Retrieving the characteristics of slab ice covering snow by remote sensing - INSU - Institut national des sciences de l'Univers Accéder directement au contenu
Article Dans Une Revue The Cryosphere Année : 2016

Retrieving the characteristics of slab ice covering snow by remote sensing

Résumé

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.
Fichier principal
Vignette du fichier
tc-10-2113-2016.pdf (1.48 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

insu-03691499 , version 1 (09-06-2022)

Licence

Paternité

Identifiants

Citer

François Andrieu, Frédéric Schmidt, Bernard Schmitt, Sylvain Douté, Olivier Brissaud. Retrieving the characteristics of slab ice covering snow by remote sensing. The Cryosphere, 2016, 10, pp.2113-2128. ⟨10.5194/tc-10-2113-2016⟩. ⟨insu-03691499⟩
11 Consultations
9 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More