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The Cryosphere An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/tc-2020-269
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-269
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2020

07 Oct 2020

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This preprint is currently under review for the journal TC.

The retrieval of snow properties from SLSTR/Sentinel-3 – part 1: method description and sensitivity study

Linlu Mei, Vladimir Rozanov, Christine Pohl, Marco Vountas, and John P. Burrows Linlu Mei et al.
  • Institute of Environmental Physics, University of Bremen, Germany

Abstract. The eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm has been applied on the Top-Of-Atmosphere reflectance measured by the Sea and Land Surface Temperature Radiometer (SLSTR) instrument onboard Sentinel-3 to derive snow properties: Snow Grain Size (SGS), Snow Particle Shape (SPS) and Specific Surface Area (SSA) under cloud-free conditions. This is the first part of the paper, to describe the retrieval method and the sensitivity study. Nine pre-defined ice crystal particle shapes (aggregate of 8 columns, Drontal, hollow bullet rosettes, hollow column, plate, aggregate of 5 plates, aggregate of 10 plates, solid bullet rosettes, column) are used to describe the snow optical properties. The optimal SGS and SPS are estimated iteratively utilizing a Look-Up-Table (LUT) approach. The SSA is then calculated using another pre-calculated LUT for the retrieved SGS and SPS. The optical properties (e.g., phase function) of the ice crystals can reproduce the wavelength-dependent/angular-dependent snow reflectance features, compared to laboratory measurements. A comprehensive study to understand the impact of aerosol, ice crystal shape, ice crystal surface roughness, and cloud contamination on the retrieval accuracy of snow properties has been performed based on SCIATRAN radiative transfer simulations. The main findings are (1) Snow angular and spectral reflectance feature can be described by the predefined ice crystal properties only when both SGS and SPS can be optimally and iteratively obtained; (2) The impact of ice crystal surface roughness plays minor effects on the retrieval results; (3) SGS and SSA show an inverse linear relationship; (4) The retrieval of SSA assuming non-convex particle shape, compared to convex particle (e.g. sphere) shows larger results; (5) Aerosol/cloud contamination due to unperfected atmospheric correction and cloud screening introduces underestimation of SGS, inaccurate SPS and overestimation of SSA.

Linlu Mei et al.

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Linlu Mei et al.

Linlu Mei et al.

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Latest update: 25 Oct 2020
Publications Copernicus
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is part 1 of the paper, which shows the method description and sensitivity study. The paper investigates the major impact factors, including the assumptions of snow optical properties and atmospheric conditions (cloud and aerosol) on snow properties retrievals from satellite observation.
This paper presents a new snow property retrieval algorithm from satellite observations. This is...
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