Articles | Volume 10, issue 5
The Cryosphere, 10, 2113–2128, 2016
The Cryosphere, 10, 2113–2128, 2016

Research article 15 Sep 2016

Research article | 15 Sep 2016

Retrieving the characteristics of slab ice covering snow by remote sensing

François Andrieu1, Frédéric Schmidt1, Bernard Schmitt2,3, Sylvain Douté2,3, and Olivier Brissaud2,3 François Andrieu et al.
  • 1GEOPS, Univ. Paris-Sud, CNRS, Université Paris-Saclay, Rue du Belvédère, Bât. 504-509, 91405 Orsay, France
  • 2Univ. Grenoble Alpes, IPAG, 38000 Grenoble, France
  • 3CNRS, IPAG, 38000 Grenoble, France

Abstract. We present an effort to validate a previously developed radiative transfer model, and an innovative Bayesian inversion method designed to retrieve the properties of slab-ice-covered surfaces. This retrieval method is adapted to satellite data, and is able to provide uncertainties on the results of the inversions. We focused on surfaces composed of a pure slab of water ice covering an optically thick layer of snow in this study. We sought to retrieve the roughness of the ice–air interface, the thickness of the slab layer and the mean grain diameter of the underlying snow. Numerical validations have been conducted on the method, and showed that if the thickness of the slab layer is above 5 mm and the noise on the signal is above 3 %, then it is not possible to invert the grain diameter of the snow. In contrast, the roughness and the thickness of the slab can be determined, even with high levels of noise up to 20 %. Experimental validations have been conducted on spectra collected from laboratory samples of water ice on snow using a spectro-radiogoniometer. The results are in agreement with the numerical validations, and show that a grain diameter can be correctly retrieved for low slab thicknesses, but not for bigger ones, and that the roughness and thickness are correctly inverted in every case.

Short summary
This article presents a set of spectro-goniometric measurements of different water ice samples and their comparison with an approximated radiative transfer model using a Bayesian approach. Two kinds of experiments were conducted: the specular spot was investigated, and then the diffuse radiation. We show that the approximated model is able to reproduce the spectro-radiogoniometric data satisfactorily, and that the inverted parameters are compatible with independent measurements.