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

Abstract

Abstract. 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 Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process.

Measurements and Modeling of Optical-Equivalent Snow Grain Sizes under Arctic Low-Sun Conditions

“…The SLTSR-based retrieval results were validated against data from seven field-based measurements showing correlation coefficients higher than 0.85 with root mean square errors for r opt and SSA of less than 15 µm and 10 m 2 kg −1 , respectively [39]. A similar correlation coefficient (0.86) was derived from the comparison of the MODIS-based product with ground measurements from six field experiments [40].…”

“…The sensitivity study, as presented in Mei et al [37], shows that the impact of snow particle shape selection on the r opt retrieval is significant, and potential cloud/aerosol contamination introduce an underestimation of r opt . The previous comparison between XBAER derived snow grain size and ground-based measurements of continental snow shows a relative difference of less than 5% [39].…”

The Determination of the Snow Optical Grain Diameter and Snowmelt Area on the Greenland Ice Sheet Using Spaceborne Optical Observations

“…The spherical grain assumption was motivated by the successful estimation of spectral hemispherical reflectances of snow with nonspherical ice particles when representing them as spherical grains of a similar volume-to-surface-area [37]. While this technique has been widely applied, it has been noted that the spherical assumption was limited in accounting for the directional variation of snow reflectance [38][39][40][41][42][43][44][45] and therefore would lead to errors when used on remotely sensed directional reflectance. Several models using the nonspherical grains assumption have been applied to snow characteristics retrievals, for example on data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites [43,44] or on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites [29,38].…”

“…While this technique has been widely applied, it has been noted that the spherical assumption was limited in accounting for the directional variation of snow reflectance [38][39][40][41][42][43][44][45] and therefore would lead to errors when used on remotely sensed directional reflectance. Several models using the nonspherical grains assumption have been applied to snow characteristics retrievals, for example on data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites [43,44] or on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites [29,38]. The asymptotic radiative transfer theory allows the retrieval of the snow albedo and optical grain diameter for snow with nonspherical grains and has been applied to MODIS data [24,30,[46][47][48], AATSR and MERIS data from the ENVISAT satellite [47,49] and OLCI data from Sentinel-3 satellites [50,51].…”

Retrieval of Snow Albedo and Total Ozone Column from Single-View MSI/S-2 Spectral Reflectance Measurements over Antarctica

“…The climatic effects of snow cover depend not only on its extent [4] but also on its spectral albedo, which plays an important role in the modification of the backscattered solar energy on local and global scales [5,6]. Snow albedo products are currently available at moderate resolution (i.e., 300 m from Ocean and Land Colour Imager (OLCI) [7,8], 500 m from MODerate Imaging Spectrometer (MODIS) [9,10] and Sea and Land Surface Temperature Radiometer (SLSTR) [11,12], and 1 km from Second Generation Global Imager (SGLI) [13]. However, they do not allow capturing the fine details of spatial variability of the snow surface properties [14].…”