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

Abstract

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.

Comparison of optical-equivalent snow grain size estimates under Arctic low Sun conditions during PAMARCMiP 2018

“…The sensitive study, as presented in Mei et al (2020b), shows that the impact of snow particle shape selection on the the r opt -retrieval is significant, and potential cloud/aerosol contamination introduce an underestimation of r opt . The comparison between XBAER derived snow grain size and ground-based measurements shows a relative difference of less than 5 % (Mei et al, 2020c).…”

“…Possible reasons for the deviation of the XBAER results, but also of the SMART retrieval, could originate from the assumption of the snow particle shape when calculating the LUTs. While for the SMART retrieval, a mixture of grain shapes is assumed, XBAER estimates the snow grain shape in 9 classes (Mei et al, 2020b) in addition to the snow grain size. For the considered area, mostly droxtals were retrieved over the land and the coastal region, and aggregates of 8 columns over sea ice.…”

“…For the considered area, mostly droxtals were retrieved over the land and the coastal region, and aggregates of 8 columns over sea ice. The use of an inappropriate ice crystal shape in XBAER leads to an error of less than 10% to more than 50% in the retrieval of grain size, depending on the grain size value itself (Mei et al, 2020b). The similar treatment of ice crystal shape in the SGSP and SMART retrieval provides a similar retrieved grain size while a totally different ice crystal shape assumption in the XBAER algorithm leads to a larger diversity, compared to the SGSP and SMART results.…”