Measurement of snow water equivalent using drone-mounted ultra-wide-band radar
The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely e...
Published in: | Remote Sensing |
---|---|
Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10037/22682 https://doi.org/10.3390/rs13132610 |
id |
ftunivtroemsoe:oai:munin.uit.no:10037/22682 |
---|---|
record_format |
openpolar |
spelling |
ftunivtroemsoe:oai:munin.uit.no:10037/22682 2023-05-15T14:25:30+02:00 Measurement of snow water equivalent using drone-mounted ultra-wide-band radar Jenssen, Rolf Ole R. Jacobsen, Svein Ketil 2021-07-02 https://hdl.handle.net/10037/22682 https://doi.org/10.3390/rs13132610 eng eng MDPI Remote Sensing info:eu-repo/grantAgreement/RCN/INTPART/261786/Norway/Arctic Field Summer Schools: Norway-Canada-USA collaboration// Jenssen, Jacobsen. Measurement of snow water equivalent using drone-mounted ultra-wide-band radar. Remote Sensing. 2021;13(13):1-17 FRIDAID 1926285 doi:10.3390/rs13132610 2072-4292 https://hdl.handle.net/10037/22682 openAccess Copyright 2021 The Author(s) VDP::Technology: 500 VDP::Teknologi: 500 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.3390/rs13132610 2021-10-06T22:54:18Z The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R > 0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm 3 . This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique. Article in Journal/Newspaper Arctic University of Tromsø: Munin Open Research Archive Remote Sensing 13 13 2610 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Technology: 500 VDP::Teknologi: 500 |
spellingShingle |
VDP::Technology: 500 VDP::Teknologi: 500 Jenssen, Rolf Ole R. Jacobsen, Svein Ketil Measurement of snow water equivalent using drone-mounted ultra-wide-band radar |
topic_facet |
VDP::Technology: 500 VDP::Teknologi: 500 |
description |
The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R > 0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm 3 . This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique. |
format |
Article in Journal/Newspaper |
author |
Jenssen, Rolf Ole R. Jacobsen, Svein Ketil |
author_facet |
Jenssen, Rolf Ole R. Jacobsen, Svein Ketil |
author_sort |
Jenssen, Rolf Ole R. |
title |
Measurement of snow water equivalent using drone-mounted ultra-wide-band radar |
title_short |
Measurement of snow water equivalent using drone-mounted ultra-wide-band radar |
title_full |
Measurement of snow water equivalent using drone-mounted ultra-wide-band radar |
title_fullStr |
Measurement of snow water equivalent using drone-mounted ultra-wide-band radar |
title_full_unstemmed |
Measurement of snow water equivalent using drone-mounted ultra-wide-band radar |
title_sort |
measurement of snow water equivalent using drone-mounted ultra-wide-band radar |
publisher |
MDPI |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/22682 https://doi.org/10.3390/rs13132610 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Remote Sensing info:eu-repo/grantAgreement/RCN/INTPART/261786/Norway/Arctic Field Summer Schools: Norway-Canada-USA collaboration// Jenssen, Jacobsen. Measurement of snow water equivalent using drone-mounted ultra-wide-band radar. Remote Sensing. 2021;13(13):1-17 FRIDAID 1926285 doi:10.3390/rs13132610 2072-4292 https://hdl.handle.net/10037/22682 |
op_rights |
openAccess Copyright 2021 The Author(s) |
op_doi |
https://doi.org/10.3390/rs13132610 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
13 |
container_start_page |
2610 |
_version_ |
1766297883409121280 |