Snow Grain Size Research Articles

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect.

Linking lidar multiple scattering profiles to snow depth and snow density: an analytical radiative transfer analysis and the implications for remote sensing of snow

Lidar multiple scattering measurements provide the probability distribution of the distance laser light travels inside snow. Based on an analytic two-stream radiative transfer solution, the present study demonstrates why/how these lidar measurements can be used to derive snow depth and snow density. In particular, for a laser wavelength with little snow absorption, an analytical radiative transfer solution is leveraged to prove that the physical snow depth is half of the average distance photons travel inside snow and that the relationship linking lidar measurements and the extinction coefficient of the snow is valid. Theoretical formulas that link lidar measurements to the extinction coefficient and the effective grain size of snow are provided. Snow density can also be derived from the multi-wavelength lidar measurements of the snow extinction coefficient and snow effective grain size. Alternatively, lidars can provide the most direct snow density measurements and the effective discrimination between snow and trees by adding vibrational Raman scattering channels.

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

The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Nonhydrostatic—Aerosols and Reactive Trace gases) simulations. The retrieval methods are based on optical measurements and rely on the ropt-dependent absorption of solar radiation in snow. The measurement data were taken during a three-week campaign that was conducted in the North of Greenland in March/April 2018, such that the retrieval methods and radiation measurements are affected by enhanced uncertainties under these low-Sun conditions. An adjusted airborne retrieval method is applied which uses the albedo at 1700 nm wavelength and combines an atmospheric and snow radiative transfer model to account for the direct-to-global fraction of the solar radiation incident on the snow. From this approach, we achieved a significantly improved uncertainty (<25%) and a reduced effect of atmospheric masking compared to the previous method. Ground-based in situ measurements indicated an increase of ropt of 15 µm within a five-day period after a snowfall event which is small compared to previous observations under similar temperature regimes. ICON-ART captured the observed change of ropt during snowfall events, but systematically overestimated the subsequent snow grain growth by about 100%. Adjusting the growth rate factor to 0.012 µm2 s−1 minimized the difference between model and observations. Satellite-based and airborne retrieval methods showed higher ropt over sea ice (<300 µm) than over land surfaces (<100 µm) which was reduced by data filtering of surface roughness features. Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals revealed a large spread within a series of subsequent individual overpasses, indicating their limitations in observing the snow grain size evolution in early spring conditions with low Sun.

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

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.

The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study

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.

Characterization of sea ice and its snow cover in the Arctic Pacific sector during the summer of 2016

A comprehensive analysis of sea ice and its snow cover during the summer in the Arctic Pacific sector was conducted using the observations recorded during the 7th Chinese National Arctic Research Expedition (CHIANRE-2016) and the satellite-derived parameters of the melt pond fraction (MPF) and snow grain size (SGS) from MODIS data. The results show that there were many low-concentration ice areas in the south of 78°N, while the ice concentration and thickness increased significantly with the latitude above the north of 78°N during CHIANRE-2016. The average MPF presented a trend of increasing in June and then decreasing in early September for 2016. The average snow depth on sea ice increased with latitude in the Arctic Pacific sector. We found a widely developed depth hoar layer in the snow stratigraphic profiles. The average SGS generally increased from June to early August and then decreased from August to September in 2016, and two valley values appeared during this period due to snowfall incidents.

Physically based model of the contribution of red snow algal cells to temporal changes in albedo in northwest Greenland

Abstract. Surface albedo of snow and ice is substantially reduced by inorganic impurities, such as aeolian mineral dust (MD) and black carbon (BC), and also by organic impurities, such as microbes that live in the snow. In this paper, we present the temporal changes of surface albedo, snow grain size, MD, BC and snow algal cell concentration observed on a snowpack in northwest Greenland during the ablation season of 2014 and our attempt to reproduce the changes in albedo with a physically based snow albedo model. We also attempt to reproduce the effects of inorganic impurities and the red snow algae (Sanguina nivaloides) on albedo. Concentrations of MD and red snow algae in the surface snow were found to increase in early August, while snow grain size and BC were found to not significantly change throughout the ablation season. Surface albedo was found to have decreased by 0.08 from late July to early August. The albedo simulated by the model agreed with the albedo observed during the study period. However, red snow algae exerted little effect on surface albedo in early August. This is probably owing to the abundance of smaller cells (4.9×104 cells L−1) when compared with the cell abundance of red snow reported by previous studies in the Arctic region (∼108 cells L−1). The simulation of snow albedo until the end of the melting season, with a snow algae model, revealed that the reduction in albedo attributed to red snow algae could equal 0.004, out of a total reduction of 0.102 arising from the three impurities on a snowpack in northwest Greenland. Finally, we conducted scenario simulations using the snow albedo model, coupled with the snow algae model, in order to simulate the possible effects of red snow blooming on snow albedo under warm conditions in northwest Greenland. The result suggests that albedo reduction by red snow algal growth under warm conditions (surface snow temperature of +1.5 ∘C) reached 0.04, equivalent to a radiative forcing of 7.5 W m−2 during the ablation season of 2014. This coupled albedo model has the potential to dynamically simulate snow albedo, including the effect of organic and inorganic impurities, leading to proper estimates of the surface albedo of snow cover in Greenland.

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

Correlation between effective snow grain size and hyper-spectral absorption peak characteristics – A case study over the North West Himalaya

ABSTRACTRadiative transfer theory based retrievals of effective snow grain size (SGS), albedo were widely explored using hyper-spectral sensors. However, investigation of linkage between diagnostic absorption peak characteristics (PCs) and snow surface parameters remained scarce, though these are significant to determine composition and state of material. In present study, field data acquired during three winter seasons was analyzed to study variability of effective SGS with PCs (i.e. relative depth, peak position and asymmetry). Effective SGS was found positively correlated with relative depth, with correlation of 90% and 86% for absorption peaks around 1.24 µm and 1.03 µm respectively. Asymmetry was found in good correlation with SGS (74%) at 1.24 µm than at 1.03 µm (21%). Further, PCs were derived using Hyperion data in ENVI software on pixel basis. Significance of PCs is described and compared with effective SGS, which was retrieved using widely accepted Asymptotic Radiative Transfer model. This work provide an alternate approach to infer state of snow metamorphism using peak characteristics, in scarcity of reference spectral database of snow classes.

On the reflectance spectroscopy of snow

Abstract. We propose a system of analytical equations to retrieve snow grain size and absorption coefficient of pollutants from snow reflectance or snow albedo measurements in the visible and near-infrared regions of the electromagnetic spectrum, where snow single-scattering albedo is close to 1.0. It is assumed that ice grains and impurities (e.g., dust, black and brown carbon) are externally mixed, and that the snow layer is semi-infinite and vertically and horizontally homogeneous. The influence of close-packing effects on reflected light intensity are assumed to be small and ignored. The system of nonlinear equations is solved analytically under the assumption that impurities have the spectral absorption coefficient, which obey the Ångström power law, and the impurities influence the registered spectra only in the visible and not in the near infrared (and vice versa for ice grains). The theory is validated using spectral reflectance measurements and albedo of clean and polluted snow at various locations (Antarctica Dome C, European Alps). A technique to derive the snow albedo (plane and spherical) from reflectance measurements at a fixed observation geometry is proposed. The technique also enables the simulation of hyperspectral snow reflectance measurements in the broad spectral range from ultraviolet to the near infrared for a given snow surface if the actual measurements are performed at a restricted number of wavelengths (two to four, depending on the type of snow and the measurement system).

Modeling biases in laser-altimetry measurements caused by scattering of green light in snow

Laser altimetry offers the potential to monitor ice-sheet elevation changes with millimeter accuracy. While previous missions have used infrared lasers to make these measurements, NASA's upcoming ICESat (Ice, Cloud, and land Elevation Satellite)-2 mission will use a green laser. Because ice absorbs green light very weakly, in the absence of light-absorbing impurities, green photons can scatter off many snow grains before returning to the surface, delaying the return pulse and leading to an apparent downward shift in the snow surface. In this paper, we explore the effects of snow-grain size and impurity content on these measurements, and investigate strategies that might help minimize the biases they introduce. We find that an uninformed choice of measurement parameters (a windowed mean including a large range of photons around the surface) can result in >0.45 m of apparent surface-height variation between large and small grain sizes. Other choices of measurement parameters, such as a windowed median, can reduce this uncertainty by a factor of two to three. In addition, measurements of surface reflectance at green and infrared wavelengths, and interpretation of return-pulse shapes may be used to estimate and correct for these biases.

Snow Grain-Size Estimation Over Mountainous Areas From MODIS Imagery

Snow in mountainous areas is a major source of surface water and groundwater recharge in the world. The water balance in mountainous regions is controlled by the interactions between the climate, cryospheric, and hydrological systems. Surface snow grain is a sensitive thermodynamic indicator of snowpack and plays an important role in the snow albedo. In mountainous regions, the complicated terrain conditions may introduce errors in the snow grain size estimated from satellite imagery. In this letter, an effective method based on the Snow Grain-Size and Pollution (SGSP) amount algorithm is proposed to estimate the surface snow grain size with careful topographic correction, using spectral reflectance data in Channel 5 (1.24 $\mu \text{m}$ ) of the Moderate Resolution Imaging Spectrometer. The SGSP-estimated snow grain size was validated with in situ measurements collected from field campaigns in mountainous areas of Manasi River Basin, China, during the periods of snow accumulation and ablation from 2011 to 2015. The $R^{2}$ value of 0.90 and root mean squared error of $80.42~\mu \text{m}$ were obtained.

Snow specific surface area remote sensing retrieval using a microstructure based reflectance model

The snow specific area (SSA) is a quantity to describe the size of snow grains, which controls the albedo of snow surface. The remote sensing retrieval of snow SSA is important for large scale energy and snow physical properties studies. The previous SSA retrieval algorithms are based on snow reflectance forward models with shape assumptions of snow grains, which may cause bias in the quantitative retrieval of SSA. In this paper, a new SSA retrieval algorithm based on a recently developed snow reflectance model is presented. In the reflectance model, the microstructure of snow medium is simulated by computer and the light scattering process is modeled by Monte Carlo ray tracing and radiative transfer theory. Validation shows the model can accurately simulate snow surface reflectance even at large incident or observation zenith angles. Three datasets were used to validate the SSA retrieval algorithm. The validation results show that the SSA can be accurately estimated using MODIS data. The difference of SSA by using reflectance of different wavelengths are studied by MODIS band 5 and band 6 data. The SSA retrieval algorithm is then applied to seasonal snow cover to study the temporal and spatial pattern of snow surface optical grain size.

Brightness temperature simulation of snow cover based on snow grain size evolution usingin situdata

Snow depth parameter inversion from passive microwave remote sensing is of great significance to hydrological process and climate systems. The Helsinki University of Technology (HUT) model is a commonly used snow emission model. Snow grain size (SGS) is one of the important input parameters, but SGS is difficult to obtain in broad areas. The time series of SGS are first evolved by an SGS evolution model (Jordan 91) using in situ data. A good linear relationship between the effective SGS in HUT and the evolution SGS was found. Then brightness temperature simulations are performed based on the effective SGS and evolution SGS. The results showed that the biases of the simulated brightness temperatures based on the effective SGS and evolution SGS were −6.5 and −3.6 K , respectively, for 18.7 GHz and −4.2 and −4.0 K for 36.5 GHz. Furthermore, the model is performed in six pixels with different land use/cover type in other areas. The results showed that the simulated brightness temperatures based on the evolution SGS were consistent with those from the satellite. Consequently, evolution SGS appears to be a simple method to obtain an appropriate SGS for the HUT model.

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

An accurate precipitation phase determination—i.e., solid versus liquid—is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.

Snow and Glacier Investigations Using Hyperspectral Data in the Himalaya

This article presents highlights of the research work done in hyperspectral remote sensing in the Himalayan cryosphere. Hyperspectral radiometric investigations conducted at different field locations of NW Himalaya and cold laboratory are discussed. Spectral signatures were collected for varying snow grain size, contamination, liquid water content, vegetation/soilmixed snow, glacier ice, moraines and other ambient objects. The important wavelengths for snow applications are found to be 440, 550, 590, 660, 860, 1050, 1240 and 1650 nm. Further, the retrieval of snow parameters such as grain size, spectral albedo and snow contamination using imaging data at the above wavelength channels is discussed. Wavelengths 550, 1240 and 1660 nm are found to be useful for discriminating different glacier features. Limitations in hyperspectral remote sensing such as availability of imaging data, rugged topography and further research issues such as multi-sensor mapping and data fusion, multiangle measurements, 3D adjacency effect and improved algorithms for quantitative retrieval of contaminants are identified.

J1020107 スキー用ポリエチレンの雪面摩擦特性

Several theories of the friction on snow and ice have been suggested. Especially, the melt-water lubrication theory proposed by Bowden and Hughes has been widely supported. However, even in very cold environment, such as water cannot exist, the surface of hard snow could be slippery, that was noticed by Nikki in skiing in Antarctica. It could be also difficult to explain some of experimental results reported recently. In this study we have measured the coefficient of friction statically and dynamically on various snow surfaces to consider the mechanism of friction fundamentally. Influence of surface energy of slider, grain size of snow, temperature, contact time (experiment on static friction), and sliding velocity (experiment on kinetic friction) has been investigated with various polyethylene for ski.

Mapping blue-ice areas in Antarctica using ETM+ and MODIS data

AbstractBlue-ice areas (BIAs) and their geographical distribution in Antarctica were mapped using Landsat-7 ETM+ images with 15 m spatial resolution obtained during the 1999–2003 austral summers and covering the area north of 82.5° S, and a snow grain-size image of the MODIS-based Mosaic of Antarctica (MOA) dataset with 125 m grid spacing acquired during the 2003/04 austral summer from 82.5°S to the South Pole. A map of BIAs was created with algorithms of thresholds based on band ratio and reflectance for ETM+ data and thresholds based on snow grain size for the MOA dataset. The underlying principle is that blue ice can be separated from snow or rock by their spectral discrepancies and by different grain sizes of snow and ice. We estimate the total area of BIAs in Antarctica during the data acquisition period is 234 549 km2, or 1.67% of the area of the continent. Blue ice is scattered widely over the continent but is generally located in coastal or mountainous regions. The BIA dataset presented in this study is the first map covering the entire Antarctic continent sourced solely from ETM+ and MODIS data. This dataset can potentially benefit other studies in glaciology, meteorology, climatology and paleoclimate, meteorite collection and airstrip site selection.

Experimental analysis of snow micropenetrometer (SMP) cone penetration in homogeneous snow layers

The cone penetration test (CPT) is widely used to determine in situ soil and snow characteristics and stratigraphy. For avalanche forecasting, knowledge about snow stratigraphy is of crucial importance. Portable electric cone penetrometers have therefore been developed with the goal of obtaining rapid and accurate measurements of snow stratigraphy. The most widely used electric penetrometer is the snow micropenetrometer (SMP), a constant-speed small-diameter cone penetrometer. The SMP was specifically designed to study snow and is not a penetrometer in the geotechnical sense, as the diameter of the SMP cone (5 mm) is comparable to the typical size of snow grains (0.1 to 1 mm). Previous numerical and experimental studies of the CPT in granular materials have highlighted the importance of material compaction around the cone. Nevertheless, given the high porosity of snow, compaction of failed elements around the SMP cone is generally neglected when interpreting SMP force signals. To verify this assumption, microcomputed tomography and particle image velocimetry were used to investigate the deformation of snow during SMP cone penetration. Results from laboratory experiments with uniform snow show that a compaction zone around the SMP tip develops during penetration. The size of the compaction zone was on average twice as large as the actual size of the cone, increasing with increasing snow density. Furthermore, an average penetration depth of about 40 mm was required for the compaction zone to develop fully. This critical penetration depth roughly decreased with increasing snow density. These results show that the compaction zone around the SMP tip is far from negligible and has to be accounted for when interpreting SMP force measurements.