Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation

Version 1 : Received: 11 November 2022 / Approved: 14 November 2022 / Online: 14 November 2022 (09:38:42 CET)

A peer-reviewed article of this Preprint also exists.

Kokhanovsky, A.; Vandecrux, B.; Wehrlé, A.; Danne, O.; Brockmann, C.; Box, J.E. An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation. Remote Sens. 2023, 15, 77. Kokhanovsky, A.; Vandecrux, B.; Wehrlé, A.; Danne, O.; Brockmann, C.; Box, J.E. An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation. Remote Sens. 2023, 15, 77.

Abstract

We present the update of the Snow and Ice (SICE) property retrieval algorithm proposed initially by Kokhanovsky et al. (2019). The algorithm is based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The algorithm is available as python and Fortran packages at https://github.com/GEUS-SICE/pySICE. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra.

Keywords

snow remote sensing; cloud screening; atmospheric correction; radiative transfer

Subject

Environmental and Earth Sciences, Environmental Science

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