A new drone-borne GPR for soil moisture mapping
In this study, we set up a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping. The whole radar system weighs 1.5 kg and consists of a handheld vector network analyzer (VNA) working as frequency domain radar, a lightweight hybrid horn-dipole antenna covering a wide frequency ran...
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| Veröffentlicht in: | Remote sensing of environment 2019-12, Vol.235, p.111456, Article 111456 |
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| creator | Wu, Kaijun Rodriguez, Gabriela Arambulo Zajc, Marjana Jacquemin, Elodie Clément, Michiels De Coster, Albéric Lambot, Sébastien |
| description | In this study, we set up a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping. The whole radar system weighs 1.5 kg and consists of a handheld vector network analyzer (VNA) working as frequency domain radar, a lightweight hybrid horn-dipole antenna covering a wide frequency range (250–2800 MHz), a GPS for positioning, a microcomputer with the controlling application, and a smartphone for remote control. Soil moisture is derived from the radar data using full-wave inverse modeling based on the radar equation of Lambot et al. and multilayered media Green's functions. The inversion is performed in the time domain and focuses on the surface reflection. The antenna-drone system is characterized by global reflection and transmission functions which are determined through a calibration procedure. We performed drone-GPR measurements over three different agricultural fields in the loess belt region of Belgium. In this study, we used the 500–700 MHz range to avoid soil surface roughness effects and to focus on the top 10–20 cm of the soil. These fields present a range of landform conditions leading to specific soil moisture distributions. The soil moisture maps were constructed from the local measurements using kriging. The obtained soil moisture maps are in good agreement with the topographical conditions of the fields and aerial orthophotography observations. These results demonstrated the potential and benefits of drone-GPR for fast, high-resolution mapping of soil moisture at the field scale, and to support, e.g., precision agriculture and environmental monitoring.
•We present a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping.•The radar system consists of a lightweight vector analyzer (VNA) combined with a hybrid horn-dipole antenna.•The radar signal is processed using full-wave inversion.•Soil moisture mapping results over 3 test sites demonstrated the protential of the technique. |
| doi_str_mv | 10.1016/j.rse.2019.111456 |
| format | Article |
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•We present a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping.•The radar system consists of a lightweight vector analyzer (VNA) combined with a hybrid horn-dipole antenna.•The radar signal is processed using full-wave inversion.•Soil moisture mapping results over 3 test sites demonstrated the protential of the technique.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2019.111456</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Agricultural land ; Agriculture ; Calibration ; Dipole antennas ; Drone ; Drone aircraft ; Environmental monitoring ; Frequency ranges ; Full-wave inversion ; Global positioning systems ; GPR ; GPS ; Green's function ; Green's functions ; Ground penetrating radar ; Kriging ; Land use ; Landforms ; Loess ; Mapping ; Network analysers ; Orthophotography ; Precision farming ; Radar ; Radar data ; Radar equipment ; Reflection ; Remote control ; Roughness effects ; Satellite navigation systems ; Soil mapping ; Soil moisture ; Soil moisture mapping ; Soils ; Surface roughness ; Surface roughness effects</subject><ispartof>Remote sensing of environment, 2019-12, Vol.235, p.111456, Article 111456</ispartof><rights>2019</rights><rights>Copyright Elsevier BV Dec 15, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-340a04f350a0ba4301eb3f8d8d31afdb7cbe42718a787710b276482336a0d5f03</citedby><cites>FETCH-LOGICAL-c325t-340a04f350a0ba4301eb3f8d8d31afdb7cbe42718a787710b276482336a0d5f03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2019.111456$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Wu, Kaijun</creatorcontrib><creatorcontrib>Rodriguez, Gabriela Arambulo</creatorcontrib><creatorcontrib>Zajc, Marjana</creatorcontrib><creatorcontrib>Jacquemin, Elodie</creatorcontrib><creatorcontrib>Clément, Michiels</creatorcontrib><creatorcontrib>De Coster, Albéric</creatorcontrib><creatorcontrib>Lambot, Sébastien</creatorcontrib><title>A new drone-borne GPR for soil moisture mapping</title><title>Remote sensing of environment</title><description>In this study, we set up a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping. The whole radar system weighs 1.5 kg and consists of a handheld vector network analyzer (VNA) working as frequency domain radar, a lightweight hybrid horn-dipole antenna covering a wide frequency range (250–2800 MHz), a GPS for positioning, a microcomputer with the controlling application, and a smartphone for remote control. Soil moisture is derived from the radar data using full-wave inverse modeling based on the radar equation of Lambot et al. and multilayered media Green's functions. The inversion is performed in the time domain and focuses on the surface reflection. The antenna-drone system is characterized by global reflection and transmission functions which are determined through a calibration procedure. We performed drone-GPR measurements over three different agricultural fields in the loess belt region of Belgium. In this study, we used the 500–700 MHz range to avoid soil surface roughness effects and to focus on the top 10–20 cm of the soil. These fields present a range of landform conditions leading to specific soil moisture distributions. The soil moisture maps were constructed from the local measurements using kriging. The obtained soil moisture maps are in good agreement with the topographical conditions of the fields and aerial orthophotography observations. These results demonstrated the potential and benefits of drone-GPR for fast, high-resolution mapping of soil moisture at the field scale, and to support, e.g., precision agriculture and environmental monitoring.
•We present a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping.•The radar system consists of a lightweight vector analyzer (VNA) combined with a hybrid horn-dipole antenna.•The radar signal is processed using full-wave inversion.•Soil moisture mapping results over 3 test sites demonstrated the protential of the technique.</description><subject>Agricultural land</subject><subject>Agriculture</subject><subject>Calibration</subject><subject>Dipole antennas</subject><subject>Drone</subject><subject>Drone aircraft</subject><subject>Environmental monitoring</subject><subject>Frequency ranges</subject><subject>Full-wave inversion</subject><subject>Global positioning systems</subject><subject>GPR</subject><subject>GPS</subject><subject>Green's function</subject><subject>Green's functions</subject><subject>Ground penetrating radar</subject><subject>Kriging</subject><subject>Land use</subject><subject>Landforms</subject><subject>Loess</subject><subject>Mapping</subject><subject>Network analysers</subject><subject>Orthophotography</subject><subject>Precision farming</subject><subject>Radar</subject><subject>Radar data</subject><subject>Radar equipment</subject><subject>Reflection</subject><subject>Remote control</subject><subject>Roughness effects</subject><subject>Satellite navigation systems</subject><subject>Soil mapping</subject><subject>Soil moisture</subject><subject>Soil moisture mapping</subject><subject>Soils</subject><subject>Surface roughness</subject><subject>Surface roughness effects</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kMFKw0AQhhdRsFYfwFvAc9KZ7Ca7xVMpWoWCInpeNslENrTZuJsqvn23xLNz-S__NzN8jN0iZAhYLrrMB8pywGWGiKIoz9gMlVymIEGcsxkAF6nIC3nJrkLoALBQEmdssUp6-kka73pKK-d7Sjavb0nrfBKc3SV7Z8N48JTszTDY_vOaXbRmF-jmL-fs4_Hhff2Ubl82z-vVNq15XowpF2BAtLyIURnBAanirWpUw9G0TSXrikQuURmppESoclkKlXNeGmiKFvic3U17B---DhRG3bmD7-NJHVsAeRwVWzi1au9C8NTqwdu98b8aQZ-86E5HL_rkRU9eInM_MRTf_7bkdagt9TU11lM96sbZf-gj_cxoHA</recordid><startdate>20191215</startdate><enddate>20191215</enddate><creator>Wu, Kaijun</creator><creator>Rodriguez, Gabriela Arambulo</creator><creator>Zajc, Marjana</creator><creator>Jacquemin, Elodie</creator><creator>Clément, Michiels</creator><creator>De Coster, Albéric</creator><creator>Lambot, Sébastien</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope></search><sort><creationdate>20191215</creationdate><title>A new drone-borne GPR for soil moisture mapping</title><author>Wu, Kaijun ; Rodriguez, Gabriela Arambulo ; Zajc, Marjana ; Jacquemin, Elodie ; Clément, Michiels ; De Coster, Albéric ; Lambot, Sébastien</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-340a04f350a0ba4301eb3f8d8d31afdb7cbe42718a787710b276482336a0d5f03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Agricultural land</topic><topic>Agriculture</topic><topic>Calibration</topic><topic>Dipole antennas</topic><topic>Drone</topic><topic>Drone aircraft</topic><topic>Environmental monitoring</topic><topic>Frequency ranges</topic><topic>Full-wave inversion</topic><topic>Global positioning systems</topic><topic>GPR</topic><topic>GPS</topic><topic>Green's function</topic><topic>Green's functions</topic><topic>Ground penetrating radar</topic><topic>Kriging</topic><topic>Land use</topic><topic>Landforms</topic><topic>Loess</topic><topic>Mapping</topic><topic>Network analysers</topic><topic>Orthophotography</topic><topic>Precision farming</topic><topic>Radar</topic><topic>Radar data</topic><topic>Radar equipment</topic><topic>Reflection</topic><topic>Remote control</topic><topic>Roughness effects</topic><topic>Satellite navigation systems</topic><topic>Soil mapping</topic><topic>Soil moisture</topic><topic>Soil moisture mapping</topic><topic>Soils</topic><topic>Surface roughness</topic><topic>Surface roughness effects</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Kaijun</creatorcontrib><creatorcontrib>Rodriguez, Gabriela Arambulo</creatorcontrib><creatorcontrib>Zajc, Marjana</creatorcontrib><creatorcontrib>Jacquemin, Elodie</creatorcontrib><creatorcontrib>Clément, Michiels</creatorcontrib><creatorcontrib>De Coster, Albéric</creatorcontrib><creatorcontrib>Lambot, Sébastien</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Kaijun</au><au>Rodriguez, Gabriela Arambulo</au><au>Zajc, Marjana</au><au>Jacquemin, Elodie</au><au>Clément, Michiels</au><au>De Coster, Albéric</au><au>Lambot, Sébastien</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new drone-borne GPR for soil moisture mapping</atitle><jtitle>Remote sensing of environment</jtitle><date>2019-12-15</date><risdate>2019</risdate><volume>235</volume><spage>111456</spage><pages>111456-</pages><artnum>111456</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>In this study, we set up a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping. The whole radar system weighs 1.5 kg and consists of a handheld vector network analyzer (VNA) working as frequency domain radar, a lightweight hybrid horn-dipole antenna covering a wide frequency range (250–2800 MHz), a GPS for positioning, a microcomputer with the controlling application, and a smartphone for remote control. Soil moisture is derived from the radar data using full-wave inverse modeling based on the radar equation of Lambot et al. and multilayered media Green's functions. The inversion is performed in the time domain and focuses on the surface reflection. The antenna-drone system is characterized by global reflection and transmission functions which are determined through a calibration procedure. We performed drone-GPR measurements over three different agricultural fields in the loess belt region of Belgium. In this study, we used the 500–700 MHz range to avoid soil surface roughness effects and to focus on the top 10–20 cm of the soil. These fields present a range of landform conditions leading to specific soil moisture distributions. The soil moisture maps were constructed from the local measurements using kriging. The obtained soil moisture maps are in good agreement with the topographical conditions of the fields and aerial orthophotography observations. These results demonstrated the potential and benefits of drone-GPR for fast, high-resolution mapping of soil moisture at the field scale, and to support, e.g., precision agriculture and environmental monitoring.
•We present a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping.•The radar system consists of a lightweight vector analyzer (VNA) combined with a hybrid horn-dipole antenna.•The radar signal is processed using full-wave inversion.•Soil moisture mapping results over 3 test sites demonstrated the protential of the technique.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2019.111456</doi></addata></record> |
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| subjects | Agricultural land Agriculture Calibration Dipole antennas Drone Drone aircraft Environmental monitoring Frequency ranges Full-wave inversion Global positioning systems GPR GPS Green's function Green's functions Ground penetrating radar Kriging Land use Landforms Loess Mapping Network analysers Orthophotography Precision farming Radar Radar data Radar equipment Reflection Remote control Roughness effects Satellite navigation systems Soil mapping Soil moisture Soil moisture mapping Soils Surface roughness Surface roughness effects |
| title | A new drone-borne GPR for soil moisture mapping |
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