Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Abstract

Abstract. This study compares different methods to retrieve the specific surface area (SSA) of snow from satellite radiance measurements in mountainous terrain. It aims at addressing the effect on the retrieval of topographic corrections of reflectance, namely slope and aspect of terrain, multiple reflections on neighbouring slopes and accounting (or not) for the anisotropy of snow reflectance. Using MODerate resolution Imaging Spectrometer (MODIS) data for six different clear sky scenes spanning a wide range of snow conditions during the winter season 2008-2009 over a domain of 46 × 50 km in the French Alps, we compared SSA retrievals with and without topographic correction, with a spherical or non-spherical snow reflectance model and, in spherical case, with or without anisotropy corrections. The retrieved SSA values were compared to field measurements and to the results of the detailed snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis. It was found that the difference in terms of surface SSA between retrieved values and SAFRAN-Crocus output was minimal when the topographic correction was taken into account, when using a retrieval method assuming disconnected spherical snow grains. In this case, the root mean square deviation was 9.4 m 2 kg −1 and the mean difference was 0.1 m 2 kg −1 , based on 3170 pairs of observation and simulated values. The added-value of the anisotropy correction was not significant in our case, which may be explained by the presence of mixed pixels and surface roughness. MODIS retrieved data show SSA variations with elevation and aspect which are physically consistent and in good agreement with SAFRAN-Crocus outputs. The variability of the MODIS retrieved SSA within the topographic classes of the model was found to be relatively small (3.9 m 2 kg −1 ). This indicates that semi-distributed snowpack simulations in mountainous terrain with a sufficiently large number of classes provides a representation of the snowpack variability consistent with the scale of MODIS 500 m pixels.

Reconstructing the mass balance of Brewster Glacier, New Zealand, using MODIS-derived glacier-wide albedo

“…albedo of winter snow only), by being situated in the path of the prevailing westerly airflow, the Southern Alps of New Zealand are exposed to deposition of ashes and dust transported from Australia across the Tasman Sea. As the MODIS albedo retrieval allows additional optical properties of snow and ice to be estimated, such as specific surface area (SSA) (Mary et al, 2013) and effective impurity content (e.g. Black Carbon and mineral dust), there is potential to explore further the albedo sequence on selected New Zealand glaciers and snowfields.…”

“…MODImLab is a MATLAB toolbox allowing MODIS L1B swath data products to be processed to map subpixel snow fraction Charrois et al, 2013), snow and ice albedo (Dumont et al, 2011(Dumont et al, , 2012, and snow specific surface area (Mary et al, 2013). It implements an image fusion approach that improves mapping in steep terrain by combining MOD02QKM bands 1 and 2 (250 m resolution) with MOD02HKM bands 3 to 7 (500 m resolution) to produce seven spectral bands at a spatial resolution of 250 m .…”

Development and calibration of an automatic spectral albedometer to estimate near-surface snow SSA time series

“…However, these techniques still require an operator which is often a limitation, particularly in Antarctica. Optical satellite remote sensing has been used to retrieve optical radius (e.g., Nolin and Dozier, 2000;Painter et al, 2009;Negi et al, 2011;Mary et al, 2013) in particular in Antarctica (Scambos et al, 2007;Jin et al, 2008). Mary et al (2013) and Tanikawa et al (2015) give an overview of the numerous existing algorithms.…”

“…Optical satellite remote sensing has been used to retrieve optical radius (e.g., Nolin and Dozier, 2000;Painter et al, 2009;Negi et al, 2011;Mary et al, 2013) in particular in Antarctica (Scambos et al, 2007;Jin et al, 2008). Mary et al (2013) and Tanikawa et al (2015) give an overview of the numerous existing algorithms. Obtaining grain size on a large spatial extent and for many years is of great interest, but attaining an accuracy similar to that of field techniques requires to overcome several additional defects that are specific to space-borne sensors and are in fact very similar to those faced for albedo retrieval.…”

On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat

“…However, the steep topography of our study area necessitated the use of an enhanced algorithm for detecting snow cover in mountainous terrain using MODIS data. MODImLab is a MATLAB toolbox allowing MODIS L1B swath data products (radiance at the top of atmosphere TOA) to be downloaded and processed automatically towards the production of binary snow maps and maps of sub-pixel snow fraction [52] snow albedo [53], and snow specific surface area [54]. In this study, MODImLab was used to process MODIS/TERRA L1B data using the image fusion approach, which allows for estimation of the short wave infrared band at 250 m spatial resolution [55].…”