Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements

Clicks: 205
ID: 118978
2020
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Monitoring the evolution of snow on the ground and lake ice—two of the most important components of the changing northern environment—is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to ≈1 m ice thickness and a radar resolution of 4 cm; (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m; (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness; (4) continuous monitoring of surface snow density under particular Antarctic conditions; (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.
Reference Key
pomerleau2020sensorslow Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Patrick Pomerleau;Alain Royer;Alexandre Langlois;Patrick Cliche;Bruno Courtemanche;Jean-Benoît Madore;Ghislain Picard;Éric Lefebvre;Pomerleau, Patrick;Royer, Alain;Langlois, Alexandre;Cliche, Patrick;Courtemanche, Bruno;Madore, Jean-Benoît;Picard, Ghislain;Lefebvre, Éric;
Journal sensors
Year 2020
DOI
10.3390/s20143909
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.