Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm

Autores
Barber, Matias Ernesto; Bruscantini, Cintia Alicia; Grings, Francisco Matias; Karszenbaum, Haydee
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper focused on exploiting remotely sensed active and passive observations over agricultural fields for soil moisture retrieval purposes. Co-polarized backscattering coefficients HH and VV and V-polarized brightness temperature TbV measurements were merged onto a Bayesian algorithm to enhance field-based retrieval estimates. The Bayesian algorithm relies on the use of active SAR to constrain passive information. It is assumed that observations are representative of an extent involving field sizes of about 800 m by 800 m, disregarding the scaling issues between the high resolution SAR pixel and the coarse resolution passive pixel. The integral equation model with multiple scattering at second order (IEM2M) and the ω-τ model were used as forward models for the backscattering coefficients and for the V-polarized brightness temperature, respectively. The Bayesian algorithm was assessed using datasets from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEx12). Such datasets are representative of contrasting soil conditions since soil moisture spanned almost its whole feasible range from 0.10 to 0.40 cm3/cm3, at different observation geometries with incidence angles ranging from 35° to 55°. Also, the fairly large amount of measurements (97) made the dataset complete for assessment purposes. Soil moisture variability at field scale and dielectric probe error were accounted for in the comparison between retrieved estimates and in situ measurements. Performance metrics were used to quantify the agreement of the retrieval methodology to in situ information, and to assess the improvement in the combined methodology with respect to the single ones (active or passive). Overall, the root mean squared error (RMSE) showed an improvement from 0.08 to 0.11 cm3/cm3 (only active) or 0.03-0.12 cm3/cm3 (only passive, after bias correction) to 0.06-0.10 cm3/cm3 (combined), thus, demonstrating the potential of such combined soil moisture estimates. When analyzed each field separately, RMSE is less than 0.07 cm3/cm3 and correlation coefficient r is greater than 0.6 for most of the fields.
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: 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: 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: 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
Bayes Procedures
Inverse Problems
Moisture
Radar Applications
Remote Sensing
Rough Surfaces
Soil Measurements
Synthetic Aperture Radar (Sar)
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:11336/21734
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/21734
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval AlgorithmBarber, Matias ErnestoBruscantini, Cintia AliciaGrings, Francisco MatiasKarszenbaum, HaydeeBayes ProceduresInverse ProblemsMoistureRadar ApplicationsRemote SensingRough SurfacesSoil MeasurementsSynthetic Aperture Radar (Sar)https://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1This paper focused on exploiting remotely sensed active and passive observations over agricultural fields for soil moisture retrieval purposes. Co-polarized backscattering coefficients HH and VV and V-polarized brightness temperature TbV measurements were merged onto a Bayesian algorithm to enhance field-based retrieval estimates. The Bayesian algorithm relies on the use of active SAR to constrain passive information. It is assumed that observations are representative of an extent involving field sizes of about 800 m by 800 m, disregarding the scaling issues between the high resolution SAR pixel and the coarse resolution passive pixel. The integral equation model with multiple scattering at second order (IEM2M) and the ω-τ model were used as forward models for the backscattering coefficients and for the V-polarized brightness temperature, respectively. The Bayesian algorithm was assessed using datasets from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEx12). Such datasets are representative of contrasting soil conditions since soil moisture spanned almost its whole feasible range from 0.10 to 0.40 cm3/cm3, at different observation geometries with incidence angles ranging from 35° to 55°. Also, the fairly large amount of measurements (97) made the dataset complete for assessment purposes. Soil moisture variability at field scale and dielectric probe error were accounted for in the comparison between retrieved estimates and in situ measurements. Performance metrics were used to quantify the agreement of the retrieval methodology to in situ information, and to assess the improvement in the combined methodology with respect to the single ones (active or passive). Overall, the root mean squared error (RMSE) showed an improvement from 0.08 to 0.11 cm3/cm3 (only active) or 0.03-0.12 cm3/cm3 (only passive, after bias correction) to 0.06-0.10 cm3/cm3 (combined), thus, demonstrating the potential of such combined soil moisture estimates. When analyzed each field separately, RMSE is less than 0.07 cm3/cm3 and correlation coefficient r is greater than 0.6 for most of the fields.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: 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: 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: 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 Engineers2016-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/21734Barber, Matias Ernesto; Bruscantini, Cintia Alicia; Grings, Francisco Matias; Karszenbaum, Haydee; Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 9; 12; 10-2016; 5449-54601939-1404CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2016.2611491info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7583692/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 Técnicas2025-09-29T10:17:57Zoai:ri.conicet.gov.ar:11336/21734instacron: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 10:17:57.346CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
title Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
spellingShingle Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
Barber, Matias Ernesto
Bayes Procedures
Inverse Problems
Moisture
Radar Applications
Remote Sensing
Rough Surfaces
Soil Measurements
Synthetic Aperture Radar (Sar)
title_short Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
title_full Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
title_fullStr Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
title_full_unstemmed Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
title_sort Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm
dc.creator.none.fl_str_mv Barber, Matias Ernesto
Bruscantini, Cintia Alicia
Grings, Francisco Matias
Karszenbaum, Haydee
author Barber, Matias Ernesto
author_facet Barber, Matias Ernesto
Bruscantini, Cintia Alicia
Grings, Francisco Matias
Karszenbaum, Haydee
author_role author
author2 Bruscantini, Cintia Alicia
Grings, Francisco Matias
Karszenbaum, Haydee
author2_role author
author
author
dc.subject.none.fl_str_mv Bayes Procedures
Inverse Problems
Moisture
Radar Applications
Remote Sensing
Rough Surfaces
Soil Measurements
Synthetic Aperture Radar (Sar)
topic Bayes Procedures
Inverse Problems
Moisture
Radar Applications
Remote Sensing
Rough Surfaces
Soil Measurements
Synthetic Aperture Radar (Sar)
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper focused on exploiting remotely sensed active and passive observations over agricultural fields for soil moisture retrieval purposes. Co-polarized backscattering coefficients HH and VV and V-polarized brightness temperature TbV measurements were merged onto a Bayesian algorithm to enhance field-based retrieval estimates. The Bayesian algorithm relies on the use of active SAR to constrain passive information. It is assumed that observations are representative of an extent involving field sizes of about 800 m by 800 m, disregarding the scaling issues between the high resolution SAR pixel and the coarse resolution passive pixel. The integral equation model with multiple scattering at second order (IEM2M) and the ω-τ model were used as forward models for the backscattering coefficients and for the V-polarized brightness temperature, respectively. The Bayesian algorithm was assessed using datasets from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEx12). Such datasets are representative of contrasting soil conditions since soil moisture spanned almost its whole feasible range from 0.10 to 0.40 cm3/cm3, at different observation geometries with incidence angles ranging from 35° to 55°. Also, the fairly large amount of measurements (97) made the dataset complete for assessment purposes. Soil moisture variability at field scale and dielectric probe error were accounted for in the comparison between retrieved estimates and in situ measurements. Performance metrics were used to quantify the agreement of the retrieval methodology to in situ information, and to assess the improvement in the combined methodology with respect to the single ones (active or passive). Overall, the root mean squared error (RMSE) showed an improvement from 0.08 to 0.11 cm3/cm3 (only active) or 0.03-0.12 cm3/cm3 (only passive, after bias correction) to 0.06-0.10 cm3/cm3 (combined), thus, demonstrating the potential of such combined soil moisture estimates. When analyzed each field separately, RMSE is less than 0.07 cm3/cm3 and correlation coefficient r is greater than 0.6 for most of the fields.
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: 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: 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: 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 This paper focused on exploiting remotely sensed active and passive observations over agricultural fields for soil moisture retrieval purposes. Co-polarized backscattering coefficients HH and VV and V-polarized brightness temperature TbV measurements were merged onto a Bayesian algorithm to enhance field-based retrieval estimates. The Bayesian algorithm relies on the use of active SAR to constrain passive information. It is assumed that observations are representative of an extent involving field sizes of about 800 m by 800 m, disregarding the scaling issues between the high resolution SAR pixel and the coarse resolution passive pixel. The integral equation model with multiple scattering at second order (IEM2M) and the ω-τ model were used as forward models for the backscattering coefficients and for the V-polarized brightness temperature, respectively. The Bayesian algorithm was assessed using datasets from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEx12). Such datasets are representative of contrasting soil conditions since soil moisture spanned almost its whole feasible range from 0.10 to 0.40 cm3/cm3, at different observation geometries with incidence angles ranging from 35° to 55°. Also, the fairly large amount of measurements (97) made the dataset complete for assessment purposes. Soil moisture variability at field scale and dielectric probe error were accounted for in the comparison between retrieved estimates and in situ measurements. Performance metrics were used to quantify the agreement of the retrieval methodology to in situ information, and to assess the improvement in the combined methodology with respect to the single ones (active or passive). Overall, the root mean squared error (RMSE) showed an improvement from 0.08 to 0.11 cm3/cm3 (only active) or 0.03-0.12 cm3/cm3 (only passive, after bias correction) to 0.06-0.10 cm3/cm3 (combined), thus, demonstrating the potential of such combined soil moisture estimates. When analyzed each field separately, RMSE is less than 0.07 cm3/cm3 and correlation coefficient r is greater than 0.6 for most of the fields.
publishDate 2016
dc.date.none.fl_str_mv 2016-10
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/11336/21734
Barber, Matias Ernesto; Bruscantini, Cintia Alicia; Grings, Francisco Matias; Karszenbaum, Haydee; Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 9; 12; 10-2016; 5449-5460
1939-1404
CONICET Digital
CONICET
url http://hdl.handle.net/11336/21734
identifier_str_mv Barber, Matias Ernesto; Bruscantini, Cintia Alicia; Grings, Francisco Matias; Karszenbaum, Haydee; Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 9; 12; 10-2016; 5449-5460
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.2016.2611491
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7583692/
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
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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