Measurement of Snow Water Equivalent Using Drone-Mounted Ultra-Wide-Band Radar

Jenssen, Rolf-Ole Rydeng; Jacobsen, Sven

doi:10.3390/rs13132610

Cited by 18 publications

(8 citation statements)

References 43 publications

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“…Our present method increases this limit to 27 cm. Analogous work was performed by Jenssen and Jacobsen [18] in Norway. They measured the snow depth, snow density, and SWE using a UAV-mounted ultra-wide-band (0.7-4.5 GHz) radar.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Snow Indices in Assessing Snow Cover Depth in Northern Kazakhstan

et al. 2022

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This study compares the performances of four existing snow indices (Normalized-Difference Snow Index, Normalized-Difference Snow and Ice Index, Difference Snow Index, and Ratio Snow Index) in estimating snow cover depth at three agricultural enterprises in different soil zones, namely, the “North Kazakhstan Agricultural Experimental Station”, A.I. Barayev “Research and Production Center for Grain Farming”, and “Naidorovskoe”. From 30 January to 9 February 2022, the snow cover thickness and density were measured at 410 and 285 points, respectively, throughout the agricultural enterprise territories. It was found that: (1) snow-covered territories were effectively classified using all spectral indices except both combinations of RSI; (2) the snow cover fraction maps generated from DSI most accurately classified the non-snow areas as forest plantations, settlements, and strongly blown uplands; (3) the maps generated from DSI-2 presented a clear pattern of objects in all three study areas; (4) the liquid water in snowpacks is available in excess for possible reservation and rational use in agriculture during the dry season. At the “North Kazakhstan AES”, A.I. Barayev “Research and Production Center for Grain Farming”, and “Naidorovskoe”, the RMSE varied from 5.62 (DSI-2) to 6.85 (NDSII-2), from 3.46 (DSI-2) to 4.86 (RSI-1), and from 2.86 (DSI-2) to 3.53 (NDSII-1), respectively. The DSI-2-based snow depths best matched the ground truth, with correlations of 0.78, 0.69, and 0.80, respectively.

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Section: Discussionmentioning

confidence: 99%

Comparison of Snow Indices in Assessing Snow Cover Depth in Northern Kazakhstan

et al. 2022

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“…Wu [64] demonstrated the development of UAVbased ground-penetrating radar for soil moisture mapping. UAV-based radars have also been used to estimate snow depth and density [65]. Tese sensors are also needed to be explored for WRMM studies.…”

Section: Radar and Synthetic Aperturementioning

confidence: 99%

“…Unmanned aerial systems can prove to be a boon. Te main applications in the cryosphere include DSM generation [154], change detection, snow depth estimation [65,237,238], tundra vegetation mapping [239,240], ice-wedge polygon mapping [241], and so on.…”

Section: Urban Storm Water Mcdonaldmentioning

confidence: 99%

Uncrewed Aerial Systems in Water Resource Management and Monitoring: A Review of Sensors, Applications, Software, and Issues

Mishra

Avtar

Prathiba

et al. 2023

Advances in Civil Engineering

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Uncrewed aerial systems (UASs) are becoming very popular in the domain of water resource mapping and management (WRMM). Being a cheaper and quicker option capable of providing high temporal and spatial resolution data, UAS has become a much sought-after platform for remote sensing. Still, their application in the field is in its early stage. This paper encompasses basic concepts of UAS, different payloads and sensor technologies available, various methodologies for its application in WRMM, different software available, and challenges associated with them, thus presenting a comprehensive review of multiple applications of UAS in different sub-domains of water resources. From cryosphere, rivers and lakes, and coastal areas to sub-surface water, as well as from water quality to wastewater management, the authors have discussed various applications of uncrewed aerial vehicles. At the end of the paper, the authors have identified the issues posing problems in the wider implementation of UAS in WRMM. Also, the future scope of the UAS in WRMM has been discussed.

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“…Airborne GPR with a helicopter or fixed-wing aircraft has shown promise in glaciology studies for assessing ice thickness [Jenssen and Jacobsen, 2021;Pritchard et al, 2020].…”

Section: Introductionmentioning

confidence: 99%

“…Airborne GPR with a helicopter or fixed‐wing aircraft has shown promise in glaciology studies for assessing ice thickness (Jenssen & Jacobsen, 2021; Pritchard et al., 2020). Compared to piloted aircrafts, unmanned aerial vehicles (UAVs) are more convenient, mobile, safe and affordable, and have become valuable remote sensing platforms (Linna et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Surface Soil Moisture by a Multi‐Elevation UAV‐Based Ground Penetrating Radar

Cheng

Binley

et al. 2023

Water Resources Research

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The measurement of soil moisture is important for a wide range of applications, including ecosystem conservation and agricultural management. However, most traditional measurement methods, e.g., time-domain reflectometry (TDR), are unsuitable for mapping field scale variability. In this study, we propose a method that uses an unmanned aerial vehicle (UAV) to support a ground penetrating radar (GPR) system for spatial scanning investigation at different elevations above ground level. This method measures the surface reflectivity to estimate the soil moisture, exploiting the linear relationship between the ratio of the reflected and the direct wave amplitudes along with the reciprocal of GPR antenna height. This relationship is deduced in this study based on the point source assumptions of a transmitter antenna and ground reflections, which is confirmed by numerical simulation results using the gprMax software. Unlike previous air-launched GPR methods, the UAV-GPR method presented here removes the limitations of a steady transmitter power and a fixed GPR survey height and the need for calibration of antenna transfer functions and geophysical inversion calculations, and thus is simpler and more convenient for field applications.We test the method at field sites within the riparian zone and a river-island grassland adjacent to the Yangtze River. The results from the field study illustrate comparable measured soil moisture to those obtained invasively using TDR. The root mean square error (RMSE) of surface reflectivity and soil moisture values between UAV-GPR with 2 8 antenna height investigations and TDR in the grassland are 0.03 and 0.05 cm 3 /cm 3 , respectively.

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Measurement of Snow Water Equivalent Using Drone-Mounted Ultra-Wide-Band Radar

Cited by 18 publications

References 43 publications

Comparison of Snow Indices in Assessing Snow Cover Depth in Northern Kazakhstan

Comparison of Snow Indices in Assessing Snow Cover Depth in Northern Kazakhstan

Uncrewed Aerial Systems in Water Resource Management and Monitoring: A Review of Sensors, Applications, Software, and Issues

Estimation of Surface Soil Moisture by a Multi‐Elevation UAV‐Based Ground Penetrating Radar

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