Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils

Soil surface roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the surface roughness sample size required in synthetic aperture radar applications and to provide some guidelines on roughness characterization in agricultural soil...

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Autores: Martínez de Aguirre Escobar, Alejandro, Álvarez-Mozos, Jesús, Lievens, Hans, Verhoest, Niko E. C., Giménez Díaz, Rafael
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/32129
Acceso en línea:https://hdl.handle.net/2454/32129
Access Level:acceso abierto
Palabra clave:Agricultural soils
Backscatter models
Surface roughness
Synthetic aperture radar (SAR)
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spelling Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soilsMartínez de Aguirre Escobar, AlejandroÁlvarez-Mozos, JesúsLievens, HansVerhoest, Niko E. C.Giménez Díaz, RafaelAgricultural soilsBackscatter modelsSurface roughnessSynthetic aperture radar (SAR)Soil surface roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the surface roughness sample size required in synthetic aperture radar applications and to provide some guidelines on roughness characterization in agricultural soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with soil moisture and surface roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of roughness parameters s and l depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two roughness parameters. A minimum of 10-15 profiles can be considered sufficient for an accurate determination of s, while 20 profiles might still be not enough for accurately estimating l. The correlation analysis revealed a clear sensitivity of backscatter to surface roughness. For sample sizes >15 profiles, R values were as high as 0.6 for s and ~0.35 for l, while for smaller sample sizes R values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of l.This work was supported by the Spanish Ministry of Economy and Competitiveness through scholarship under Grant BES-2012-054521 and through MINECO/FEDER, EU, under Project CGL2011-24336, Project CGL2015-64284-C2-1-R, and Project CGL2016-75217-R.IEEELanda Ingeniaritza eta ProiektuakInstitute on Innovation and Sustainable Development in Food Chain - ISFOODProyectos e Ingeniería Rural2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2454/32129reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/MICINN//CGL2011-24336info:eu-repo/grantAgreement/MINECO//CGL2015-64284-C2-1-Rinfo:eu-repo/grantAgreement/ES/1PE/CGL2016-75217© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/321292026-06-17T12:41:47Z
dc.title.none.fl_str_mv Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
title Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
spellingShingle Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
Martínez de Aguirre Escobar, Alejandro
Agricultural soils
Backscatter models
Surface roughness
Synthetic aperture radar (SAR)
title_short Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
title_full Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
title_fullStr Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
title_full_unstemmed Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
title_sort Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
dc.creator.none.fl_str_mv Martínez de Aguirre Escobar, Alejandro
Álvarez-Mozos, Jesús
Lievens, Hans
Verhoest, Niko E. C.
Giménez Díaz, Rafael
author Martínez de Aguirre Escobar, Alejandro
author_facet Martínez de Aguirre Escobar, Alejandro
Álvarez-Mozos, Jesús
Lievens, Hans
Verhoest, Niko E. C.
Giménez Díaz, Rafael
author_role author
author2 Álvarez-Mozos, Jesús
Lievens, Hans
Verhoest, Niko E. C.
Giménez Díaz, Rafael
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Landa Ingeniaritza eta Proiektuak
Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Proyectos e Ingeniería Rural
dc.subject.none.fl_str_mv Agricultural soils
Backscatter models
Surface roughness
Synthetic aperture radar (SAR)
topic Agricultural soils
Backscatter models
Surface roughness
Synthetic aperture radar (SAR)
description Soil surface roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the surface roughness sample size required in synthetic aperture radar applications and to provide some guidelines on roughness characterization in agricultural soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with soil moisture and surface roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of roughness parameters s and l depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two roughness parameters. A minimum of 10-15 profiles can be considered sufficient for an accurate determination of s, while 20 profiles might still be not enough for accurately estimating l. The correlation analysis revealed a clear sensitivity of backscatter to surface roughness. For sample sizes >15 profiles, R values were as high as 0.6 for s and ~0.35 for l, while for smaller sample sizes R values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of l.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/32129
url https://hdl.handle.net/2454/32129
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN//CGL2011-24336
info:eu-repo/grantAgreement/MINECO//CGL2015-64284-C2-1-R
info:eu-repo/grantAgreement/ES/1PE/CGL2016-75217
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
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