Abstract
Abstract. The eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm has been designed for the top-of-atmosphere reflectance measured by the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board 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 SPSs (aggregate of 8 columns, droxtal, hollow bullet rosette, hollow column, plate, aggregate of 5 plates, aggregate of 10 plates, solid bullet rosette, 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 and angular-dependent snow reflectance features, compared to laboratory measurements. A comprehensive study to understand the impact of aerosols, SPS, ice crystal surface roughness, cloud contamination, instrument spectral response function, the snow habit mixture model and snow vertical inhomogeneity in 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 features 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 on the retrieval results is minor; (3) SGS and SSA show an inverse linear relationship; (4) the retrieval of SSA assuming a non-convex particle shape, compared to a convex particle shape (e.g., sphere), shows larger retrieval results; (5) aerosol/cloud contamination due to unperfected atmospheric correction and cloud screening introduces underestimation of SGS, “inaccurate” SPS and overestimation of SSA; (6) the impact of the instrument spectral response function introduces an overestimation into retrieved SGS, introduces an underestimation into retrieved SSA and has no impact on retrieved SPS; and (7) the investigation, by taking an ice crystal particle size distribution and habit mixture into account, reveals that XBAER-retrieved SGS agrees better with the mean size, rather than with the mode size, for a given particle size distribution.
Highlights
In order to have a reasonable setting for observation/illumination geometries in the sensitivity study, we perform a statistical analysis of the Sea and Land Surface Temperature Radiometer (SLSTR) observation geometries, to Mei et al (2020a)
There are a couple of criteria we considered for the selection of the optimal wavelengths (0.55 and 1.6 μm) in the XBAER algorithm, for the purpose of creating a long-term satellite snow property dataset with good and stable accuracy
When applying the XBAER algorithm to the SLSTR instrument for real scenarios, two additional factors need to be considered as well: one is the impact of the instrument spectral response function (SRF), and the other one is the representativeness of the snow scenario for reality
Summary
Snow properties such as snow albedo, snow grain size (SGS), snow particle shape (SPS), specific surface area (SSA) and snow purity (Warren and Wiscombe, 1980; Painter et al, 2003; Hansen and Nazarenko, 2004; Taillandier et al, 2007; Gallet et al, 2009; Battaglia et al, 2010; Gardner and Sharp, 2010; Domine et al, 2011; Liu et al, 2012; Qu et al, 2015; Baker, 2019; Pohl et al, 2020a) show large variabilities temporally and spatially (Kukla et al, 1986).
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