scholarly journals Using GPS multipath for snow depth sensing - first experience with data from permanent stations in Slovakia

2013 ◽  
pp. 53-63 ◽  
Author(s):  
Jan Hefty
Keyword(s):  
Snow Depth ◽  
Depth Sensing

scholarly journals Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry

2020 ◽  
Vol 12 (20) ◽  
pp. 3352
Author(s):  
Jiachun An ◽  
Pan Deng ◽  
Baojun Zhang ◽  
Jingbin Liu ◽  
Songtao Ai ◽  
...  
Keyword(s):  
Snow Depth ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
The Arctic ◽  
Noise Power ◽  
Snow Surface ◽  
Depth Sensing ◽  
Three Stages ◽  
The Impact ◽  
Mean Square Errors

Snow plays a critical role in hydrological monitoring and global climate change, especially in the Arctic region. As a novel remote sensing technique, global navigation satellite system interferometric reflectometry (GNSS-IR) has shown great potential for detecting reflector characteristics. In this study, a field experiment of snow depth sensing with GNSS-IR was conducted in Ny-Alesund, Svalbard, and snow depth variations over the 2014–2018 period were retrieved. First, an improved approach was proposed to estimate snow depth with GNSS observations by introducing wavelet decomposition before spectral analysis, and this approach was validated by in situ snow depths obtained from a meteorological station. The proposed approach can effectively separate the noise power from the signal power without changing the frequency composition of the original signal, particularly when the snow depth changes sharply. Second, snow depth variations were analyzed at three stages including snow accumulation, snow ablation and snow stabilization, which correspond to different snow-surface-reflection characteristics. For these three stages of snow depth variations, the mean absolute errors (MAE) were 4.77, 5.11 and 3.51 cm, respectively, and the root mean square errors (RMSE) were 6.00, 6.34 and 3.78 cm, respectively, which means that GNSS-IR can be affected by different snow surface characteristics. Finally, the impact of rainfall on snow depth estimation was analyzed for the first time. The results show that the MAE and RMSE were 2.19 and 2.08 cm, respectively, when there was no rainfall but 5.63 and 5.46 cm, respectively, when it was rainy, which indicates that rainfall reduces the accuracy of snow depth estimation by GNSS-IR.

LONG-TERM CHANGES IN SNOWFALL AND SNOW DEPTH IN TOYAMA PREFECTURE DURING 1974-2015

2018 ◽  
Vol 74 (5) ◽  
pp. I_73-I_78
Author(s):  
Shigehiko ODA ◽  
Takuya MATSUURA ◽  
Masashi SHIMOSAKA ◽  
Taichi TEBAKARI
Keyword(s):  
Snow Depth ◽  
Long Term Changes

scholarly journals Depth-Sensing Indentation as a Micro- and Nanomechanical Approach to Characterisation of Mechanical Properties of Soft, Biological, and Biomimetic Materials

Nanomaterials ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 15 ◽  
Author(s):  
Nikolay V. Perepelkin ◽  
Feodor M. Borodich ◽  
Alexander E. Kovalev ◽  
Stanislav N. Gorb
Keyword(s):  
Review Paper ◽  
Soft Materials ◽  
Data Fitting ◽  
Depth Sensing ◽  
Adhesive Properties ◽  
Distinctive Features ◽  
Experimental Part ◽  
Properties Of Materials ◽  
The Mathematical Model ◽  
Non Destructive

Classical methods of material testing become extremely complicated or impossible at micro-/nanoscale. At the same time, depth-sensing indentation (DSI) can be applied without much change at various length scales. However, interpretation of the DSI data needs to be done carefully, as length-scale dependent effects, such as adhesion, should be taken into account. This review paper is focused on different DSI approaches and factors that can lead to erroneous results, if conventional DSI methods are used for micro-/nanomechanical testing, or testing soft materials. We also review our recent advances in the development of a method that intrinsically takes adhesion effects in DSI into account: the Borodich–Galanov (BG) method, and its extended variant (eBG). The BG/eBG methods can be considered a framework made of the experimental part (DSI by means of spherical indenters), and the data processing part (data fitting based on the mathematical model of the experiment), with such distinctive features as intrinsic model-based account of adhesion, the ability to simultaneously estimate elastic and adhesive properties of materials, and non-destructive nature.

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