- -

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 completo del ítem

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/169019

Ficheros en el ítem

Thumbnail
Nombre: Martín;Luján;Anquela ...
Tamaño: 1.040Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir/Preview
[Cerrado]
Nombre: Martín2020_Articl ...
Tamaño: 798.0Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor

Metadatos del ítem

Título: Python software tools for GNSS interferometric reflectometry (GNSS-IR)
Autor: Martín Furones, Ángel Esteban Luján García Muñoz, Raquel Anquela Julián, Ana Belén
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria
Fecha difusión:
Resumen:
[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. ...[+]
Palabras clave: GNSS-IR reflectometry , Python software , Soil moisture , Signal-to-noise ratio (SNR)
Derechos de uso: Reserva de todos los derechos
Fuente:
GPS Solutions. (issn: 1080-5370 )
DOI: 10.1007/s10291-020-01010-0
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s10291-020-01010-0
Agradecimientos:
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 ...[+]
Tipo: Artículo

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

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

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 [+]
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

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

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

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

Gurtner W, Estey L (2015) RINEX: the receiver independent exchange format version 3.03. ftp://igs.org/pub/data/format/rinex303.pdf

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

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

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

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

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

Leick A, Rapoport L, Tatarnikov D (2015) GPS satellite surveying, 4th edn. Wiley, Hoboken, p 840

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

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

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

Roesler C, Larson KM (2018) Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solut. https://doi.org/10.1007/s10291-018-0744-8

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

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

Sanz J, Juan JM, Hernández-Pajares M (2013) GNSS data processing. Volume I: fundamentals and algorithms. European Space Agency Communications, 223 pp

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

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

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

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

[-]

recommendations

 

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

Mostrar el registro completo del ítem