- -

Python software tools for GNSS interferometric reflectometry (GNSS-IR)

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Python software tools for GNSS interferometric reflectometry (GNSS-IR)

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Martín;Luján;Anquela ...
Tamaño: 1.040Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: Martín2020_Articl ...
Tamaño: 798.0Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Martín Furones, Ángel Esteban es_ES
dc.contributor.author Luján García Muñoz, Raquel es_ES
dc.contributor.author Anquela Julián, Ana Belén es_ES
dc.date.accessioned 2021-07-09T03:31:37Z
dc.date.available 2021-07-09T03:31:37Z
dc.date.issued 2020-07-20 es_ES
dc.identifier.issn 1080-5370 es_ES
dc.identifier.uri http://hdl.handle.net/10251/169019
dc.description.abstract [EN] Global Navigation Satellite System (GNSS) interferometric reflectometry, also known as the GNSS-IR, uses data from geodetic-quality GNSS antennas to extract information about the environment surrounding the antenna. Soil moisture moni-toring is one of the most important applications of the GNSS-IR technique. This manuscript presents the main ideas and implementation decisions needed to write the Python code for software tools that transform RINEX format observation and navigation files into an appropriate format for GNSS-IR (which includes the SNR observations and the azimuth and elevation of the satellites) and to determine the reflection height and the adjusted phase and amplitude values of the interferometric wave for each individual satellite track. The main goal of the manuscript is to share the software with the scientific com-munity to introduce new users to the GNSS-IR technique. es_ES
dc.description.sponsorship The authors want to thank the staff of the Cajamar Center of Experiences, and especially Carlos Baixauli, for their support and collaboration in the Paiporta experiment. The authors also want to thank Alfred Leick and Steve Hilla for their valuable comments and suggestions. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof GPS Solutions es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject GNSS-IR reflectometry es_ES
dc.subject Python software es_ES
dc.subject Soil moisture es_ES
dc.subject Signal-to-noise ratio (SNR) es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Python software tools for GNSS interferometric reflectometry (GNSS-IR) es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10291-020-01010-0 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Martín Furones, ÁE.; Luján García Muñoz, R.; Anquela Julián, AB. (2020). Python software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solutions. 24(4):1-7. https://doi.org/10.1007/s10291-020-01010-0 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10291-020-01010-0 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 7 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 24 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\415999 es_ES
dc.contributor.funder Fundación Cajamar es_ES
dc.description.references Chen Q, Won D, Akos DM, Small EE (2016) Vegetation using GPS interferometric reflectometry: experimental results with a horizontal polarized antenna. IEEE J Select Top Appl Earth Obs Rem Sens 9(10):4771–4780. https://doi.org/10.1109/JSTARS.2016.2565687 es_ES
dc.description.references Chew CC, Small EE, Larson KM, Zavorotny VU (2014) Effects of near-surface soil moisture on GPS SNR data: development and retrieval algorithm for soil moisture. IEEE T Geosci Rem Sens 52(1):537–543. https://doi.org/10.1109/TGRS.2013.2242332 es_ES
dc.description.references Chew CC, Small EE, Larson KM, Zavorotny UZ (2015) Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE T Geosci Rem Sens 53(5):2755–2764. https://doi.org/10.1109/TGRS.2014.2364513 es_ES
dc.description.references Chew CC, Small EE, Larson KM (2016) An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. GPS Solut 20(3):525–537. https://doi.org/10.1007/s10291-015-0462-4 es_ES
dc.description.references Gurtner W, Estey L (2015) RINEX: the receiver independent exchange format version 3.03. ftp://igs.org/pub/data/format/rinex303.pdf es_ES
dc.description.references Larson KM, Small EE, Gutmann ED, Bilich AL, Axelrad A, Braun JJ (2008a) Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solut 12(3):173–177. https://doi.org/10.1007/s10291-007-0076-6 es_ES
dc.description.references Larson KM, Small EE, Gutmann ED, Bilich AL, Braun JJ, Zavorotny VU (2008b) Use of GPS receivers as a soil moisture network for water cycle studies. Geophys Res Lett 35:L24405. https://doi.org/10.1029/2008GL036013 es_ES
dc.description.references Larson KM, Gutmann E, Zavorotny VU, Braun J, Williams M, Nievinski FG (2009) Can we measure snow depth with GPS receivers? Geophys Res Lett 36:L17502. https://doi.org/10.1029/2009GL039430 es_ES
dc.description.references Larson KM, Braun JJ, Small EE, Zavorotny VU (2010) GPS multipath and its relation to near-surface soil moisture content. IEEE J Selec Top Appl Earth Obs Rem Sens 3(1):91–99. https://doi.org/10.1109/JSTARS.2009.2033612 es_ES
dc.description.references Larson KM, Nievinski FG (2013) GPS snow sensing: results from the EarthScope plate boundary observatory. GPS Solut 17(1):41–52. https://doi.org/10.1007/s10291-012-0259-7 es_ES
dc.description.references Leick A, Rapoport L, Tatarnikov D (2015) GPS satellite surveying, 4th edn. Wiley, Hoboken, p 840 es_ES
dc.description.references Martín A, Ibañez S, Baixauli C, Blanc S, Anquela AB (2020) Multi-constellation interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-24-3573-2020 es_ES
dc.description.references Nievinski GG, Larson KM (2014) An open source GPS multipath simulator in Matlab/Octave. GPS Solut 18:473–481. https://doi.org/10.1007/s10291-014-0370-z es_ES
dc.description.references Nischan T (2016) GFZRNX—RINEX GNSS data conversion and manipulation toolbox (Version 1.05). GFZ Data Serv. https://doi.org/10.5880/GFZ.1.1.2016.002 es_ES
dc.description.references Roesler C, Larson KM (2018) Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solut. https://doi.org/10.1007/s10291-018-0744-8 es_ES
dc.description.references Roussel N, Ramilien G, Frappart F, Darrozes J, Gay A, Biancale R, Striebig N, Hanquiez V, Bertin X, Allain A (2015) Sea level monitoring and sea estimate using a single geodetic receiver. Remote Sens Environ 171:261–277. https://doi.org/10.1016/j.rse.2015.10.011 es_ES
dc.description.references Roussel N, Frappart F, Ramillien G, Darroes J, Baup F, Lestarquit L, Ha MC (2016) Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2º to 70º. IEEE J Selec Top Appl Earth Obs Rem Sens 9(10):4781–4794. https://doi.org/10.1109/JSTARS.2016.2537847 es_ES
dc.description.references Sanz J, Juan JM, Hernández-Pajares M (2013) GNSS data processing. Volume I: fundamentals and algorithms. European Space Agency Communications, 223 pp es_ES
dc.description.references Small EE, Larson KM, Chew CC, Dong J, Ochsner TE (2016) Validation of GPS-IR soil moisture retrievals: comparison of different algorithms to remove vegetation effects. IEEE J Selec Top Appl Earth Obs Rem Sens 9(10):4759–4770. https://doi.org/10.1109/JSTARS.2015.2504527 es_ES
dc.description.references Vey S, Güntner A, Wickert J, Blume T, Ramatschi M (2016) Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. GPS Solut 20:641–654. https://doi.org/10.1007/s10291-015-0474-0 es_ES
dc.description.references Wan W, Larson KM, Small EE, Chew CC, Braun JJ (2015) Using geodetic GPS receivers to measure vegetation water content. GPS Solut 19:237–248. https://doi.org/10.1007/s10291-014-0383-7 es_ES
dc.description.references Zhang S, Roussel N, Boniface K, Ha MC, Frappart F, Darrozes J, Baup F, Calvet JC (2017) Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop. Hydrol Earth Syst Sci 21:4767–4784. https://doi.org/10.5194/hess-21-4767-2017 es_ES
dc.subject.ods 02.- Poner fin al hambre, conseguir la seguridad alimentaria y una mejor nutrición, y promover la agricultura sostenible es_ES
dc.subject.ods 15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica es_ES
dc.subject.ods 06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem