the peak of ice algal absorption whereas 565 nm is close to the minimum algal absorption (Hawes et al, 2012;Melbourne-Thomas et al, 2015), and thus this NDI can be expected to be sensitive to biomass. At least one other investigation has found ratios constructed from similar wavelengths to be effective predictors of chla concentrations (Fritsen 30 et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

“…These differences in ice properties make universal algorithms for converting optical properties of ice to algal biomass elusive (Melbourne-Thomas et al, 2015), though with the development and testing of area-specific algorithms the acquisition of optical data using AUV techniques should be as equally applicable as other optical methods.…”

Section: Discussionmentioning

confidence: 99%

“…Most of these techniques have used empirically-derived Normalized Difference Indices (NDI); an approach widely used in satellite-based remote sensing of terrestrial vegetation (Townshend et al, 1985). NDI values have been developed for both Arctic and Antarctic regions and have been show to explain substantial amounts of spatial variability of ice algae biomass (Mundy et al, 2007;Melbourne-Thomas et al, 2015). 10…”

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confidence: 99%

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Brief Communication: Capturing scales of spatial heterogeneity of Antarctic sea ice algae communities

Forrest

Lund-Hansen

Sorrell

et al. 2016

Preprint

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Abstract. Identifying spatial heterogeneity of sea ice algae communities is critical to predicting ecosystem response under future climate scenarios. Using an autonomous robotic sampling platform beneath sea ice in McMurdo Sound, Antarctica, we measured irradiance in spectral bands expected to describe the spatial heterogeneity. Derived estimates of ice algae biomass identified patchiness at length scales varying from 50-70 m under first-year sea ice. These results demonstrate that a 15 step-change in how these communities can be assessed and monitored. The developed methodologies could be subsequently refined to further categorize different ice algae communities and their associated productivity in both Arctic and Antarctic waters.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Brief Communication: Capturing scales of spatial heterogeneity of Antarctic sea ice algae communities

Forrest

Lund-Hansen

Sorrell

et al. 2016

Preprint

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Sea ice algae chlorophyll a concentrations derived from under-ice spectral radiation profiling platforms

Lange

Katlein

Nicolaus

et al. 2016

J. Geophys. Res. Oceans

106

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Multiscale sea ice algae observations are fundamentally important for projecting changes to sea ice ecosystems, as the physical environment continues to change. In this study, we developed upon previously established methodologies for deriving sea ice‐algal chlorophyll a concentrations (chl a) from spectral radiation measurements, and applied these to larger‐scale spectral surveys. We conducted four different under‐ice spectral measurements: irradiance, radiance, transmittance, and transflectance, and applied three statistical approaches: Empirical Orthogonal Functions (EOF), Normalized Difference Indices (NDI), and multi‐NDI. We developed models based on ice core chl a and coincident spectral irradiance/transmittance (N = 49) and radiance/transflectance (N = 50) measurements conducted during two cruises to the central Arctic Ocean in 2011 and 2012. These reference models were ranked based on two criteria: mean robustness R2 and true prediction error estimates. For estimating the biomass of a large‐scale data set, the EOF approach performed better than the NDI, due to its ability to account for the high variability of environmental properties experienced over large areas. Based on robustness and true prediction error, the three most reliable models, EOF‐transmittance, EOF‐transflectance, and NDI‐transmittance, were applied to two remotely operated vehicle (ROV) and two Surface and Under‐Ice Trawl (SUIT) spectral radiation surveys. In these larger‐scale chl a estimates, EOF‐transmittance showed the best fit to ice core chl a. Application of our most reliable model, EOF‐transmittance, to an 85 m horizontal ROV transect revealed large differences compared to published biomass estimates from the same site with important implications for projections of Arctic‐wide ice‐algal biomass and primary production.

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Influence of snow depth and surface flooding on light transmission through Antarctic pack ice

et al. 2017

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Snow on sea ice alters the properties of the underlying ice cover as well as associated physical and biological processes at the interfaces between atmosphere, sea ice, and ocean. The Antarctic snow cover persists during most of the year and contributes significantly to the sea‐ice mass due to the widespread surface flooding and related snow‐ice formation. Snow also enhances the sea‐ice surface reflectivity of incoming shortwave radiation and determines therefore the amount of light being reflected, absorbed, and transmitted to the upper ocean. Here, we present results of a case study of spectral solar radiation measurements under Antarctic pack ice with an instrumented Remotely Operated Vehicle in the Weddell Sea in 2013. In order to identify the key variables controlling the spatial distribution of the under‐ice light regime, we exploit under‐ice optical measurements in combination with simultaneous characterization of surface properties, such as sea‐ice thickness and snow depth. Our results reveal that the distribution of flooded and nonflooded sea‐ice areas dominates the spatial scales of under‐ice light variability for areas smaller than 100 m‐by‐100 m. However, the heterogeneous and highly metamorphous snow on Antarctic pack ice obscures a direct correlation between the under‐ice light field and snow depth. Compared to the Arctic, light levels under Antarctic pack ice are extremely low during spring (<0.1%). This is mostly a result of the distinctly different dominant sea ice and snow properties with seasonal snow cover (including strong surface melt and summer melt ponds) in the Arctic and a year‐round snow cover and widespread surface flooding in the Southern Ocean.

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

Cited by 27 publications

References 45 publications

Brief Communication: Capturing scales of spatial heterogeneity of Antarctic sea ice algae communities

Brief Communication: Capturing scales of spatial heterogeneity of Antarctic sea ice algae communities

Sea ice algae chlorophyll a concentrations derived from under-ice spectral radiation profiling platforms

Influence of snow depth and surface flooding on light transmission through Antarctic pack ice

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