Oceanography | Early Online Release
Oceanography | Vol. 39, No. 1
ADVANCING
MONITORING OF
NEARSHORE ANTARCTIC
SEA ICE AND BENTHIC
ECOSYSTEMS WITH
HIcyBot
By Emiliano Cimoli, Juan Carlos Montes-Herrera, Vonda Cummings,
Peter Marriott, Ryan S. Haynes, and Vanessa Lucieer
BREAKING WAVES
Oceanography | Early Online Release
Early Online Release | Oceanography
BACKGROUND
Sea ice is the dominant feature structuring Antarctic coastal marine
ecosystems. Fast ice (i.e., annual sea ice attached to the shore) can
support some of the most productive marine habitats on Earth,
contributing approximately 55%–68% of the total primary pro
duction in coastal areas (McMinn et al., 2010). Other contributors
include phytoplankton as well as benthic microalgae and macro
algae. As coastal sea ice extent and stability shift, the balance of pri
mary production between phytoplankton, sea ice algae, and ben
thic microalgae may change significantly, with cascading impacts
on tightly linked seafloor biodiversity and overall ecosystem func
tioning (Norkko et al., 2007; Wing et al., 2018).
Seasonal changes in fast ice thickness, extent, and snow cover
regulate light transmission, strongly influencing under-ice pri
mary production. The presence or absence of sea ice alters aquatic
photosynthesis by modifying light intensity and spectral qual
ity (Soja-Woźniak et al., 2025). The shallow depths typical of ice-
covered coastal zones further promote efficient export of ice algae
and other particulate matter to the seafloor via both primary and
secondary production, resulting in tight cryo-pelagic-benthic
coupling. The resulting quantity and quality of phytodetritus
through the seasons play a role in shaping the diversity and abun
dance of benthic microbial, algal, and invertebrate communities
(Rossi et al., 2019).
Historically, we have struggled to capture the high spatio-
temporal variability at which sea ice biogeochemical processes
occur (from 1 µm to 10–1,000 m, from seconds to decades).
Shifts in the sources and fluxes of organic matter from the ice to
benthic food webs may serve as early indicators of broader eco
logical responses to physical environmental changes driven by cli
mate change in Antarctica (Clark et al., 2017). Looking directly
below the surface, the complexity of the Antarctic seafloor speaks
for itself. Shallow benthic ecosystems display remarkable struc
tural, functional, and biological diversity, shaped by dense assem
blages of sessile epifaunal and infaunal invertebrates, fishes
and algae, patchy habitat mosaics, and tightly coupled trophic
interactions that persist despite extreme conditions (Gutt et al.,
2015). The scales and intensity of this coupling vary consider
ably with depth and connectivity with primary food sources (from
meters to kilometers).
Because nearshore productivity and biodiversity are closely
linked to sea ice dynamics, there is a pressing need for monitor
ing methods that can efficiently and simultaneously track changes
in light levels, changes in cryo-benthic communities, and food
pulses. These techniques must also be practical for use in extremely
remote and challenging environments—such as under two meters
of thick Antarctic fast ice. Historically, this has been done using
diver-based surveys, which are necessarily restricted in spatial and
temporal extent and in maximum depth. Recently, the availabil
ity of small remotely operated vehicles (ROVs) has enabled the
expansion of coastal under-ice sampling areas at reduced logis
tical complexity. Incorporating hyperspectral imaging into these
platforms can expand their utility beyond mapping the under-ice
seascape and associated biodiversity, enabling the spatial and tem
poral quantification of ecosystem function through measurements
of biomass and accessory pigment estimates that can serve as indi
cators of organismal health.
UNDERWATER HYPERSPECTRAL IMAGING
The devil, of course, is in the detail. Process studies should strive
to take observations at the spatial resolution over which the pro
cess operates (Levin, 1992). Analogous to the rapid rise of close-
range terrestrial hyperspectral imaging using drones over the
past decade, in situ underwater hyperspectral imaging (UHI) has
emerged as a powerful tool for generating high-resolution biogeo
chemical maps of seafloor properties. First introduced as a ded
icated subdiscipline of marine remote sensing by Johnsen et al.
(2013), proximity-based hyperspectral sensing in marine envi
ronments is an emerging methodology; to date, only a handful of
documented systems have been deployed on diver-operated plat
forms, ROVs, or under-ice sleds across diverse habitats from trop
ical reefs to the deep sea (Montes-Herrera et al., 2021; Summers
ABSTRACT. In Antarctica’s coastal waters, the seafloor hosts a surprisingly rich diversity of life, shaped by intricate interactions
between sea ice and flora and faunal communities. As sea ice forms and melts seasonally, it modulates the availability of light to both
the microscopic algae living within and beneath the ice itself (sympagic algae), and, in turn, influences the productivity and biodiver
sity of the benthic ecosystems below. The complex pathways that connect sea ice dynamics with the benthos are particularly vulnerable
to climate-driven changes in sea ice cover. Understanding responses to a rapidly changing icescape, shaped by warming air and ocean
conditions, is essential for predicting the future of benthic biodiversity and its ecosystem functions and services, including biogeochem
ical cycling and carbon storage. Key to this understanding is coupling structural and biological observations across these mirrored eco
systems. We describe a proof-of-concept under-ice hyperspectral imaging platform, HIcyBot, that we deployed in the Ross Sea Marine
Protected Area in 2023. HIcyBot is a remotely operated vehicle that leverages emerging sea ice bio-optical models to quantify ice algae
biomass and classifies fine-scale seafloor features through integration of underwater hyperspectral imaging, stereophotogrammetry, and
acoustic positioning. Through near simultaneous high-resolution characterization of these under-ice realms, the platform introduces a
novel, spatially explicit approach to understanding how biodiversity and the ecosystem function beneath Antarctic sea ice and how they
are responding to a changing icescape.
Oceanography | Early Online Release
et al., 2022; Lange et al., 2024; Anhaus et al., 2025). Its repeatable,
non-invasive capabilities make UHI especially suited for tracing
and quantifying fine-scale biogeochemical processes in remote
and sensitive environments.
UNDER-ICE MAPPING WITH HICYBOT
The HIcyBot system aims to produce co-georeferenced UHI and
three-dimensional (3D) structural data of the seafloor and overlying
fast ice based on several surveys conducted within hours of one
another (Figure 1). The optical components include three primary
subsystems: a central hyperspectral imager, a stereophotogrammet
ric dual-camera system, and a USB live-stream reference camera
contained within a tethered underwater enclosure (Figure 2).
An eight-hydrophone-array ultra-short baseline (USBL) sen
sor was integrated to enable geolocation of pushbroom hyperspec
tral frames with synchronized, timestamped Global Navigation
Satellite System (GNSS) and inertial navigation data. The
co-mounted dual-vision cameras leverage stereophotogramme
try techniques to derive camera positions and orientations from
overlapping stereo images, providing independent vehicle pose
estimates that support local motion compensation for the UHI in
conditions where USBL signal quality is degraded beneath the ice
(Figure 2). The payload enclosure was incorporated into a custom
ized BlueROV2 heavy-configuration kit. A key design feature was
the addition of bottom ballast to create a pendulum-like configu
ration, enabling passive, stable vertical alignment for nadir-facing
(a) Sea-Ice Mode
(b) Seafloor Mode
FIGURE 1. HIcyBot is a proof-of-concept under-ice remotely operated vehicle (ROV) with hyperspectral imaging capability, designed for concomitant mapping
of (a) sympagic (ice-associated) habitats, and (b) benthic, seafloor-associated habitats that remain largely inaccessible to other marine sensing and survey
techniques. Pushbroom sensors record one spatial line per frame as the platform moves, forming a 3D hyperspectral cube (X, Y, λ) linking spatial and spec-
tral domains. GNSS = Global Navigation Satellite System. USBL = ultra-short baseline. MPA = Marine Protected Area. Click on the arrow icons to the left of
this figure to view videos of the HIcyBot in (a) sea ice, and (b) seafloor modes.
Early Online Release | Oceanography
imaging without active control. Technical specifications, image
pre-processing, and georeferencing challenges for UHI are pro
vided in the online Supplementary Materials.
FIELD TEST IN THE ROSS SEA MPA
The system was first deployed from November 7 to 22, 2023,
under land-fast sea ice at Cape Evans, within the Ross Sea Marine
Protected Area (MPA), Antarctica, the world’s largest MPA. This
region currently represents one of the least human-impacted envi
ronments, yet one of the most challenging to study due to extreme
Antarctic conditions, remoteness, and extensive sea ice cover. It
is considered a benthic biodiversity hotspot fueled by overlying
under-ice algae of the highest concentrations on record. The pri
mary objective was to generate preliminary products showing
ice-associated (sympagic) algal biomass or chlorophyll a (Chl-a)
concomitant with seafloor biodiversity, including class-level maps
incorporating photosynthetic and accessory pigments of selected
organism groups, all resolved along the hyperspectral transects at
millimeter scales.
LOOKING ABOVE: AN ICE ALGAL COMMUNITY
PRODUCT EXAMPLE
Quantifying sympagic biomass was previously only achievable
with labor-intensive ice coring techniques and laboratory process
ing. UHI offers a significantly improved way to monitor the bio
physical process in sea ice at varying spatial and temporal scales.
Building on emerging work to develop bio-optical models for
under-ice hyperspectral imaging in Antarctic land-fast sea ice, a
“spectra to biomass” correlation was applied at a pixel level to the
“x, y, λ hypercube” to fundamentally enhance the estimation of
under-ice algal biomass and their microscale distribution patterns
remotely and quantitatively (e.g., LAUC650–700 from Cimoli et al.,
2025; see Figure 2 workflow).
Figure 3a provides an example of a quantitative high-
resolution estimation of sympagic Chl-a from an ROV. It shows
an ice algal biomass map derived from optimized bio-optical
algorithms with fine-scale variability associated with features
such as brinicles. Brinicles, often referred as “ice stalactites,” are
complex physical structures that form as dense, saline brine is
excluded from seawater as it freezes and drains downward into
the ocean, freezing the surrounding seawater and creating hol
low, downward-growing ice tubes (Testón-Martínez et al., 2024).
Brinicles appear to support up to twice the amount of algal bio
mass around their edges compared with surrounding flat areas
(Figure 3a), potentially providing important habitat for under‑ice
grazers through both enhanced food availability and their shel
tering role. With HIcyBot, we can scale up efforts to quantify
and characterize ice algal biomass through larger spatial swaths,
achieving millimeter‑scale resolution while extending cover
age to survey‑grid scales (e.g., 100 × 100 m), and potentially
providing pigment proxies of algal photoacclimation state and
health (Cimoli et al., 2025).
LOOKING BELOW: A BENTHIC BIODIVERSITY
PRODUCT EXAMPLE
HIcyBot’s internal enclosure can quickly be physically inverted
within the customized BlueROV2 frame between deployments,
enabling a downward-facing configuration for seafloor surveys.
Operating roughly half a meter above the dark benthos at depths
of 10–40 m with active illumination, this setup preserves vehi
cle nadir stability and allows detailed imaging of benthic habitats
located beneath previously scanned ice surfaces.
FIGURE 2. The data processing workflow for both sea-ice and seafloor
HIcyBot operating modes (upward- and downward-looking) includes opti-
cal and acoustic data streams and subsequent processing steps that lead
to example data products, with both modes sharing the same general pre-
processing pipeline. RGB = red, green, blue. UHI = underwater hyperspec-
tral imaging. USBL = ultra-short baseline. DEM = digital elevation model.
CCA = crustose coralline algae. MPB = microphytobenthos.
Oceanography | Early Online Release
(a) Sea Ice
(b) Seafloor Seep
(c) Seafloor
FIGURE 3. Stereophotogrammetry enabled 3D reconstructions of the (a) sea-ice underside and (b,c) seafloor. Georeferenced models provided camera
pose data (pitch, roll, yaw) that, combined with USBL positioning, aided hyperspectral image geolocation. (a) Under-ice algal biomass map derived from
bio-optical algorithms showing fine-scale variability associated with brinicles (“ice stalactites”). (b) and (c) Spectral Angle Mapper-based seafloor classi-
fication outputs identify major benthic classes, including a methane seep, millimeter-scale spectral proxies maps of Chl-a (MPBI), and phycoerythrin in
crustose coralline algae (ANMB).
Early Online Release | Oceanography
Following reflectance conversion, we used the Spectral Angle
Mapper (SAM) algorithm as an example of UHI classifica
tion. SAM compares the angle between the spectral signature of
each pixel and known target reference spectra, essentially mea
suring how similar two spectral “color patterns” are, regard
less of brightness. Because of the close distance to the target and
use of artificial lighting, even small changes in distance from
the sensor to the seafloor can cause large intensity variations
(Dumke et al., 2018).
Automated hyperspectral classification of the seafloor at
Cape Evans enabled generation of high-resolution maps show
ing benthic biodiversity, habitat composition, and the area cov
ered by each class instantly and independent of the shape or illu
mination of the targets (Figure 3b). Substrate was distinguished
between pebble/gravel and fine sand, aligning well with the known
geomorphology of the site. A diffuse surface layer, likely composed
of microalgae and/or diatoms, was observed across these sedi
ment types. This “fluff layer” may be epipsammic, living on sand
grains, or epipelic, occurring on finer sediments like mud or silt
(Sutherland, 2008). Its origin remains uncertain and could either
reflect in situ microphytobenthic growth or settled material from
the water column or the ice-algae above.
The red macroalgae, Phyllophora antarctica, dominated the
benthic assemblages, often attached to the sea urchin Sterechinus
neumayeri via their tube feet or overgrown by brown microalgal
assemblages. The urchins camouflage themselves with algal fronds
and debris, forming a mutualism in which the algae avoid deep
displacement while the urchins gain protection, a known inter
action reinforced by Phyllophora’s chemical defenses against
herbivory. Crustose coralline algae, considered important eco
system engineers, were found encrusting many of the exposed
rocks. Automated image classification further revealed a diversity
of invertebrates, including Abatus spp. (burrowing echinoids with
five radial grooves), and shells of the bivalve Laternula elliptica. The
cod icefish (Trematomus bernacchii) can be seen swimming above
a “starry” seafloor populated by the sea star Odontaster spp., whose
colors range from pink to orange-yellow. Nemerteans (Parborlasia
corrugatus), conspicuous worm-like scavengers and predators,
were found traversing the seafloor or curled. We also captured and
spectrally classified imagery of a newly emerged methane seep at
Cape Evans (Figure 3b) adding to recent observations along the
Ross Sea coast (Seabrook et al., 2025).
Following classification, the true potential of UHI lies in
targeted millimeter-scale mapping of benthic organisms’ bio
chemical traits in situ. For example, here we applied algorithms
developed under controlled laboratory conditions to estimate
spectral proxies of R-phycoerythrin, an indicator of environ
mental state and health in coralline algae (using ANMB565 from
Montes-Herrera et al., 2024), and Chl-a in epipelic communities,
using models derived from comparable biological systems such
as microphytobenthos (using MPBI from Chennu et al., 2013;
Figure 3c). While these are realistic proxies, consistent quantita
tive field calibrations remain limited due to the need for species-
and site-specific validation, robust calibration datasets, and stan
dardized protocols.
OPTICAL MONITORING OF CRYO-BENTHIC
LINKAGES IN A CHANGING ICESCAPE
Projected shifts in seasonal sea ice dynamics underscore the grow
ing need to assess how well existing tools can capture the immediate
responses of shallow, ice-covered marine habitats across different
spatio-temporal scales. While traditional ice coring and seafloor
surveys provide valuable ecological insights, new high-resolution
optical systems offer non-invasive methods for capturing the spec
tral fingerprints of different sympagic and benthic organisms and
substrates that allow their classification and quantification based
on their spectral features (Figure 4a–c).
Spatially explicit UHI data open a new frontier for exploring
and quantifying the dynamic exchange between sea ice and the
seafloor, offering a “spectral breadcrumb trail” that links the ice
canopy to the life below. For example, can spectral fingerprints help
quantitatively track biogeochemical linkages between sea ice and
the seafloor? Do sea ice algae retain pigment-specific signatures,
and/or remain detectable after seafloor deposition, and can they
be distinguished from native benthic phototrophs? Understanding
how light transmission, algal productivity, and “slough-off” affect
benthic biodiversity (both phylogenetic and functional) demands
an integrated approach combining hyperspectral imaging with
process-based measurements (e.g., ice-hanging sediment traps).
Figure 4 highlights spectral characteristics of the coupled ice-
benthic system, enabling quantification of light-driven impacts
on benthic communities and associated biogeochemical pro
cesses. The system’s ability to capture both structural and compo
sitional complexity, including organism volume, habitat architec
ture, and phototrophic biomass per unit area, provides a solid basis
for developing indicators of the emerging Antarctic blue carbon
potential. It may also help in differentiation of the relative con
tributions of sympagic, pelagic, and authigenic carbon sources,
which is important for guiding conservation priorities and climate
mitigation strategies in polar regions (Sands et al., 2023).
FUTURE DEVELOPMENTS
The HIcyBot system is transitioning from a proof-of-concept to a
functional monitoring toolkit, with the potential to enable remote
observations of under-ice ecological and biogeochemical pro
cesses that are otherwise too difficult to sustain. Such efforts aim
to be integrated within broader, standardized long-term observa
tion programs at representative sites like those coordinated by the
Antarctic Nearshore and Terrestrial Observing System (ANTOS)
Expert Group, and may ultimately evolve into fixed, station-like
robotic observatories with edge artificial intelligence capabilities,
forming part of integrated coastal monitoring networks.
Oceanography | Early Online Release
The UHI community now faces a pressing need for develop
ing standardized protocols in georectification, radiometric cor
rection, and reflectance calibration to fully unlock the technique’s
potential. Encouragingly, efforts are already underway to estab
lish greater consistency and rigor to this emerging research field
(Løvås et al., 2022; Liu et al., 2024).
Broader implementation also depends on both technology
and accessibility. Given the high costs and logistical complexity
of UHI and Antarctic work, maximizing data cost performance
remains critical. Identifying the most informative narrow spec
tral bands will support the creation of simpler, lower-cost multi
spectral systems and help to democratize underwater spectral
imaging applications.
SUPPLEMENTARY MATERIALS
The supplementary materials are available online at https://doi.org/10.5670/
oceanog.2026.e107.
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ACKNOWLEDGMENTS
This research was supported by the Australian Research Council Special Research
Initiative, Australian Centre for Excellence in Antarctic Science (project number
SR200100008), and the New Zealand Antarctic Science Platform (K882-2324-A
RSRED – Benthic Sentinel Sites; Ministry of Business, Innovation, and Employment
Grant/Award Number MBIE ANTA1801). We are grateful to staff at Antarctica
New Zealand for their field-based operational and logistical support. Mechatronics
support was provided by Adept Turnkey Ltd. and the UTAS Central Science
Laboratory, with special thanks to Sean Sarikas and Philip Hortin. This work contrib-
utes to BEPSII Task Group 2: New Technologies.
AUTHORS
Emiliano Cimoli (emiliano.cimoli@utas.edu.au), Institute for Marine and Antarctic
Studies, College of Sciences and Engineering, University of Tasmania, and Australian
Centre for Excellence in Antarctic Science, University of Tasmania, Hobart, Tasmania,
Australia. Juan Carlos Montes-Herrera, Institute for Marine and Antarctic Studies,
College of Sciences and Engineering, University of Tasmania, and Discipline of
Geography and Spatial Sciences, School of Technology, Environments and Design,
College of Sciences and Engineering, University of Tasmania, Hobart, Tasmania,
Australia. Vonda Cummings and Peter Marriott, Earth Sciences New Zealand,
Wellington, New Zealand. Ryan S. Haynes, Discipline of Geography and Spatial
Sciences, School of Technology, Environments and Design, College of Sciences and
Engineering, University of Tasmania, Hobart, Tasmania, Australia. Vanessa Lucieer,
Institute for Marine and Antarctic Studies, College of Sciences and Engineering,
University of Tasmania, and Australian Centre for Excellence in Antarctic Science,
University of Tasmania, Hobart, Tasmania, Australia.
ARTICLE CITATION
Cimoli, E., J.C. Montes-Herrera, V. Cummings, P. Marriott, R.S. Haynes, and V. Lucieer.
2026. Advancing monitoring of nearshore Antarctic sea ice and benthic ecosystems
with HIcyBot. Oceanography 39(1), https://doi.org/10.5670/oceanog.2026.e107.
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