The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation

• Published in: The Cryosphere
• Main Authors: Mei, Linlu, Rozanov, Vladimir, Jäkel, Evelyn, Cheng, Xiao, Vountas, Marco, Burrows, John P.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2021
• Subjects: article; Verlagsveröffentlichung; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-15-2781-2021; https://noa.gwlb.de/receive/cop_mods_00057076; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00056726/tc-15-2781-2021.pdf; https://tc.copernicus.org/articles/15/2781/2021/tc-15-2781-2021.pdf

To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m2/kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic ...