Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data

Autores
Barber, Matias Ernesto; Grings, Francisco Matias; Perna, Pablo Alejandro; Piscitelli, Marcela; Maas, Martín Daniel; Bruscantini, Cintia Alicia; Jacobo Berlles, Julio César Alberto; Karszenbaum, Haydee
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
—Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh’s model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimizationbased procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.
Fil: Barber, Matias Ernesto. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Grings, Francisco Matias. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Perna, Pablo Alejandro. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Piscitelli, Marcela. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomia; Argentina
Fil: Maas, Martín Daniel. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Bruscantini, Cintia Alicia. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Jacobo Berlles, Julio César Alberto. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
Fil: Karszenbaum, Haydee. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Materia
Soil Moisture
Radar Applications
Bayesian Methods
Synthetic Aperture Radar
Inverse Problems
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/19527
Acceder
id CONICETDig_09f75a940c48f813762bd7480f00ea83
oai_identifier_str oai:ri.conicet.gov.ar:11336/19527
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR dataBarber, Matias ErnestoGrings, Francisco MatiasPerna, Pablo AlejandroPiscitelli, MarcelaMaas, Martín DanielBruscantini, Cintia AliciaJacobo Berlles, Julio César AlbertoKarszenbaum, HaydeeSoil MoistureRadar ApplicationsBayesian MethodsSynthetic Aperture RadarInverse Problemshttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1—Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh’s model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimizationbased procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.Fil: Barber, Matias Ernesto. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Grings, Francisco Matias. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Perna, Pablo Alejandro. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Piscitelli, Marcela. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomia; ArgentinaFil: Maas, Martín Daniel. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Bruscantini, Cintia Alicia. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Jacobo Berlles, Julio César Alberto. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Karszenbaum, Haydee. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaInstitute of Electrical and Electronics Engineers2012-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/19527Barber, Matias Ernesto; Grings, Francisco Matias; Perna, Pablo Alejandro; Piscitelli, Marcela; Maas, Martín Daniel; et al.; Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 5; 3; 6-2012; 942-9511939-1404CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2012.2191266info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6227352/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:12:04Zoai:ri.conicet.gov.ar:11336/19527instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:12:04.493CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
title Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
spellingShingle Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
Barber, Matias Ernesto
Soil Moisture
Radar Applications
Bayesian Methods
Synthetic Aperture Radar
Inverse Problems
title_short Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
title_full Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
title_fullStr Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
title_full_unstemmed Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
title_sort Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
dc.creator.none.fl_str_mv Barber, Matias Ernesto
Grings, Francisco Matias
Perna, Pablo Alejandro
Piscitelli, Marcela
Maas, Martín Daniel
Bruscantini, Cintia Alicia
Jacobo Berlles, Julio César Alberto
Karszenbaum, Haydee
author Barber, Matias Ernesto
author_facet Barber, Matias Ernesto
Grings, Francisco Matias
Perna, Pablo Alejandro
Piscitelli, Marcela
Maas, Martín Daniel
Bruscantini, Cintia Alicia
Jacobo Berlles, Julio César Alberto
Karszenbaum, Haydee
author_role author
author2 Grings, Francisco Matias
Perna, Pablo Alejandro
Piscitelli, Marcela
Maas, Martín Daniel
Bruscantini, Cintia Alicia
Jacobo Berlles, Julio César Alberto
Karszenbaum, Haydee
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Soil Moisture
Radar Applications
Bayesian Methods
Synthetic Aperture Radar
Inverse Problems
topic Soil Moisture
Radar Applications
Bayesian Methods
Synthetic Aperture Radar
Inverse Problems
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv —Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh’s model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimizationbased procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.
Fil: Barber, Matias Ernesto. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Grings, Francisco Matias. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Perna, Pablo Alejandro. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Piscitelli, Marcela. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomia; Argentina
Fil: Maas, Martín Daniel. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Bruscantini, Cintia Alicia. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Jacobo Berlles, Julio César Alberto. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
Fil: Karszenbaum, Haydee. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
description —Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh’s model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimizationbased procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.
publishDate 2012
dc.date.none.fl_str_mv 2012-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/19527
Barber, Matias Ernesto; Grings, Francisco Matias; Perna, Pablo Alejandro; Piscitelli, Marcela; Maas, Martín Daniel; et al.; Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 5; 3; 6-2012; 942-951
1939-1404
CONICET Digital
CONICET
url http://hdl.handle.net/11336/19527
identifier_str_mv Barber, Matias Ernesto; Grings, Francisco Matias; Perna, Pablo Alejandro; Piscitelli, Marcela; Maas, Martín Daniel; et al.; Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 5; 3; 6-2012; 942-951
1939-1404
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2012.2191266
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6227352/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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