Toward a new method for estimating underwater light regime under spatially heterogeneous surface environments in the Arctic


Arctic Change 2017
December 11 - 15, Québec City, Québec, Canada


Philippe Massicotte, Guislain Bécu, Simon Lambert-Girard, Julien Laliberté, Marcel Babin

Underwater light field

The characteristics of underwater light field are very important drivers of ecosystem functioning:

  • Primary production

  • Photo-chemical reactions (photo-degradation)

  • Energy budget in the water column

Measuring light in open water

Nowadays, measuring downwelling irradiance (\(E_d\)) in open water is pretty straightforward.

Typical underwater light profiles

In open water, downwelling irradiance (\(E_d\)) is known to decrease exponentially with increasing depth.

Mathematical formulation

\[ Ed(z) = Ed(0) \times e^{-K \times z} \]

  • \(K\) (\(m^{-1}\)) is the vertical diffuse attenuation coefficient describing the rate at which light decreases with increasing depth.

  • Important metric used by biologists and to parameterize models (need for precise estimates).

An example

Assuming an optically homogeneous water column.

Problematics

Due to spatial horizontal heterogeneity, measuring vertical light profiles under ice cover presents considerable challenges in comparison to open water.

Credit: Joannie Ferland - Takuvik

What is seen by the radiometer





Note the heterogeneity of the light field.

Light measured under ice

Under a heterogeneous surface, light transmission can increase to reach a subsurface maximum between 5 and 10 meters.

Estimating K underice

Obviously, estimating \(K_{Ed}\) under ice represent a considerable challenge.

Estimating K underice

Influence of the horizontal heterogeneity on measured light.

what image shows

Estimating K underice

Due to the horizontal heterogeneity, measured light properties are only valid locally and cannot be generalized to the surrounding environment.

Main Objective

New method to have adequate light estimates under horizontally heterogeneous ice sheet

A promising avenue

One promising way is to use upwelling radiance (\(L_u\)), which is far less influenced by the surface spatial heterogeneity.

Caption for the picture.

Why upwelling radiance?

  1. Not influenced by the surface spatial heterogeneity (no subsurface maximum).
  1. Both seem to follow the same exponential decreases after the subsurface maximum point.

Data exploration

  • We propose using upwelling radiance attenuation coefficient (\(K_{Lu}\)) as a proxy to reconstruct downwelling irradiance light profiles under a spatially heterogeneous snow/ice environment.

  • En extensive dataset of light measurements (C-OPS, ACS) done under the landfast ice during the 2016 Green Edge field sampling.

What we did

  • Compare \(K_{Ed}\) and \(K_{Lu}\) from simultaneously collected light profiles during the 2016 Green Edge ice camp mission (\(n = 86\)).

  • Given that water column is unlikely to be optically homogeneous, attenuation coefficients K were calculated on a 5-meters sliding window.

  • K were calculated starting at 10 meters to reduce the effect of subsurface light maximum.

What we did

  • A total of 36 120 non-linear models were calculated. 86 profiles \(\times\) 14 depths \(\times\) 15 wavelengths \(\times\) 2 lights (Ed, Lu)

  • A conservative \(R^2 = 0.99\) was used to filter poor models.

  • 14 942 models were kept for subsequent analysis.

Preliminary results

  • Generally a good relationship between \(K_{Lu}\) and \(K_{Ed}\).
  • Outliers in the 10-15 meter cluster.
  • Regression line close to the 1:1 line.
what image shows

Preliminary results

Decoupling at higher wavelengths (red region).

what image shows

Preliminary results

Tighter relation in the blue/green regions.

what image shows

Preliminary results

  • The relationship in the red is likely to be affected by the fluorescence of Chlorophyll a and the inelastic scattering of water.


  • Not a problem because, \(E_d\) is rarely influenced by surface heterogeneity due to the rapid absorption.

Preliminary results

Preliminary results

Possible workaround:


  1. Use \(K_{Lu}\) to estimate \(K_{Ed}\) in the blue/green regions which are influenced by the spatial heterogeneity of the surface.


  1. Directly estimate \(K_{Ed}\) in the red region because it is not influenced by the spatial heterogeneity of the surface.


  1. Reconstruct the \(Ed(z)\) profile.

Preliminary results

Reconstruction of Ed(z) profile.

What is next?

Derived \(K_{Ed}\) estimated from \(Lu\) profiles are compared with those estimated from inherent optical properties (IOPs), not influenced by the surface heterogeneity.

\[ K_{Ed} \approx \frac{a + b_b}{\mu_d} \]

  • \(a\) = absorption
  • \(b_b\) = back scattering
  • \(\mu_d\) = the average cosines of downward plane irradiances

IOPs data are currently processed by Guislain Bécu.

What is next?

Validate our method with different spatial heterogeneous conditions and water column IOPs generated numerically with 3D Monte-Carlo simulations.

The simulations would include:

  • 3D heterogeneous ice conditions
  • Water column IOPs (absorption and multiple scattering)

Acknowledgements

The GreenEdge project is funded by the following French and Canadian programs and agencies: ANR (Contract #111112), CNES (project #131425), IPEV (project #1164), CSA, Fondation Total, ArcticNet, LEFE and the French Arctic Initiative (GreenEdge project). This project would not have been possible without the support of the Hamlet of Qikiqtarjuaq and the members of the community as well as the Inuksuit School and its Principal Jacqueline Arsenault. The project is conducted under the scientific coordination of the Canada Excellence Research Chair on Remote sensing of Canada’s new Arctic frontier and the CNRS & Université Laval Takuvik Joint International laboratory (UMI3376). The field campaign was successful thanks to the contribution of J. Ferland, G. Bécu, C. Marec, J. Lagunas, F. Bruyant, J. Larivière, E. Rehm, S. Lambert-Girard, C. Aubry, C. Lalande, A. LeBaron, C. Marty, J. Sansoulet, D. Christiansen-Stowe, A. Wells, M. Benoît-Gagné, E. Devred and M.-H. Forget from the Takuvik laboratory, C.J. Mundy and V. Galindo from University of Manitoba as well as F. Pinczon du Sel and E. Brossier from Vagabond. We also thank Michel Gosselin, Québec-Océan, the CCGS Amundsen and the Polar Continental Shelf Program for their in-kind contribution in polar logistic and scientific equipment.