Remote sensing phenology of Antarctic green and red snow algae using WorldView satellites.

Gray, Andrew; Krolikowski, Monika; Fretwell, Peter ORCID: https://orcid.org/0000-0002-1988-5844; Convey, Peter ORCID: https://orcid.org/0000-0001-8497-9903; Peck, Lloyd S. ORCID: https://orcid.org/0000-0003-3479-6791; Mendelova, Monika; Smith, Alison G.; Davey, Matthew P.. 2021 Remote sensing phenology of Antarctic green and red snow algae using WorldView satellites. Frontiers in Plant Science, 12, 671981. 16, pp. 10.3389/fpls.2021.671981

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Text (Open Access) Copyright © 2021 Gray, Krolikowski, Fretwell, Convey, Peck, Mendelova, Smith and Davey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. fpls-12-671981.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (40MB) | Preview

Abstract/Summary

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3389/fpls.2021.671981
ISSN:	1664462X
Additional Keywords:	Snow Algae, Antarctica, Remote Sensing, Snow, Satellite, WorldView, Ecology
Date made live:	12 Jul 2021 08:31 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529753

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