# Inferring inherent optical properties of sea ice using 360° camera radiance measurements

Raphaël Larouche, Bastian Raulier, Christian Katlein, Simon

Lambert-Girard, Simon Thibault, Marcel Babin

► To cite this version:

Raphaël Larouche, Bastian Raulier, Christian Katlein, Simon Lambert-Girard, Simon Thibault, et al.. Inferring inherent optical properties of sea ice using 360° camera radiance measurements. The Cryosphere, 2025, 19 (10), pp.4785 - 4804. 10.5194/tc-19-4785-2025. hal-05397380

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https://cnrs.hal.science/hal-05397380v1

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# Inferring inherent optical properties of sea ice using 360° camera radiance measurements

Raphaël Larouche<fnref n="1" />,<fnref n="2" />,*, Bastian Raulier<fnref n="1" />,<fnref n="3" />,*, Christian Katlein<fnref n="4" />, Simon Lambert-Girard<fnref n="1" />, Simon Thibault<fnref n="2" />, and Marcel Babin<fnref n="1" />

**Correspondence:** Raphaël Larouche (raphael.larouche@takuvik.ulaval.ca) and Bastian Raulier (bastian.raulier@takuvik.ulaval.ca)

Received: 4 December 2024 – Discussion started: 14 February 2025
Revised: 21 May 2025 – Accepted: 17 July 2025 – Published: 21 October 2025

**Abstract.** In this work, we demonstrate the utilization of a compact, consumer-grade 360° camera for measuring the in-ice spectral angular radiance distribution. This novel technique allows for the instantaneous acquisition of all radio-metric quantities at a given depth with a non-intrusive probe. This gives the opportunity to monitor the light field structure (mean cosines) from the atmosphere to the underlying ocean beneath ice. In this study, we report vertical profiles of the light field geometric distribution measured at two sites representative of distinct ice types: High Arctic multi-year ice and Chaleur Bay (Quebec, Canada) landfast first-year ice. We also propose a technique to empirically retrieve the depth-resolved inherent optical properties by matching simulated profiles of spectral irradiances calculated with the HydroLight radiative transfer model to the observed ones. As reported in other studies, the derived reduced scattering coefficients were high (641.57 m⁻<fnref n="1" />; 72.85 m⁻<fnref n="1" />) at the top (2 cm; 5 cm) for both sites (High Arctic; Chaleur Bay) and lower in the interior part of the ice (0.48–4.10 m⁻<fnref n="1" />; 0.021–7.79 m⁻<fnref n="1" />). Due to the inherent underdetermined nature of the inversion problem, we emphasize the importance of using a similarity parameter that considers both the absorption and the reduced scattering coefficients. Finally, we believe that this radiometric device, combined with the proposed inversion technique, will allow the research community to scale up the measurements of the inherent optical properties of different kinds of

sea ice, making it possible to take better account of terrain variability in radiative transfer models.

## 1 Introduction

The Arctic Ocean has undergone major transformations in the last few decades, with perennial sea ice largely being replaced by thinner first-year ice (Comiso, 2002; Maslanik et al., 2011; Stroeve et al., 2012; Tschudi et al., 2016) and a significant decrease in the ice extent being observed (Comiso et al., 2008; Serreze et al., 2007; Stroeve and Notz, 2018). These changes have critical impacts on the atmosphere–ice–ocean system, especially during the spring to summer melting season as sea ice is transformed in a highly inhomogeneous cover of snow, bare ice, melt ponds, and leads (Ehn et al., 2011; Frey et al., 2011; Horvat et al., 2020; Katlein et al., 2016; Perovich et al., 2002). Seasonal ice has a higher melt pond fraction (Eicken et al., 2004; Hunke et al., 2013; Li et al., 2020) and enables more solar shortwave radiation transmission and heat deposition through absorption (Nicolaus et al., 2012; Stroeve and Notz, 2018). This new energy partition warms sea ice and the underlying ocean, leading to accelerated ice melt through a positive feedback loop (Arndt and Nicolaus, 2014; Curry et al., 1995; Perovich et al., 2008), which impacts regional Arctic climates. Thinner ice cover

<footnote n="1">¹CNRS – Université Laval – Sorbonne Université – Takuvik International Research Laboratory, Québec City, Québec, Canada</footnote>

<footnote n="2">²Centre d'optique, photonique et laser (COPL), Université Laval, Québec, QC, Canada</footnote>

<footnote n="3">³CERVO Brain Research Center, Québec, G1J 2G3, Canada</footnote>

<footnote n="4">⁴Alfred-Wegener-Institut Helmholtz-Zentrum für Polar und Meeresforschung, Bremerhaven, Germany</footnote>

<footnote>★These authors contributed equally to this work.</footnote>
promotes significant under-ice phytoplankton blooms earlier in the season (Arrigo et al., 2012, 2014; Mundy et al., 2009).

The amount of shortwave radiation scattered or absorbed by a given medium is determined by its inherent optical properties (IOPs). These material properties determine the propagation of light throughout the medium, which in bulk are often described by apparent optical properties (AOPs) like albedo and transmittance (Light et al., 2003). The apparent optical properties of sea ice exhibit large seasonal and spatial variability due to the mosaic-like surface structure and variations in the physical properties of sea ice (Katlein et al., 2019; Matthes et al., 2020; Perovich et al., 1998). An improved understanding of the links between structural and optical properties is needed to predict the impacts of sea ice transformation on Arctic ecosystems. While surface AOPs such as albedo (Ehn et al., 2006; Grenfell and Perovich, 2004; Perovich et al., 2002) and bulk transmittance (Katlein et al., 2015; Nicolaus and Katlein, 2013; Perovich et al., 1993, 1998) have been well documented over the past decades, very few in situ optical measurements have been made inside sea ice. These additional measurements are needed to better constrain optical models. Vertical profiles of planar irradiances have been acquired with large probes – which are prone to self-shadowing – lowered into bore holes of 5–10 cm in diameter (Ehn et al., 2008b; Light et al., 2008, 2015; Xu et al., 2012a) or, alternatively, measured by digging holes from below the sea ice, which required a diver (Ehn et al., 2008a). Efforts to reduce perturbations on sea ice physical properties were made with the development of vertical arrangements of fiber optics (Wang et al., 2014) or photodiodes having their normal axis rotated by 90° relative to the zenith direction (Katlein et al., 2021) taking autonomous measurements of irradiance in refrozen holes. Acquiring either planar or scalar irradiance is not sufficient to fully describe the internal light field because both quantities average the angular distribution of radiance. These measurements lack information about the light field geometry within the ice. Measurements of radiance have previously been collected in sea ice using single-direction radiance meters with a 3 to 7° field of view (FOV) (Pegau and Zaneveld, 2000; Perovich et al., 1998; Xu et al., 2012a). This approach is, however, time consuming, as it requires drilling holes in different zenith directions and rotating the radiometer for azimuth measurements. This process results in a full radiance distribution but suffers from low angular resolution and high structural disturbance by the sampling method.

We present a solution to measure radiance using a camera assembly whose pixels instantaneously resolve angular radiance distributions over a large portion of 4π steradians. This enables the measurement of all radiometric quantities and, consequently, all AOPs that can be calculated from the angular radiance distribution (Mobley, 1994). Such instruments have been used to study radiative transfer in the ocean (Antoine et al., 2013; Smith et al., 1970; Voss and Chapin, 1992, 2005; Voss and Zibordi, 1989; Wei et al., 2012). The

smallest of these radiometers, which measured radiance, was packaged inside a 9.6 cm × 26 cm (diameter × length) case (Antoine et al., 2013), making it unsuitable for in-ice applications due to self-shadowing and the large hole that must be drilled. Recently, lens and imaging sensor miniaturization has led to the commercialization of compact fisheye cameras. One of them is the Insta360 ONE (see Fig. 1c; Insta360, Arashi Vision Inc.). After rigorous radiometric calibrations (Larouche et al., 2024b), this low-cost camera becomes an easy-to-use instrument for measuring radiance distributions within sea ice. This paper presents the first vertical profiles of the angular radiance distributions inside sea ice at high angular resolution. The measurements were acquired at two different field sites, one in the High Arctic close to the geographic North Pole and the other one in Chaleur Bay in the province of Quebec. First, we provide the vertical profile of the radiometric quantities for High Arctic multi-year sea ice and then some depth-resolved AOPs. For both field sites, we present IOPs inferred from the radiometric quantities calculated with the HydroLight (HL) radiative transfer simulation software that best fit the observed ones.

## 2 Materials and methods

### 2.1 Field measurements

High Arctic field measurements were made near the geographic North Pole on 31 August 2018 (89°25.21′N, 63°08.67′E) during the AO18 expedition with the research icebreaker *Oden* (see Fig. 1a and d). The multi-year (MY) sea ice at the site was 185 cm thick with a freeboard of 17 cm and covered by 2 cm of fresh, dry snow. Profiles of angular radiance distributions were collected inside a 5 cm diameter hole, snugly fitting the camera case. Measurements were made at a vertical resolution of 20 cm. At the measurements site, three RAMSES-ACC-VIS sensors (TriOS GmbH, Rastede, Germany) were positioned to acquire the downward, reflected, and transmitted (under sea ice) planar irradiance (Fig. 1c). Spectral irradiance was measured from 320–950 nm with increments of 3.3 nm (Katlein et al., 2021; Grosfeld et al., 2016; Nicolaus et al., 2010). As for the atmospheric conditions, the incoming radiation was highly diffuse (see Fig. 1a) because of the presence of low stratus clouds (often observed in the High Arctic) and the low sun elevation.

A second field campaign took place in Chaleur Bay, Quebec, Canada (48°06.47′N, 66°26.97′W) on 23 March 2022 (see Fig. 1b). This place is located in the Gulf of St. Lawrence, where seasonal sea ice forms during winter. The sampling site was chosen close to land. Four different holes were drilled at the site. The thickness of the ice varied from 72 to 80 cm, while the freeboard was within 15–20 cm. This unusually high freeboard is explained by the landfast ice attached to the coast, even at low tide. The surface of the ice was covered with very granular snow, indistinguishable from
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Field sampling of the depth-resolved angular radiance distribution inside sea ice using the commercial 360° camera Insta360 ONE. In (a), the image taken with the omnidirectional camera at the site near the North Pole in the High Arctic (89°25.21′N, 63°08.67′E) shows the sun near the horizon and a mostly overcast sky, which are conditions producing an incident light field close to isotropic. (b) Sampling site at Chaleur Bay (48°06.47′N, 66°26.97′W), Quebec, Canada, with completely different meteorological conditions (sunny day with very few clouds), where one can see the operator inserting inside sea ice a graduated stick with the camera attached to the tip. A schematic of the acquisition method is illustrated in (c), with the camera inserted in the drilled hole (5 cm in diameter) for image capture at multiple depths. In the High Arctic, RAMSES-ACC-VIS sensors were used to measure irradiance at the surface and below sea ice, while a C-OPS irradiance sensor was used (at the surface only) at Chaleur Bay. (d) Structural model associated with the different ice layers used for inversion of the optical properties.</figcaption></figure>

sun-transformed ice. To underline this ambiguity, this part of the ice is referred to as “surface slab”. Vertical profiles of the angular radiance distribution were acquired in each hole with depth increments of 5 cm. The Compact-Optical Profiling System (C-OPS; Biospherical Instruments Inc.) was used to continuously collect the downwelling irradiance (19 spectral bands between 380 and 875 nm; Morrow et al., 2010) as the surface reference during in-ice camera measurements. As seen from Fig. 1b, clear sky conditions prevailed at the sampling site, with a few passing clouds. The sun elevation at the time of measurements was between 40° and 43°. At both field locations, the experimenters paid particular attention to site selection in order to ensure the largest possible homogeneous area. No keels or under-ice variability was observed. Each profile measurement took approximately 10–15 min, depending on the vertical resolution and ice thickness.

## 2.2 Camera description and capture modes

The Insta360 ONE low-cost omnidirectional camera (see Fig. 1c) has a diameter of 5 cm, includes two fixed-aperture ($f# = 2.2$) fisheye lenses. The imaging detectors are two Sony CMOS sensors (1/2.3" format, 6.95 mm in diagonal) with a total of 3456 pixel × 3456 pixel (12 megapixels) covered in a repeated Bayer mosaic of three waveband filters (conventional RGB). The analog-to-digital converter (ADC) has a resolution of 14 bits (16 384 possible values). When the camera is in air, each imaging sensor can capture light from a hemispherical solid angle of $2\pi$ steradians. In water, this solid angle is reduced due to a decrease in the field of view along the optical axis of each lens, from 90° to 76° (Larouche et al., 2024b). In our measurement setup, the optical axes (aligned with the *zc* axis in the enlarged region of Fig. 1c) of both fisheye lenses are oriented 90° from the zenith. An important feature of this camera is the availability of raw image capture at the sensor level, allowing radiom-
etry utilization. The camera can be purchased with a water-
proof enclosure and thus can be used in wet environments
such as holes drilled in sea ice. With the camera attached to a
depth-graduated stick, the acquisition strategy was to start a
timer – set to the maximum value of 10 s – and then quickly
lower the camera to the desired location inside the drill hole
(see Fig. 1c). With the raw option activated, the images were
saved to a microSD card in Digital Negative (DNG) format
(developed by Adobe).

## 2.3 Image processing, radiometry, and optical properties

The image processing pipeline starts by performing demo-
saicring, where each spectral RGB component is downsam-
pled from the raw image. Then, after dark correction, we ap-
ply the measurement equation with the proper calibration pa-
rameters – considering in-air/in-water utilization – to trans-
form the digital numbers of each spectral *i* band into effective
spectral radiance values $\overline{L}_i$ [W sr$^{-1}$ m$^{-2}$ nm$^{-1}$]. For the dark
subtraction, an average of the unexposed part of the CMOS
sensor was used. The measurement equation and the calibra-
tion and characterization methodologies of the variables in-
volved in this formula are fully described in Larouche et al.
(2024b). Lastly, the pixel-wise $\overline{L}_i(\theta_c, \phi_c)$ – with $\theta_c, \phi_c$ being
the spherical coordinates with respect to the optics reference
systems – are re-mapped on a zenith ($\theta$) and azimuth ($\phi$) grid
of 1° in angular resolution.

The angular distribution of radiance carries a large amount
of information; the integration of the radiant energy in ev-
ery direction (over a hemispherical solid angle) weighted by
cos θ gives the equivalent planar irradiance. This radiometric
quantity is used for energy budget calculation influencing sea
ice mass balance (Ebert et al., 1995; Jin et al., 1994; Light
et al., 2015). Scalar irradiance is a more appropriate radio-
metric quantity for photosynthetically active organisms, as
they are equally sensitive to every photon direction (Morel,
1991). Both quantities can be calculated given:

$$
E(z, \lambda) = \int_{0}^{2\pi} \int_{\theta_1}^{\theta_2} f(z, \theta, \phi) \, d\theta \, d\phi, \quad (1)
$$

with $E(z\lambda)$ [W m$^{-2}$ nm$^{-1}$] being the irradiances at dif-
ferent depths z [cm]. For a planar irradiance of $E_d$ or
$E_u$, $f(z, \theta, \phi) = \bar{L}_i(z, \theta, \phi)|\cos\theta|\sin\theta$. With a scalar irra-
diance of $E_d^\circ$ or $E_u^\circ$, the integrand function is $f(z, \theta, \phi) =$
$\bar{L}_i(z, \theta, \phi)\sin\theta$. For the radiation coming from either the
downward or upward directions (d and u subscripts), the
zenithal integration boundaries are set to $[\theta_1, \theta_2] = [0, \pi/2]$
and $[\theta_1, \theta_2] = [\pi/2, \pi]$, respectively. We used the compos-
ite Simpson's rule for the numerical integrations in both di-
mensions $(\theta, \phi)$. However, before calculating the irradiances,
any missing radiance values – resulting from the in-water
reduced FOV – had to be extrapolated. This extrapolation

was accomplished through a fifth-degree Legendre polyno-
mial fit on the azimuthally averaged spectral radiance (see
Appendix A). The attenuation with depth of the planar irra-
diance traveling in the downward hemisphere is calculated
as:

$$
K_d(z', \lambda) = - \frac{2}{E_d(z_{k+1}) + E_d(z_k)} \cdot \frac{E_d(z_{k+1}) - E_d(z_k)}{z_{k+1} - z_k}, \quad (2)
$$

where $K_d(z', \lambda)$ [m$^{-1}$] denotes the downwelling diffuse at-
tenuation coefficients at the central depths $z' = 0.5 \cdot (z_{k+1} + z_k)$, midway between two consecutive discrete measure-
ments $z_k$ and $z_{k+1}$ [m]. This function gives the rate at which
light gets attenuated due to scattering and absorption. In ad-
dition to being determined by the IOPs, the $K_d(z', \lambda)$ profile
is also influenced by the structure of the ambient light field
determined by environmental conditions such as the position
of the sun in the sky. Therefore, it is an apparent optical prop-
erty.

The average cosine links together the planar and scalar ir-
radiances for the downward and upward light field according
to:

$$
\bar{\mu}_d(z, \lambda) = \frac{E_d(z, \lambda)}{E_d^\circ(z, \lambda)}, \quad \bar{\mu}_u = \frac{E_u(z, \lambda)}{E_u^\circ(z, \lambda)}, \qquad (3)
$$

where $\bar{\mu}_d$ and $\bar{\mu}_u$ are, respectively, the average cosines for downwelling and upwelling light fields. They are geometric indices of the radiance angular distribution, and their evolution is driven by the medium inherent optical properties. For $\mu_d$, values of 0.5 and 1.0 correspond to isotropic and completely downward light fluxes, respectively. The average cosine of the complete angular radiance distribution is calculated from the following relationship:

$$
\begin{align}
\bar{\mu}(z, \lambda) &= \langle \cos \theta \rangle, \nonumber \\
&= \frac{\int_0^{2\pi} \int_0^{\pi} \overline{L}_i(z, \theta, \phi) \cdot \cos \theta \cdot \sin \theta \, d\theta \, d\phi}{\int_0^{2\pi} \int_0^{\pi} \overline{L}_i(z, \theta, \phi) \cdot \sin \theta \, d\theta \, d\phi}, \tag{4} \\
&= \frac{E_d(z, \lambda) - E_u(z, \lambda)}{E_o(z, \lambda)}, \nonumber
\end{align}
$$

where $\bar{\mu}$ and $E_o$ [W m$^{-2}$ nm$^{-1}$] are, respectively, the average cosine and the scalar irradiance of the $4\pi$ steradian sphere. Apart from understanding how the geometry of the light field evolves with depth, paring the average cosine $\bar{\mu}(z, \lambda)$ with the net irradiance $E_{\text{net}}(z, \lambda) = E_d(z, \lambda) - E_u(z, \lambda)$ gives an estimation of the depth-resolved absorption coefficient $a(z\lambda)$ [m$^{-1}$] under conservation of energy (Mobley, 1994):

$$
a(z', \lambda) = - \frac{\bar{\mu}(z_{k+1}) + \bar{\mu}(z_k)}{2} \cdot \frac{\ln[E_{\text{net}}(z_{k+1}, \lambda)/E_{\text{net}}(z_k, \lambda)]}{z_{k+1} - z_k} \quad (5)
$$

This equation, also known as Gershun's law, resolves the
transport equation by explicitly assuming negligible contri-
butions from inelastic scattering or internal sources. It has
been used to infer absorption coefficients in natural waters with an uncertainty in the range of 21% (Voss, 1989).

The other inherent optical properties, i.e., the scattering coefficient $b(z)$ [$m^{-1}$] and the phase function $p(\theta)$ [$sr^{-1}$], cannot be calculated explicitly. However, they can be estimated by carefully matching radiative transfer simulation outputs to the measured radiometric quantities or apparent optical properties (Light et al., 2008; Mobley et al., 1998; Perovich, 1990; Xu et al., 2012b). The phase function describes the probability density that a photon gets redirected at a certain angle after a single scattering event. In sea ice (assuming negligible Rayleigh scattering), the scattering events occur mainly after a photon reaches an interface between ice and air or brine. This scattering probability in sea ice is assumed to be spectrally invariant, a correct simplification in the Mie regime when the scatterers are large and distant, as diffraction and interference effects are negligible (Grenfell, 1983). For radiative transfer simulations in sea ice, the one-term Henyey-Greenstein (HG) function (Henyey and Greenstein, 1941) is usually adopted to approximate the phase function (Light et al., 2003, 2004, 2008, 2015; Mobley et al., 1998; Petrich et al., 2012):

$$p_{HG}(\theta, g_{HG}) = \frac{1}{4\pi} \cdot \frac{1 - g_{HG}^2}{(1 + g_{HG}^2 - 2g_{HG} \cos\theta)^{\frac{3}{2}}}. \quad (6)$$

The asymmetry parameter (anisotropy coefficient) $g$ varies between 0 (isotropic) and 1 (complete forward) and is equal to:

$$g = \langle \cos \theta \rangle = 2\pi \int_{0}^{\pi} p(\theta) \cos \theta \sin \theta d\theta. \quad (7)$$

The one-term Henyey-Greenstein equation was proposed from observations of the scattering by interstellar matter and has only 1 degree of freedom. It fails to accurately model complex phase functions, the latter being physically determined by the shape, the size distributions, and the complex refractive index of the scatterers. In sea ice, where most of the layers have large scattering coefficients, the diffusion regime is often reached so that the detailed angular shape of the phase function becomes irrelevant and only its first moment – driving the front-to-back scattering ratio – has relevance for the radiative transfer (Jacques, 2013). The radiative transfer simulations presented in this work therefore use the Henyey-Greenstein one-term equation.

## 2.4 Inversion of inherent optical properties

There is a wide variety of models used to solve the radiative transfer equation (RTE) (Preisendorfer, 1965) in scattering media. In this study, we chose the HydroLight (Mobley, 1994) radiative transfer model, which solves the RTE using the invariant embedding technique. This model, with its Python interface (Raulier, 2023), takes as input any depth.

The two measurement sites were treated differently in HydroLight because they differ in many respects. For the High Arctic, we set a diffuse sky illumination to simulate the stratus cloud cover that day (see Fig. 1a). Several homogeneous layers were used for the ice structural model (see Fig. 1d). At the surface, a 2 cm snow layer on top of the ice was set, followed by a layer from the snow to the water level. Interior ice was divided into slabs of 40 cm, except for the first layer below the water level, which was set to 37 cm to include at least one point of comparison with the field measurements. The remaining 8 cm above the ice-seawater interface was considered the skeletal layer. In the High Arctic, the sea floor reflectance effect was neglected, the water column being simulated as infinitely deep. For the measurements taken under a clear sky in Chaleur Bay, the geographic coordinates of the sampling site and the UTC time of measurement were used to calculate the proper zenith angle of the sun within the model. For this site, we considered a 5 cm surface layer also followed by a layer down to the freeboard level. Inner ice layers were then determined by inspecting the profile of absorption coefficients calculated using Gershun's law and that of the diffuse attenuation coefficient. Then, to properly represent the shallow waters of the bay, the simulations were run with a water column beneath the ice of 5 m with a sea floor reflectance of 10%. This reflectance value is representative of coastal oceanic bottom reflectance of intertidal ecosystems near Chaleur Bay (Légaré et al., 2022). For both measurement sites, the downwelling spectral irradiance measurement at the surface was used as input in the simulations, while the shape of the radiance was calculated with HydroLight using atmospheric variables adapted to recreate the conditions observed in the field. The changes in refractive indexes in the snow ice-ocean system were not considered (Ehn et al., 2008a, b; Jin et al., 1994). This simpler treatment avoids the problem of enhanced downward irradiance (EDI) at the atmosphere-sea ice interface (Jiang et al., 2005). It also helps remove the ambiguity of choosing the position of the air-ice interface when there is a snow layer on top. These interfaces are simply treated as additional scattering events in our simulations. We acknowledge that these are assumptions and that more efforts would be required solely toward understanding how to properly model the interfaces and their refractive index in sea ice.

To constrain the optical properties, we decided to fix the absorption coefficients. In the case of the High Arctic, it is reasonable to assume that the ice itself is responsible for most of the absorption. This assumption was also made in the inversions reported by previous studies (Light et al., 2008, 2015). We used the absorption spectral coefficients reported by Grenfell and Perovich (1981). In the case of Chaleur Bay, we observed the presence of impurities in sea ice, likely microalgae on site. We therefore used the absorption coefficients calculated by Gershun's law (Eq. 5), taking the median of the values inside each layer. To further constrain the problem, we also fixed the $g$ parameter of the phase func-
tion. Below the surface layer, the HG function was given a constant high asymmetry parameter of 0.99 (highly forward peak scattering), which is in accordance with previous works on radiative transfer inside sea ice (Ehn et al., 2008b; Light et al., 2004; Maffione et al., 1998; Mobley et al., 1998; Petrich et al., 2012). Near the surface, we set g to 0.85 based on the literature (Light et al., 2004), as previous Mie calculations gave an asymmetry parameter in that vicinity from a larger volume fraction of air bubbles in the layer.

To infer the scattering coefficient profile of the two sites, we developed a recursive inversion algorithm using the Nelder-Mead downhill simplex algorithm (Nelder and Mead, 1965). The algorithm is as follows and is repeated N times until satisfactory results are obtained.

1. Repeat N times:

(a) For each layer in the profile:

(i) Run the Nelder-Mead algorithm, varying the scattering coefficient until the layer's measured and simulated reflectance are within 1% or until the maximum iteration number M is reached.

2. Run the Nelder-Mead algorithm, varying the entire profile of scattering coefficients, and stop when loss $L \le 0.01$ (see Eq. 8) or the maximum iteration number G is reached.

This algorithm comprises two distinct steps. Firstly, it recursively uses the discrete reflectance of each layer as an indicator; this apparent optical property approaches an intrinsic characteristic of the layer and is only slightly influenced by the others. This reduces the dimensionality of the problem in several small succinct steps, enabling the Nelder-Mead method to converge more rapidly. With the first stage having converged to the vicinity of the global minimum, the second stage proceeds to the minimization over all irradiance profiles. The loss $L$ [-] used at that second step is the sum of the relative error for the different irradiances ($E_d$, $E_u$, and $E_o$) for each spectral band $i$ and at discrete measurement depth $z_k$ [cm]:

$$L = \sum_{i=0}^{I} \sum_{k=0}^{K} \frac{|E_{d,i,HL}(z_k) - E_{d,i,Field}(z_k)|}{E_{d,i,HL}(z_k)} + \frac{|E_{u,i,HL}(z_k) - E_{u,i,Field}(z_k)|}{E_{u,i,HL}(z_k)} + \frac{|E_{o,i,HL}(z_k) - E_{o,i,Field}(z_k)|}{E_{o,i,HL}(z_k)} \quad (8)$$

where the subscripts “HL” and “Field” refer to the simulation and the measurement, respectively. This way of calculating the error enables fine-tuning of the inverted values to obtain the simulated irradiance profile closest to the measured one.

This procedure, although leading to profiles very close to those observed, does not necessarily recover the actual inherent optical properties of the ice. This procedure allows

finding only one of the solutions among a larger ensemble. This ensemble is defined as the sets of inherent optical properties leading to the same irradiance gradient. For example, we can imagine two different media: one very absorbent but with low scattering and another with little absorption but high scattering. Both would result in the same irradiance gradient. In 1980, van de Hulst introduced a useful equation, i.e., Eq. (9), to compare the triplet of IOPs that give rise to the same gradient (van de Hulst, 1980):

$$S = \sqrt{(1 - \omega_o)/(1 - \omega_o g)} = [1 + (b/a) \cdot (1 - g)]^{-1/2}, \quad (9)$$

where S is the similarity parameter and $\omega_o = b/(a+b)$ is the single scattering albedo. This invariant is the geometric mean of the two first moments of the diffusion pattern. Because the latter is valid for thick scattering slabs far from boundaries, it has already been used by Light and Ehn to compare a set of IOPs (Ehn et al., 2008b; Light et al., 2004). Recalling Eq. (9) with the reduced scattering coefficient, if the ratio $b'/a \gg 1$ (mainly the scattering medium), then the similarity parameter will tend toward 0. Conversely, a ratio $b'/a \ll 1$ (mainly the absorbing medium) means that S will get closer to unity. Also, one medium that scatters isotropically may be equivalent radiometrically to another one with a higher scattering coefficient and an asymmetry parameter closer to unity. This gives rise to:

$$b' = b(1 - g), \quad (10)$$

with $b'$ corresponding to the reduced scattering coefficient, another similarity parameter. This quantity is often used for comparisons of inverted IOPs in sea ice, as two regions with the same reduced scattering coefficients are identical in terms of scattering.

## 2.5 Inversion uncertainties

The inversion described in the previous section certainly leads to errors when inferring inherent optical parameters. Therefore, we developed an experiment to estimate the error level that percolates through the process. For that purpose, we used HydroLight to generate the radiometric quantities of a set of 50 different ice conditions following the structural model proposed in previous inversions (Light et al., 2008, 2015). In each case, the ice was 150 cm thick and consisted of a surface scattering layer (5 cm), a drained layer (27.5 cm), and an interior layer (117.5 cm). The conditions differed by their reduced scattering coefficient profile; for each of the layers, a random value was drawn from uniform distributions. The boundaries of these distributions were $b' \in [20, 150] m^{-1}$ ($g = 0.85$) for the surface scattering layer, $[2.4, 12] m^{-1}$ ($g = 0.99$) for the drained layer, $[0.5, 1.8] m^{-1}$ ($g = 0.99$) for the interior layer, and $[0.1, 1.0] m^{-1}$ ($g = 0.90$) for the seawater. These values correspond to the expected values for the various layers for first-year ice (Light et al., 2008, 2015). Then, the inversion algorithm was
used to infer the randomly drawn scattering coefficients for all the layers. To increase the complexity of the problem, the starting points for the inference were also sampled randomly. In this way, it was possible to check whether the distance between the desired start and end points had any impact on the error.

# 3 Results

## 3.1 Field measurements

### 3.1.1 High Arctic

Figure 2 shows the spectral angular radiance distributions as measured by the camera for the three spectral bands. In the first row (Fig. 2a-c), the spectral components of the geometric light field $\bar{L}(\theta, \phi)$ for a depth of 40 cm are shown, whereas the second row (Fig. 2d-f) displays normalized-radiance distributions vertically stacked for depths between 40 and 160 cm with increments of 40 cm. These polar graphs show the radiance angular distribution in spherical coordinates. The azimuth angle corresponds to a fixed reference on the camera, with lens #1 arbitrarily set at 90° and lens #2 at 270° when transforming raw images into radiance. However, as the radiative field in sea ice is considered to be homogeneous as a function of the azimuth angle, the camera is not positioned in exactly the same way for each measurement. This could have an impact for ice with very low scattering or under a melt pool, but not in the two cases studied. The zenith angle indicates where radiance comes from relative to the vertical axis. It varies from 0 (center of graph, downward direction) to 160° (outer ring, upward direction) and indicates the elevation of the energy direction, where 0° indicates a downward direction (toward the ocean) and 180° would indicate a perfectly upward direction (toward the atmosphere). The top panels (a, b, and c) show radiance with the same color scale for the three channels centered at 480, 540, and 600 nm. The signal is predominantly blue in the downward direction (center of graph), followed by green and red. In the bottom panels (d, e, and f), radiance is normalized to each depth, allowing us to better appreciate how its shape changes with depth. At higher elevations, the signal is much more homogeneous, whereas deeper within the sea ice, the angular distribution of the radiance becomes increasingly downward. This effect is further accentuated at longer wavelengths, as the loss (from top to bottom) of the radiation traveling upward ($\theta > 90°$) is larger compared to the removal of photons below 90°. From 60 to 200 cm, $\bar{\mu}_d$ varies between 0.521 and 0.555 at 540 nm and between 0.531 and 0.562 at 600 nm (see full curves of Fig. 3d). For the upwelling light field, $\bar{\mu}_u$ varies from 0.483 to 0.336 (540 nm) and 0.482 to 0.333 (600 nm) for the same depth interval (60–200 cm). As an average cosine for the upwelling photons of 1.0 represents completely straight-upward light fluxes ($\theta = 180°$), the reduction of $\bar{\mu}_u$

with depth suggests that we gradually find less radiation at larger zenith angles. Another apparent optical property, the diffuse attenuation coefficient, gives insights on the presence of different layers in the ice column. Figure 3h shows these coefficients for the downward light field. The bulk diffuse attenuation coefficients for the total ice thickness are $1.35 \text{ m}^{-1}$ (480 nm), $1.37 \text{ m}^{-1}$ (540 nm), and $1.58 \text{ m}^{-1}$ (600 nm). At the top (0–20 cm), the average $K_d$ values are $4.24 \text{ m}^{-1}$ (480 nm), $4.39 \text{ m}^{-1}$ (540 nm), and $4.84 \text{ m}^{-1}$ (600 nm). Just below, an intermediate layer sits from 20 to 80 cm, with average values of $1.69, 1.75$, and $2.08 \text{ m}^{-1}$ for the blue, green, and red spectral bands, while between 80 and 180 cm, these $K_d$ decrease to averages of 0.56, 0.54, and $0.63 \text{ m}^{-1}$ (see Fig. 3h).

Figure 3a-c present the planar downward, planar upward, and total scalar irradiances used for IOP inversion. We observe that the simulated irradiances closely align with the measured ones. Notably, the average relative differences are 10.3%, 7.2%, and 6.3% for the planar downwelling, planar upwelling, and scalar total light fields, respectively. The relative differences spectrally averaged are all below 15% at depths between 20 and 200 cm for $E_d, E_u$, and $E_o$. Indeed, higher discrepancies are found as we get closer to the upper and lower boundaries. This is particularly obvious when looking at average cosines $\le 20.0$ cm for the upwelling (Fig. 3e) and total (Fig. 3f) radiance distributions, where surface hole effects seem to have perturbed the measurements (displayed as full lines). Table 1 shows the set of IOP profiles associated with the modeled irradiances. The absorption coefficient used in the simulations (full line) and the one calculated with Gershun's law (Eq. 5) are displayed in Fig. 3i. The $a(z)$ isolated from Gershun's law is not constant as a function of depth, unlike the pure ice values used for the HydroLight simulations at that site. At the top, this may be due to surface effects that contaminated our measurements. Some of the Gershun's law-inferred absorption coefficients are negatives (80–100 cm). These are caused by the increase in the net irradiances between the two measurements at 80 and 100 cm (see Fig. 3g). We decided not to show them in Fig. 3i because they likely derive from large observational uncertainties. Nonetheless, we interestingly noticed – by taking the depth median of all the Gershun's law in-ice values – that the absorption coefficients of 0.061, 0.054, and $0.138 \text{ m}^{-1}$ for the blue, green, and red channels are very close to those for pure bubble free ice of 0.043, 0.0683, and $0.12 \text{ m}^{-1}$ (Grenfell and Perovich, 1981). Table 1 gives the fitted scattering properties, where we clearly observe distinctions between the layers. At the surface, the modeled snow layer has the largest $b'$ of 641.57 $\text{m}^{-1}$. Just below, the reduced scattering coefficient drops significantly to $4.10 \text{ m}^{-1}$ and remains roughly constant in the two subsequent layers, with $b'$ being 3.96 and $2.92 \text{ m}^{-1}$ (20–57 and 57–97 cm). The inferred coefficient then decreases to 0.98 and $0.48 \text{ m}^{-1}$ (97–137 and 137–177 cm). Lastly, we note an increase in scattering in the last layer to $2.77 \text{ m}^{-1}$ (177–185 cm).
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Angular spectral radiance distribution [W sr⁻¹ m⁻² nm⁻¹] as measured by the camera in the High Arctic (AO2018 expedition). The top row (a–c) displays the light fields at 40 cm depth for the blue (480 nm), green (540 nm), and red (600 nm) bands, respectively. The zenith coordinates θ correspond to the radial circular lines, while the azimuth φ corresponds to the angular lines. The white regions are the missing values over the 4π steradian sphere due to the field of view being reduced to 76° in water. The second row presents the radiance distributions at various depths (40, 80, 120, and 160 cm) but normalized to their respective maximum. The blue (480 nm), green (540 nm), and red (600 nm) bands are shown respectively in (d–f).</figcaption></figure>

<table><caption>**Table 1.** Inherent optical properties (IOPs) inverted from HydroLight and depth-resolved angular radiance distributions measured in the High Arctic (AO2018 expedition). The absorption coefficients *a* [m⁻¹], the scattering coefficients *b* [m⁻¹], the anisotropy coefficient of the phase function *g*, and the reduced scattering coefficient *b*(1 − *g*) are given for an ice slab split into one drained layer (DL) above the freeboard and five layers below it. The layers are made of old interior ice (OII), young interior ice (YII), and the skeletal layer (SL). The dimensionless similarity parameters *S* for all the IOPs combined, calculated from Eq. (9), are also given.</caption><thead><tr><th rowspan="3">Layers</th><th rowspan="3">Depths [cm]</th><th colspan="9">High Arctic</th></tr><tr><th colspan="3">a [m<sup>-1</sup>]</th><th rowspan="2">b<br>[m<sup>-1</sup>]</th><th rowspan="2">g<br>[m<sup>-1</sup>]</th><th colspan="3">S</th></tr><tr><th>480 nm</th><th>540 nm</th><th>600 nm</th><th>480 nm</th><th>540 nm</th><th>600 nm</th></tr></thead><tbody><tr><td>Snow</td><td>0–2</td><td>0.043</td><td>0.0683</td><td>0.12</td><td>4277.1</td><td>0.85</td><td>641.57</td><td>0.008</td><td>0.010</td><td>0.014</td></tr><tr><td>DL</td><td>2–20</td><td>0.043</td><td>0.0683</td><td>0.12</td><td>410.44</td><td>0.99</td><td>4.10</td><td>0.102</td><td>0.128</td><td>0.169</td></tr><tr><td>OII</td><td>20–57</td><td>0.043</td><td>0.0683</td><td>0.12</td><td>396.73</td><td>0.99</td><td>3.96</td><td>0.104</td><td>0.130</td><td>0.171</td></tr><tr><td>OII</td><td>57–97</td><td>0.043</td><td>0.0683</td><td>0.12</td><td>291.53</td><td>0.99</td><td>2.92</td><td>0.121</td><td>0.151</td><td>0.199</td></tr><tr><td>YII</td><td>97–137</td><td>0.043</td><td>0.0683</td><td>0.12</td><td>97.90</td><td>0.99</td><td>0.98</td><td>0.205</td><td>0.255</td><td>0.330</td></tr><tr><td>YII</td><td>137–177</td><td>0.043</td><td>0.0683</td><td>0.12</td><td>48.40</td><td>0.99</td><td>0.48</td><td>0.286</td><td>0.352</td><td>0.446</td></tr><tr><td>SL</td><td>177–185</td><td>0.043</td><td>0.0683</td><td>0.12</td><td>276.52</td><td>0.99</td><td>2.77</td><td>0.124</td><td>0.156</td><td>0.204</td></tr><tr><td>Seawater</td><td>185–seafloor</td><td>0.0475</td><td>0.050</td><td>0.12</td><td>0.89</td><td>0.90</td><td>0.09</td><td>0.571</td><td>0.660</td><td>0.758</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Figure 3. Irradiance measurements at the High Arctic site: vertical profiles of (a) downward planar irradiance, (b) upward planar irradiance, and (c) scalar irradiance. The second row shows the average cosines for, respectively, the downward, the upward, and the complete radiance values angularly defined (from left to right). In the last row, we see the net irradiance as a function of the depth in sea ice (g), the diffuse attenuation coefficient for the downwelling irradiance, K<sub>d</sub> (in m<sup>-1</sup>) (h), and the Gershun’s law-derived absorption coefficient (i). For each subfigure, the three spectral band curves are displayed according to their color. The broken lines are the measurement results, whereas the solid ones are the RT simulation outputs.</figcaption></figure>

The first row of Fig. 4 shows the azimuthally averaged radiance measured and modeled at multiple depths inside the sea ice. Distributions at the surface are not shown, as they were too greatly affected by hole effects and operator shadow. The angular radiance distributions are also not displayed at 180 and 200 cm depths because of uncertain camera vertical positioning. The left to right columns (Fig. 4a–c) correspond respectively to the 480, 540, and 600 nm spectral

bands. For comparisons of the simulated and measured angular radiance distribution, we calculate statistical metrics such as the mean unbiased percent difference (MUPD [%]):

$$MUPD_i = 200 \cdot \frac{1}{K} \frac{1}{N} \cdot \sum_{k=1}^{K} \sum_{n=1}^{N} \frac{[\overline{L}_{i,HL}(z_k, \theta_n) - \overline{L}_{i,Field}(z_k, \theta_n)]}{[\overline{L}_{i,HL}(z_k, \theta_n) + \overline{L}_{i,Field}(z_k, \theta_n)]}$$
and the root-mean-square error (RMSE [%]):

$$RMSE_i = 100$$

$$ \cdot \sqrt{\frac{1}{K} \frac{1}{N} \cdot \sum_{k=1}^{K} \sum_{n=1}^{N} \left[ \frac{\overline{L}_{i,HL}(z_k, \theta_n) - \overline{L}_{i,Field}(z_k, \theta_n)}{\overline{L}_{i,HL}(z_k, \theta_n)} \right]^2} \quad (12) $$

with $\overline{L}_{i,HL}(z_k, \theta_n)$ being the HydroLight azimuthally averaged radiance, $\overline{L}_{i,Field}(z_k, \theta_n)$ representing the field measurements at each discrete depth $z_k$ [cm], and $\theta_n$ being the zenith angles between 0° and 180°. The average errors, given from Eq. (11) for the MUPD and Eq. (12) for the RMSE, are respectively 3.31 % and 10.33 % (blue channel), -3.90 % and 9.11 % (green channel), and -0.80 % and 10.08 % (red channel). At depths $\le 60$ cm, there are important differences between the measured and simulated angular radiance distributions near both angular extremities (20° and 160°). As we progress deeper inside the ice slab, these extremity errors seem to reduce, as does the error curves at all zenith angles compared to their lower-depth counterparts.

### 3.1.2 Chaleur Bay

One of the vertical profiles of spectral radiance captured at Chaleur Bay is displayed in the second row of Fig. 4. We show only the light field below the freeboard, as the measurements above were particularly contaminated by the hole's effect on the light field. Radiances obtained with HydroLight RT simulations (see Sect. 2.4 for the procedure) are presented in Fig. 4d-f as plain lines for each depth of measurement. Table 2 shows the inherent optical properties inferred for the ice geometry. We inverted a 5 cm thick scattering surface layer with a $b'$ of 72.85 m$^{-1}$, which decreases significantly to 5.55 m$^{-1}$ in the region above the freeboard. Next, there are two successive layers of interior ice (18-35 and 35-65 cm) with similar $b'$ values of 5.59 and 7.79 m$^{-1}$, respectively. These values for the interior ice are larger than what was observed in the High Arctic. The last layer (before the water column) has a reduced scattering coefficient of 0.021 m$^{-1}$. Apart from large scattering coefficients for the interior ice, we also inferred high absorption coefficients from Gershun's law at that site. Above 35 cm, the coefficients are all higher than 1.80 m$^{-1}$. Spectrally, the absorption is larger at 600 nm than at 540 nm for almost all the layers (except for the skeletal layer), while the blue band coefficients surpass those in the red band in every layer. The high absorption of the medium is also reflected in the $K_d$ coefficients, which are higher than those of Arctic sea ice. The diffuse planar downwelling attenuation coefficients are shown in the Supplement, along with all the same quantities as the ones presented in Fig. 4 (see Figs. S1 and S2). The agreement between the RT simulated and measured zenith radiances, quantified using the MUPD, are of -22.49 %, -11.26 %, and -15.62 %, respectively, for the 480, 540, and 600 nm channels. For the root-mean-square errors, which give a better

idea of the residuals, we obtained 115.51 %, 91.48 %, and 106.86 % for the same spectral bands. These discrepancies are larger than for the High Arctic inversion and may be caused by a larger impact of the hole and self-shading on the measurements. As seen in the second row of Fig. 4, greater errors appear at low and large zenith angles, i.e., regions in the radiance data prone to the impacts of the drilling hole and the shading of the stick we inserted into it. In addition, uncertainties associated with the ice thickness measurements taken during the fieldwork, as well as those related to the ice-ocean interface position in the model, may account for the notable differences at 70 cm. This is because the interface between the highly scattering sea ice and the ocean causes a large change in the shape of the radiance. Inaccurate positioning of the interface in the simulation will therefore lead to large differences in simulated and measured radiances. It should also be mentioned that the inherent optical properties inversion algorithm is based on global error minimization, i.e., it achieves a compromise where a greater error at a given depth may give rise to a smaller one in another region.

## 3.2 Inversion errors

To evaluate any systematic error induced by the inversion procedure presented in Sect. 2.4, we generated a set of 50 artificial in-ice irradiance profiles from realistic depth-dependent IOPs. The algorithm was then used to invert the optical properties or the different irradiance profiles using the following iteration numbers: $N = 5$, $M = 8$, and $G = 150$ (see Sect. 2.4). These iterations numbers were determined empirically to reduce the error while limiting the computation time required to perform an inversion. The error for each layer is shown in Fig. 5, comparing the reduced diffusion coefficients found with the references. The mean absolute errors for the inversion of the surface scattering layer, drained layer, interior layer, and seawater coefficients are 3.55 %, 12.23 %, 2.62 %, and 3.78 %, respectively. During the inversions, we observed that the algorithm converged toward the desired values, even if the starting values were far from the references.

# 4 Discussion

## 4.1 Field measurements

Although we set several layers for each site (see Sect. 2.4), it is interesting to note that subsequent layers merged into larger regions of constant scattering properties, suggesting zones of similar microstructure. The first 2 cm of pack ice is a special case, as it is made up of snow, the most scattering element, with the highest coefficient, i.e., $b' = 641.47$ m$^{-1}$. Just below sits the drained layer (DL), constrained by the freeboard position. This layer is also known to cause significant scattering as the inclusions are drained and the ice becomes more porous. The value of the reduced scattering co-
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Depth-resolved spectral angular radiance distributions azimuthally averaged. The first row represents the High Arctic radiance for the (a) 480, (b) 540, and (c) 600 nm spectral channels, while the second row shows the same bands (in order from left to right) but for the Chaleur Bay site. The broken lines are the camera measurements, and the full lines are the radiance data modeled with HydroLight (both at 1° angular resolution).</figcaption></figure>

<table><caption>**Table 2.** Ice inherent optical properties obtained by fitting the HydroLight radiative transfer simulations to the radiance measurements at the Chaleur Bay site (on 23 March 2022). The table provides the absorption coefficients *a* [m⁻¹], the scattering coefficients *b* [m⁻¹], the anisotropy coefficient of the phase function *g*, the reduced scattering coefficient *b*(1 − *g*), and the similarity parameter *S* for all the layers. Below the surface slab sits the drained layer (DL), followed by the interior ice (II) and the skeletal layer (SL).</caption><thead><tr><th rowspan="3">Layers</th><th rowspan="3">Depths [cm]</th><th colspan="10">Chaleur Bay</th></tr><tr><th colspan="3">a [m<sup>-1</sup>]</th><th rowspan="2">b<br>[m<sup>-1</sup>]</th><th rowspan="2">g<br>[m<sup>-1</sup>]</th><th rowspan="2">b(1-g)<br>[m<sup>-1</sup>]</th><th colspan="3">S</th></tr><tr><th>480 nm</th><th>540 nm</th><th>600 nm</th><th>480 nm</th><th>540 nm</th><th>600 nm</th></tr></thead><tbody><tr><td>Surface</td><td>0–5</td><td>2.20</td><td>1.97</td><td>2.12</td><td>484.36</td><td>0.85</td><td>72.85</td><td>0.171</td><td>0.162</td><td>0.169</td></tr><tr><td>DL</td><td>5–18</td><td>2.20</td><td>1.97</td><td>2.12</td><td>555.05</td><td>0.99</td><td>5.55</td><td>0.533</td><td>0.512</td><td>0.527</td></tr><tr><td>II</td><td>18–35</td><td>2.11</td><td>1.80</td><td>2.02</td><td>558.86</td><td>0.99</td><td>5.59</td><td>0.523</td><td>0.494</td><td>0.515</td></tr><tr><td>II</td><td>35–65</td><td>0.87</td><td>0.65</td><td>0.77</td><td>779.14</td><td>0.99</td><td>7.79</td><td>0.317</td><td>0.277</td><td>0.300</td></tr><tr><td>SL</td><td>65–72</td><td>0.44</td><td>0.34</td><td>0.32</td><td>2.06</td><td>0.99</td><td>0.021</td><td>0.977</td><td>0.971</td><td>0.969</td></tr><tr><td>Seawater</td><td>72–seafloor</td><td>0.22</td><td>0.11</td><td>0.18</td><td>0.87</td><td>0.90</td><td>0.09</td><td>0.847</td><td>0.748</td><td>0.821</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Estimation of the errors made by the inherent optical properties (IOPs) inversion algorithm. The figure links the reference reduced scattering coefficients with ones estimated by the algorithm for the three-layer ice model composed of a surface scattering layer, a drained layer, and an interior ice layer.</figcaption></figure>

efficient for this layer is 4.10 m⁻¹, which falls within the previously reported range of 2.14–12.0 m⁻¹ (Light et al., 2008). The two subsequent slabs (20–57 cm, 57–97 cm) below the freeboard also have considerable inverted *b'* values of 3.96 and 2.91 m⁻¹. These values are larger than those inferred for multi-year interior ice during the summer of 1998 in the Beaufort Sea, which range between 0.5 and 1.8 m⁻¹ (Light et al., 2008). They, however, fall in the gaps of larger *b'* of 2.1–4.4 m⁻¹ (between 6 and 76 cm) and 2.8–7.1 m⁻¹ (between 10 and 100 cm) measured respectively for snow-covered and bare first-year interior ice (Perron et al., 2021). Below 97 cm, we observe *b'* inside the Light et al. (2008) interior ice (II) interval, as our values are within 0.48–0.98 m⁻¹. In the last ice layer (177–185 cm), we notice an increase in the scattering coefficient. This is probably the skeletal layer, formed by the advective exchange between ocean and ice. This leads us to assume that the ice at that High Arctic site was probably composed of two types of more translucent interior ice: an old interior ice (OII) from 20 to 97 cm and a younger interior ice (YII) from 97 to 185 cm. MY ice is known to have large variations in its optical properties and in boundaries of its different layers due to multiple melt and growth cycles (Pegau and Zaneveld, 2000). This likely means that the YII was recently formed, while the OII layer was shaped during previous summers, giving time for brine pockets to be drained over one or multiple melt seasons (Perovich et al., 2002). It would indeed have been very interesting to explain the number of seasons the ice had survived. However, this would have required crystallographic investigations accompanied by oxygen isotope analyses, which could be the sub-

ject of a future study. Higher scattering in the upper half of the ice column was also evident in optical observations and investigations conducted a few hundred meters away on the same ice floe (Katiein et al., 2021). From this last study, a four-layer model using the DORT 2002 RT model (Edström, 2005) helped in inverting reduced scattering coefficients of 25 m⁻¹ for the surface scattering layer and 2.5 m⁻¹ for the interior ice. This latter *b'* falls near the center value of 2.22 m⁻¹ of our inversion for interior ice (0.48 m⁻¹, 3.96 m⁻¹).

For the absorption coefficients of High Arctic MY sea ice derived through Gershun's law (Fig. 3i), the depth median absorption coefficients of 0.061, 0.054, and 0.138 m⁻¹ are close to those for pure bubble-free ice of 0.043, 0.0683, and 0.12 m⁻¹ for the 480, 540, and 600 nm channels, respectively. This would suggest that using the pure ice assumption would be valid, particularly in this case, as it is reasonable to assume that sediment is unlikely to be found in multi-annual sea ice cores near the North Pole. The larger median coefficient in the blue compared to the green spectral band may be surprising but agrees with previously reported values found in landfast sea ice (Ehn et al., 2008b). In the upper region ranging to 80 cm, we notice larger *a* in the green band compared to the 480 nm channel, while for depths ≥ 100 cm, the blue absorption coefficients surpass those in the green (see Fig. 3i). This is apparent also in the color of the ice of Fig. 6b – extracted from jpeg images saved along with the raw ones – which displays blue colors becoming greener at 100 cm. The increase in *a* for shorter wavelengths could be caused by algal or non-algal particles (AP and NAP) that may be larger in concentrations as we progress toward the ice-ocean interface (Ehn et al., 2008b). The larger surface values are hard to explain physically, as the presence of air in the snow should slightly reduce the absorption coefficient, *a*. We think that this may be due to hole effects, namely, a bright one at the surface, increasing downwelling irradiance, and a dark spot down under, decreasing the upwelling irradiance. Regarding the negative inverted absorption coefficients caused by the increase in net irradiance (see Fig. 3g), we believe that measurements artifacts may be responsible. They include possible angular misalignment of the optics inside the hole or surface effects such as the shadow of the operator near the hole or snow displacement. The average cosine $\bar{\mu}$ has the same spectral trends as the measured absorption coefficients, as they are related through Gershun's law, presented in Eq. (5). The increase in $\bar{\mu}$ with depth starting from 60 cm (see Fig. 3f) reflects a higher proportion of light rays that vanished at large angles the deeper we were in the ice. This is explained by being closer to the ice-ocean interface, where there are less photons going upward due to the significantly less scattering in the last ice layers and in the ocean. This is also why the gradient with depth for $\bar{\mu}_u$ is larger than $\bar{\mu}_d$, as the light rays in the downwelling field are less affected by the proximity of the ice-ocean interface. No in-ice average cosine measurements were found in the literature, but some reported $\bar{\mu}_d$ just below the sea ice bottom ranging from 0.59
to 0.70 based on direct observations (Katiein et al., 2014; Massicotte et al., 2018; Matthes et al., 2019) or models (Arigo et al., 1991; Ehn and Mundy, 2013). Downwelling average cosines measured by Matthes et al. (2019) show spectral $\bar{\mu}_d \le 0.59$ 3.0 m below the bottom for ice covered with snow. These measurements reported under ice are consistent with the one we report here at 15 cm under ice, with 0.556, 0.555, and 0.562 for the 480, 540, and 600 nm bands, respectively. Also, measurements of the mean cosine in the part of the ice where several organisms live could have an impact on calculations of primary production in the ice. Primary production models could gain in accuracy if they included the value of $\bar{\mu}$ in their calculations. Algae, like the radiometric tool used in this study, are sensitive to the entire radiative field, whether it comes from above or below.

At the Chaleur Bay site (72 cm ice thickness), the first layer, which includes the granular layer, has the largest reduced coefficients $b'$ of 72.85 m⁻¹, which is significantly smaller than what was observed in the High Arctic for the same layer. The absence of snow at the site may explain this lower value. The two interior ice layers (18–35 cm, 35–65 cm) have $b'$ values of 5.59 and 7.79 m⁻¹, respectively. Those layers scattered in the same range of the layer above the freeboard ($b' = 5.55$ m⁻¹), and their values are slightly higher than past observations in interior ice (and those presented in this work in the High Arctic). From our discussions with a local ice fisher man, we understood that heavy and multiple snowfalls during winter had come to melt and refreeze, forming layers of coarse ice grains. These are snow ice layers (Ehn et al., 2008b) and would explain the larger scattering coefficients inverted in the zones below the freeboard. Non-constant absorption coefficients with depth allowed good agreements between the simulations and the radiometric observations. The ice color for each depth displayed in Fig. 7c also supports variation of the coefficients with depth, as the RGB vary greatly depending on the position within the slab. The inferred *a* ranges from 0.32 to 2.20 m⁻¹ across the entire ice column. Above water level (18 cm), the absorption coefficients stay constant in both simulated layers (0–5 cm, 5–18 cm) at 2.20, 1.97, and 2.12 m⁻¹ for, respectively, the blue, green, and red bands. Although being quite large, we think that these values are plausible, as some studies reported absorption coefficients over 1.0 m⁻¹ in the visible spectrum inside sea ice of Liaodong Bay, a region surrounded by some industrial, agricultural, and residential zones (Xu et al., 2012b). In the first layer below the freeboard, the absorption coefficients decrease slightly, and then they decrease more rapidly in the last two layers up to *a* in the range of 0.32–0.44 m⁻¹. These latter inverted coefficients are higher than what was reported for pure interior ice. This informs us that the ice at Chaleur Bay may have contained a higher concentration of algal and non-algal particles than the Arctic site. The higher absorption coefficients in the blue band compared to the green channel (see Table 2) are

consistent with Fig. 6c, which shows greener colors compared to those observed for the High Arctic (Fig. 6b).

For highly scattering media such as the ice of the studied sites, the inverted IOPs are only true under the similarity principle, as two different sets of IOPs with the same *S* give similar light fields. Following Eq. (9), if the ratio $b'/a$ (or $b/a$ given constant asymmetry parameter) increases, the similarity parameter decreases. In other words, the higher the value of *S*, the greater the energy loss in the layer concerned due to absorption. Figure 6a shows the similarity parameter profiles for the two field campaigns at the three measured wavelengths. The background shows the similarity parameters (540 nm) as calculated using measurements found in the literature. Firstly, this visualization effectively summarizes the variation within the profile of the optical properties of High Arctic ice. Indeed, low values of *S* can be seen at the top of the profile, reflecting the dominance of scattering in this region. Throughout the profile, the value of the similarity parameter increases. Indeed, scattering decreases and absorption increases in the lower layers. Spectrally, we can see that the value of *S* is greater for red than for blue, which is to be expected because ice absorbs the blue part of the visible spectrum to a minimum. In short, the profile of the similarity parameter for measurements in the High Arctic shows that the ice there appears to be rather pure and that scattering loses its importance significantly as we move down the profile. In the Chaleur Bay data analysis, the case is less than that of pure textbook sea ice. In this case, there are two distinct zones: the upper zone, where absorption dominates, and the lower zone, where the value of *S* decreases. This behavior can be explained by the phenomenon of ice formation at this point. Indeed, discussions with residents have confirmed that pack ice in this area forms in two stages. First, during the first sustained subzero temperatures, a layer of ice about 30 cm thick forms. This would appear to be the layer with the lowest *S* values. As the winter season progresses, heavy snow accumulations weigh on the pack ice, leading to the formation of snow ice. Presumably, when this snow is flooded and frozen, it has already accumulated atmospheric deposits of dust. This would explain the second part, where the *S* values are lower. Spectrally, *S* values are arranged differently for the High Arctic. In fact, the *S* value for blue is greater than that for green and red, reflecting greater absorption in the shorter wavelengths of the visible spectrum. One possible explanation for this spectral behavior could be the presence of chlorophyll, which absorbs most of the blue part of the spectrum, as well as a little of the red, leaving out the green. This would explain why the value of the similarity parameter is highest for blue, then red, and finally green. Figure 6b shows that, as described above, the ice has a bluish hue, as would be expected for ice from the High Arctic. Figure 6c, on the other hand, shows a grayish hue that might be expected from dirty snow, followed by an increasingly greenish tinge, as would be expected from the presence of large numbers of photosynthetic organisms. In the future, analysis of particu-
<figure><img src="image_9.png" /><figcaption>**Figure 6.** (a) Summary of the similarity parameter *S* inverted from measurements at both the High Arctic (solid lines) and Chaleur Bay (dashed lines) sites, and those reported in previous studies for different types of sea ice. The background rectangles represent past measurements (Ehn et al., 2008b; Light et al., 2008; Perron et al., 2021), whereas the curves are the depth-dependent similarity parameters in each spectral band and for both sites. To calculate the similarity parameters of Light et al. (2008) and Perron et al. (2021), we used the absorption coefficient of pure ice at 540 nm (Grenfell and Perovich, 1981). The *S* values of Ehn et al. (2008) are provided in their paper at 500 nm. (b–c) RGB color of the High Arctic and Chaleur Bay ice respectively extracted from jpeg images saved along with DNG files (see Fig. S4 in the Supplement for an example of these rectilinear images). Obtaining these two 1D images involved: (1) taking the average pixel value inside a square at the center of the image, (2) normalization by the diffuse planar downwelling attenuation coefficient spectrally averaged, and (3) normalization by the maximum.</figcaption></figure>

late absorption and chlorophyll measurements could give rise to the possibility of confirming the insight we obtain from the mean color of the ice.

## 4.2 Errors analysis

Certain sources of uncertainties may have affected the IOP inversion from the set of measurements presented in this study. These include uncertainties due to the measuring device and its manipulation on-field, the source of errors due to the 3D variability in the ice structure itself, and the Hydro-Light inversion uncertainties. First, the absolute calibration of the radiance camera bears uncertainties, as discussed in detail in Larouche et al. (2024b). Second, potential manipulation errors that could have occurred during fieldwork are the following: misalignment of the optics inside the hole, leading to misaligned angular coordinates of the radiance values; the shadow of the manipulators affecting surface measurements; and unprecise depth determination, as sometimes the holding stick came up folded. Another instrument artifact affecting the geometric light field is the presence of the camera-stick assembly inside the ice (Picard et al., 2016). We did a brief investigation of self-shading effects, which revealed that radiance distributions are affected more strongly at small and large zenith angles. This likely explains why the simulated

and measured angular radiance distributions are more distant at the two extremities of the curves, as seen in Fig. 4. In addition, the solid nature of the ice leaves no choice but to dig a hole for internal measurements. From above or below the camera position, the hole creates a path for light to easily propagate and add to the fluxes that would be there without the opening. These hole effects increase when the camera is positioned near the boundaries, as the hole is seen inside a larger portion of the FOV. A second source of errors due to the ice itself is the large spatial variation in its structure (micro- and macroscale) over only a few meters (Frey et al., 2011; Pegau and Zaneveld, 2000; Perovich and Gow, 1996). The light field from a nearby region can affect the radiance at the position of interest. This is difficult to discern from our radiance measurements and cannot be simulated in HydroLight. To be able to characterize the horizontal variability effect, several measurements with the camera could be taken. For example, it would be interesting to perform a transect with the camera, gradually approaching a melt pond, and observe the distance at which the light field begins to depart from azimuthal homogeneity. Finally, there is also the anisotropic scattering coefficient that was neglected in this study – as it is poorly known and not configurable in the Hy-
droLight RT model – which might have permitted better IOP inversion and fitting of the field observations.

The scattering coefficient inversion algorithm performed well for the evaluation dataset. The error for the layers is around 3%, except for the drained layer, where the average error of the algorithm is 4 times larger at 12%. This error is probably due to the sensitivity of the loss function to scattering events occurring in this layer. Indeed, although scattering is less, this layer is smaller (27.5 cm) than the inner layer (117.5 cm) and experiences 10 times less scattering than the surface scattering layer. It is reasonable to assume that the weight of the inner and surface layers dominates the loss function because they have greater optical depth, which explains the lower performance for the drained layer. Nonetheless, the algorithm we developed was able to find the values we were looking for, even if they were very far from the starting values. This performance is due to the algorithm’s ability to explore the solution space and find the global minimum. This exploration of the space is made possible by the iterative process, where the search is repeated several times. The use of reflectance as a loss function in that first step also helps to explain the good algorithm performance. In fact, the reflectance of a layer is only slightly influenced by the other surrounding layers, making this measure more robust. Unlike reflectance, irradiance measured at a given location is highly dependent on the layers above and below it. This strong interlayer dependence makes inversion very difficult, as the loss for the different layers is interdependent. To sum up, using the recursive method with reflectance provides a good global exploration and enables us to find the vicinity of the global minimum. The second, more sensitive step enables fine convergence when the first step provides a profile very close to the desired solution.

On the relevance of angular radiance distribution measurements, it has been determined that we could successfully invert the IOPs having simultaneous measurements of $E_d$, $E_u$, and $E_o$. Ultimately, by making the observed and simulated irradiances match, we noticed correspondences when verifying radiance distributions. This is typical of the diffusion regime, where the zenithal radiances approach an asymptotic shape decaying with depth at the same rate as the irradiances (Preisendorfer, 1958). The usefulness of radiance over almost $4\pi$ steradians resides in the fact that we can collect all the possible irradiance quantities ($E_d$, $E_u$, $E_d^\circ$, $E_u^\circ$, and $E_o$) simultaneously from one capture of the 360° camera. To gather all these quantities from currently available radiometers at the same time would be a complicated task, as they are usually designed to measure only one type of irradiance. Packaging this into a single profiler would require large assemblies of tens of centimeters. In addition, as we would capture radiance closer to boundaries with a non-isotropic incident light field, in layers with small scattering coefficients, the azimuthal and zenithal angular shape of the light field would be of great interest. These conditions could happen in the marine environment, for instance, inside blue

ice of glacier crevasses and icebergs (Warren et al., 2019). For future studies, it would also be interesting to investigate in more detail the range of scattering coefficients for which the shape of the phase function has an impact on angular radiance distributions. This would lead to a better understanding of the need to increase the number of phase function moments in radiative transfer simulations in order to correctly model the observations. In these cases, the angular shape of the radiance would contain information on the phase function.

## 5 Summary

In this paper, we successfully demonstrate the utility of 360° cameras to study in-ice radiative transfer. Those compact optical systems enable capture of full radiance angular distributions at a fixed point in space and inside three spectral bands centered on 480, 540, and 600 nm. We would like to emphasize this new possibility to recover all the irradiance radiometric quantities ($E_d$, $E_u$, $E_d^\circ$, $E_u^\circ$, and $E_o$) from unique captures of this low-cost camera. Subsequently, from these irradiances, we were able to retrieve profiles of IOPs using an inversion algorithm that matched HydroLight-simulated radiometric quantities to those measured at two sites: the High Arctic and Chaleur Bay (Quebec). In the 1.85 m thick High Arctic MY ice, we inferred a reduced scattering coefficient of $641.57 \text{ m}^{-1}$ at the surface and three distinct regions of interior ice: older interior ice (OII) with $b'$ between 2.92 and $3.96 \text{ m}^{-1}$, younger interior ice (YII) with $b'$ within 0.48 and $0.98 \text{ m}^{-1}$, and a skeletal layer with a reduced scattering coefficient of $2.77 \text{ m}^{-1}$. Inside the seasonal Chaleur Bay interior ice, significantly higher light attenuation was assessed, due to both larger absorption, $0.32-2.11 \text{ m}^{-1}$, and more scattering, $0.021-7.79 \text{ m}^{-1}$, compared to the High Arctic site. Those results may be attributed to the presence of snow ice as well as absorbing particles (AP and NAP). The inversion problem faced in this work was greatly underdetermined. We had very little complementary information about the ice that forced assumptions for the absorption coefficients and the asymmetry parameter *g*. Combined with instrumental and manipulation errors on the field, the optimization algorithm – although being quite accurate for perfectly generated angular radiance distributions with relative errors between 2.62% and 12.23% – may converge toward erroneous scattering coefficients. This is why we prefer reporting the similarity parameter *S*, which is right for the observed radiometric quantities. Much more effort continues to be required in order to improve the inversion. For future fieldwork, we intend to use independent measurements of the absorption coefficients (total, algal, and non-algal particulate matters) from melted co-localized sea ice layers as well as in situ measurements of $b'$ with a diffuse reflectance probe (Perron et al., 2021). These measures could enable significant improvement of the inversion process. Measurements of the ice core temperature,
salinity, and birefringence image of the crystals could also be captured. This would give us a more complete story of the sea ice microstructure and allow better assessments of separated abg. Finally, two engineering challenges remain. Because each camera is currently calibrated individually, it would be valuable to purchase a batch (e.g., more than 10) of identical cameras to assess the variability introduced during manufacturing. If this variability proves to be low, it could reduce or even eliminate the need for systematic calibration, making the camera easier to use and enabling widespread adoption within the research community. Additionally, the camera must be removed from the hole between each measurement to allow time for image acquisition. Under normal conditions, it could be controlled remotely via a radiofrequency signal (Wi-Fi or Bluetooth) sent from a smartphone. However, the ice pack completely absorbs these frequencies. It would therefore be valuable to develop a solution that remains as compact as possible to control the camera from the surface. Such a system would accelerate data acquisition and minimize the risk of disturbing the environment.

This study has demonstrated the potential offered by the new 360° cameras available on the market. These cameras can replace the research prototypes that are very cumbersome to develop and potentially present lower performance. The commercial camera used here was, among those available on the market, one of the most suitable for our application. As the smartphone industry continues to strongly stimulate innovation in optical imaging, new camera models will undoubtedly become available in the future that are smaller, more sensitive, and perhaps more effective in their spectral resolution. Our study has opened the door to the use of these highly advanced commercial technologies for characterizing the optical properties of sea ice.

## Appendix A

Legendre polynomials form a set of infinite orthogonal functions with interesting mathematical properties. They were previously used to fit angular radiance distributions (Kattawar, 1975). These polynomials are also employed for the discretization of radiance in the numerical discrete-ordinate solution for the radiative transfer equation (Stamnes et al., 1988). They were naturally selected for the extrapolation of the unknown radiance values in water (camera below the ice freeboard). The spectral radiance $\bar{L}$ [W sr$^{-1}$ m$^{-2}$ nm$^{-1}$] at a given depth and wavelength is expressed as:

$$ \bar{L}(\mu) = c_0 + \sum_{l=1}^{l=5} c_l \cdot P_l(\mu), \\ P_l(\mu) = \begin{cases} \mu, & l=1 \\ \frac{1}{2}(3\mu^2 - 1), & l=2 \\ \frac{1}{2}(5\mu^3 - 3\mu), & l=3 \\ \frac{1}{8}(35\mu^4 - 30\mu^2 + 3), & l=4 \\ \frac{1}{8}(63\mu^5 - 70\mu^3 + 15\mu), & l=5 \end{cases} \quad (A1) $$

with $\mu = \cos\theta$ and $P_l$ and $c_l$ being, respectively, the Legendre polynomials and their corresponding coefficient of degree $l$. A least-square minimization routine led to the determination of the Legendre coefficients for the azimuthally averaged spectral radiance. This is possible knowing that azimuthal dependence of the light field under optically thick ice is negligible (Pegau and Zaneveld, 2000). Angles below 25° ($\mu > 0.906$) were discarded to mitigate the effect of self-shadow and the drastic light fall-off at the edge of the field of view. We used the development of the polynomials up to a fifth degree $l$, found sufficient to fit different shapes of $\bar{L}(\mu)$ commonly found in the medium. Figure S3 in the Supplement shows some results of the fit for the data taken during the AO2018 expedition. Two example cases are displayed: for a depth of 40 cm at $\lambda = 480$ nm and for a depth of 120 cm at $\lambda = 540$ nm.

## Appendix B

This section is aimed at potential users interested in making radiometric measurements with a commercial 360° camera in scattering environments. We focus on the challenges raised by changes in incident radiation and camera disturbances to the radiative field within ice.

Firstly, it is important to minimize disturbance to the incident radiation by avoiding movement between the sun and the site of interest and by reducing sources of shading. Ideally, the site should be as homogeneous as possible over a large area, particularly in terms of snow cover. It is also essential to use an upward-looking radiometer to monitor irradiance throughout the experiment, especially under variable sky conditions such as passing clouds. Secondly, the radiative field within the pack ice must remain as undisturbed as possible, despite the hole made for the measurement. The use of a diffuser to block unwanted light should be considered. Additionally, the camera and boom should be painted to match the reflectance of the surrounding medium to avoid absorbing light that would alter the radiative field. Thirdly, measuring light in air above the freeboard level presents several challenges due to the reduced radiance caused by the change in the refractive index. A satisfactory solution to this problem has yet to be identified.

**Code availability.** The scripts used in this study are available in the online repository: https://github.com/RaphaelLarouche/radianceCameraInsta360 (last access: 24 September 2025). The latest version is also archived in the Zenodo repository: https://doi.org/10.5281/zenodo.4660993 (Larouche, 2024). This repository includes all scripts for processing fieldwork data and routines for generating the figures presented in this paper. The Python interface for HydroLight is available on GitHub: https://github.com/BRaulier/HE60-PyMagister (last access: 24 September 2025) (Raulier, 2023). Note that the codes are subject to continuous development.
*Data availability.* The dataset of this study is accessible online through the Zenodo repository: https://doi.org/10.5281/zenodo.14263255 (Larouche et al., 2024a).

Additionally, data from the RAMSES irradiance sensors collected during the AO2018 expedition are accessible via the Meereisportal platform: https://www.meereisportal.de/en/ (last access: 24 September 2025). These datasets can be retrieved from this page under the buoy name 2018R4 (Grosfeld et al., 2016).

*Supplement.* The supplement related to this article is available online at https://doi.org/10.5194/tc-19-4785-2025-supplement.

*Author contributions.* CK and BR did the fieldwork and acquired the in-ice measurements presented in this study. RL, SLG, and CK designed the camera calibration and characterization methodologies, while RL carried them out. BR, RL, CK, and SLG designed and performed the radiative transfer simulations. RL and BR prepared – with equal contribution – the first draft of this paper, with inputs from all the authors. SLG and MB had the idea to use a 360° camera inside sea ice and provided help and guidance throughout the project. ST and MB supervised the project and provided guidance.

*Competing interests.* The contact author has declared that none of the authors has any competing interests.

*Disclaimer.* Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

*Acknowledgements.* We gratefully acknowledge the scientific and financial support of Québec-Océan. Field measurements during the AO18 expedition on board icebreaker Oden were funded by the Helmholtz infrastructure initiative “FRAM” (Frontiers in Arctic marine Monitoring) and supported by the Swedish Polar Research Secretariat (SPRS). We thank Philipp Anhaus and Mario Hoppmann for assistance in the field. CK was supported by a postdoctoral fellowship from Sentinel North. We would like to thank Anne-Sophie Poulin-Girard for useful inputs regarding calibration methodologies, Marie-Hélène Forget for coordination, and Numerical Optics CEO John Hedley for his generous answers to our countless questions about HydroLight. Also, we would like to thank Daniel C. Côté, Yasmine Alikacem, and Christophe Perron for fruitful discussions.

*Financial support.* This research has been supported by the Canada Excellence Research Chairs, Government of Canada (Remote sensing of Canada's new Arctic frontier), the Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (Discovery Grant Marcel Babin, Northern research supplement RGPIN-2020-06384, CREATE 497040-2017, and NSERC SMAART Training Grant), the Canada First Research Excellence Fund (Sentinel North), and the Networks of Centres of Excellence of Canada (ArcticNet).

*Review statement.* This paper was edited by Ted Maksym and reviewed by two anonymous referees.

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