Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance

Eppanapelli, Lavan Kumar; Lintzén, Nina; Casselgren, Johan; Wå̊hlin, Johan

doi:10.1061/(asce)cr.1943-5495.0000158

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…Wilkinson [32] detailed the influence of pressure sintering on snow microstructure and its mechanical properties. The sintering process in a snow pack was observed previously [33,34], where snow coarsening was forced due to loading and liquid water content. A common observation of the snow compaction between the tomography data and our previous works, is that the majority of displacement of snow grains occur at the near-surface layers.…”

Section: Discussionmentioning

confidence: 92%

3D Analysis of Deformation and Porosity of Dry Natural Snow during Compaction

et al. 2019

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The present study focuses on three-dimensional (3D) microstructure analysis of dry natural snow during compaction. An X-ray computed microtomography (micro-CT) system was used to record a total of 1601 projections of a snow volume. Experiments were performed in-situ at four load states as 0 MPa, 0.3 MPa, 0.6 MPa and 0.8 MPa, to investigate the effect of compaction on structural features of snow grains. The micro-CT system produces high resolution images (4.3 μm voxel) in 6 h of scanning time. The micro-CT images of the investigated snow volume illustrate that grain shapes are mostly dominated by needles, capped columns and dendrites. It was found that a significant number of grains appeared to have a deep hollow core irrespective of the grain shape. Digital volume correlation (DVC) was applied to investigate displacement and strain fields in the snow volume due to the compaction. Results from the DVC analysis show that grains close to the moving punch experience most of the displacement. The reconstructed snow volume is segmented into several cylinders via horizontal cross-sectioning, to evaluate the vertical heterogeneity of porosity distribution of the snow volume. It was observed that the porosity (for the whole volume) in principle decreases as the level of compaction increases. A distinct vertical heterogeneity is observed in porosity distribution in response to compaction. The observations from this initial study may be useful to understand the snow microstructure under applied stress.

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Section: Discussionmentioning

confidence: 92%

3D Analysis of Deformation and Porosity of Dry Natural Snow during Compaction

et al. 2019

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“…Thus, no successive tests can be made at a given site since the snowpack is irreversibly altered at every measurement. More gentle, non-destructive methods are based on the changes of spectral reflectance in the NIR region (920-1650 nm) of snow bearing different water contents [17,18] and ground-penetrating radar, by which an electromagnetic signal is generated and the reflected wavefield is measured, from which it is possible to map the values of θ w in the sampled region. Recent satellite and ground-based remote sensing are based on the analysis of microwave radiation reflected at the Earth's surface in comparison to a signal directly received at an antenna above the ground [19,20].…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopy-Based Assessment of the Liquid Water Content in Snow

2022

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In snow, water coexists in solid, liquid and vapor states. The relative abundance of the three phases drives snow grain metamorphism and affects the physical properties of the snowpack. Knowledge of the content of the liquid phase in snow is critical to estimate the snowmelt runoff and to forecast the release of wet avalanches. Liquid water does not spread homogeneously through a snowpack because different snow layers have different permeabilities; therefore, it is important to track sudden changes in the amount of liquid water within a specific layer. We reproduced water percolation in the laboratory, and used Raman spectroscopy to detect the presence of the liquid phase in controlled snow samples. We performed experiments on both fine- and coarse-grained snow. The obtained snow spectra are well fitted by a linear combination of the spectra typical of liquid water and ice. We progressively charged snow with liquid water from dry snow up to soaked snow. As a result, we exploited continuous, qualitative monitoring of the evolution of the liquid water content as reflected by the fitting coefficient c.

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“…In fact, snow granulometry is clearly visible in the NIR and the short waves of infrared regions (SWIR) [ 35 , 36 ]. Eppanapelli et al [ 37 ] found that the spectral reflectance of snow in the NIR spectrum is inversely proportional to the liquid water content of the snow. In addition, the absorption of ice in the NIR spectrum is very high [ 38 ], so the effect of impurities such as mineral dust and soot is negligible beyond wavelenths of 1000 nm.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the liquid water content of the snow was modeled based on the reflective properties of the snow. The results show that liquid water content is inversely correlated with NIR reflectance [ 33 , 37 ]. Density was also correlated with this part of the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data

Oufir

Chokmani

Alem

et al. 2021

Sensors

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This paper proposes an innovative method for classifying the physical properties of the seasonal snowpack using near-infrared (NIR) hyperspectral imagery to discriminate the optical classes of snow at different degrees of metamorphosis. This imaging system leads to fast and non-invasive assessment of snow properties. Indeed, the spectral similarity of two samples indicates the similarity of their chemical composition and physical characteristics. This can be used to distinguish, without a priori recognition, between different classes of snow solely based on spectral information. A multivariate data analysis approach was used to validate this hypothesis. A principal component analysis (PCA) was first applied to the NIR spectral data to analyze field data distribution and to select the spectral range to be exploited in the classification. Next, an unsupervised classification was performed on the NIR spectral data to select the number of classes. Finally, a confusion matrix was calculated to evaluate the accuracy of the classification. The results allowed us to distinguish three snow classes of typical shape and size (weakly, moderately, and strongly metamorphosed snow). The evaluation of the proposed approach showed that it is possible to classify snow with a success rate of 85% and a kappa index of 0.75. This illustrates the potential of NIR hyperspectral imagery to distinguish between three snow classes with satisfactory success rates. This work will open new perspectives for the modelling of physical parameters of snow using spectral data.

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Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance

Cited by 15 publications

References 27 publications

3D Analysis of Deformation and Porosity of Dry Natural Snow during Compaction

3D Analysis of Deformation and Porosity of Dry Natural Snow during Compaction

Raman Spectroscopy-Based Assessment of the Liquid Water Content in Snow

Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data

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