Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices

Autores
Aguirre, Cesar Augusto; Rondan, Guillermo Antonio; Sedano, Carlos German; Cardozo, Macarena; Rut, Tatiana
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The estimation of soil moisture contents on crop growth is the most important variable and, in many cases, determines yields. Many simulation models predict this variable by considering atmospheric conditions, crop water needs, and soil characteristics. In general, these models simulate soil moisture content for an aver-age plant in the cultivated field without taking into account spatial variability. Relief is one of the characteris-tics that can explain this variability, so obtaining maps of soil moisture in the root zone has been addressed in this work through spatial interpolations obtained in sampling campaigns, together with the application of the Bayesian data fusion technique. In this work, soil moisture measurements were carried out in the first half of 2021 and the second half of 2022. With these data, several topographic indices were analyzed, finding that the inverse of the topographic wetness index and the digital terrain model best explain the spatial variability of soil moisture. Subsequently, data fusion techniques were applied by combining the results of the Ordinary Kriging interpolation method and these topographic indices. An analysis of the estimation errors was carried out using an independent set of data that did not participate in the spatial interpolations. It is observed that the application of the Bayesian data fusion method, considering these topographic indices, improves the soil mois-ture estimates compared to the use of the Ordinary Kriging interpolation method alone.
Fil: Aguirre, Cesar Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina. Universidad Tecnológica Nacional. Facultad Regional Paraná; Argentina
Fil: Rondan, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
Fil: Sedano, Carlos German. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
Fil: Cardozo, Macarena. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
Fil: Rut, Tatiana. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
Materia
SOIL MOISTURE
SPATIAL VARIABILITY
GEOSTATISTICS
ORDINARY KRIGING
TOPOGRAPHY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/282494
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/282494
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic IndicesAguirre, Cesar AugustoRondan, Guillermo AntonioSedano, Carlos GermanCardozo, MacarenaRut, TatianaSOIL MOISTURESPATIAL VARIABILITYGEOSTATISTICSORDINARY KRIGINGTOPOGRAPHYhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The estimation of soil moisture contents on crop growth is the most important variable and, in many cases, determines yields. Many simulation models predict this variable by considering atmospheric conditions, crop water needs, and soil characteristics. In general, these models simulate soil moisture content for an aver-age plant in the cultivated field without taking into account spatial variability. Relief is one of the characteris-tics that can explain this variability, so obtaining maps of soil moisture in the root zone has been addressed in this work through spatial interpolations obtained in sampling campaigns, together with the application of the Bayesian data fusion technique. In this work, soil moisture measurements were carried out in the first half of 2021 and the second half of 2022. With these data, several topographic indices were analyzed, finding that the inverse of the topographic wetness index and the digital terrain model best explain the spatial variability of soil moisture. Subsequently, data fusion techniques were applied by combining the results of the Ordinary Kriging interpolation method and these topographic indices. An analysis of the estimation errors was carried out using an independent set of data that did not participate in the spatial interpolations. It is observed that the application of the Bayesian data fusion method, considering these topographic indices, improves the soil mois-ture estimates compared to the use of the Ordinary Kriging interpolation method alone.Fil: Aguirre, Cesar Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina. Universidad Tecnológica Nacional. Facultad Regional Paraná; ArgentinaFil: Rondan, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Sedano, Carlos German. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Cardozo, Macarena. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Rut, Tatiana. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaZhejiang A&F University2025-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/282494Aguirre, Cesar Augusto; Rondan, Guillermo Antonio; Sedano, Carlos German; Cardozo, Macarena; Rut, Tatiana; Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices; Zhejiang A&F University; Agricultural and Rural Studies; 3; 2; 5-2025; 1-242959-9784CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://sccpress.com/ars/article/view/114info:eu-repo/semantics/altIdentifier/doi/10.59978/ar03020009info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-03-11T13:27:24Zoai:ri.conicet.gov.ar:11336/282494instacron: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:34982026-03-11 13:27:24.917CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
title Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
spellingShingle Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
Aguirre, Cesar Augusto
SOIL MOISTURE
SPATIAL VARIABILITY
GEOSTATISTICS
ORDINARY KRIGING
TOPOGRAPHY
title_short Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
title_full Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
title_fullStr Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
title_full_unstemmed Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
title_sort Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices
dc.creator.none.fl_str_mv Aguirre, Cesar Augusto
Rondan, Guillermo Antonio
Sedano, Carlos German
Cardozo, Macarena
Rut, Tatiana
author Aguirre, Cesar Augusto
author_facet Aguirre, Cesar Augusto
Rondan, Guillermo Antonio
Sedano, Carlos German
Cardozo, Macarena
Rut, Tatiana
author_role author
author2 Rondan, Guillermo Antonio
Sedano, Carlos German
Cardozo, Macarena
Rut, Tatiana
author2_role author
author
author
author
dc.subject.none.fl_str_mv SOIL MOISTURE
SPATIAL VARIABILITY
GEOSTATISTICS
ORDINARY KRIGING
TOPOGRAPHY
topic SOIL MOISTURE
SPATIAL VARIABILITY
GEOSTATISTICS
ORDINARY KRIGING
TOPOGRAPHY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The estimation of soil moisture contents on crop growth is the most important variable and, in many cases, determines yields. Many simulation models predict this variable by considering atmospheric conditions, crop water needs, and soil characteristics. In general, these models simulate soil moisture content for an aver-age plant in the cultivated field without taking into account spatial variability. Relief is one of the characteris-tics that can explain this variability, so obtaining maps of soil moisture in the root zone has been addressed in this work through spatial interpolations obtained in sampling campaigns, together with the application of the Bayesian data fusion technique. In this work, soil moisture measurements were carried out in the first half of 2021 and the second half of 2022. With these data, several topographic indices were analyzed, finding that the inverse of the topographic wetness index and the digital terrain model best explain the spatial variability of soil moisture. Subsequently, data fusion techniques were applied by combining the results of the Ordinary Kriging interpolation method and these topographic indices. An analysis of the estimation errors was carried out using an independent set of data that did not participate in the spatial interpolations. It is observed that the application of the Bayesian data fusion method, considering these topographic indices, improves the soil mois-ture estimates compared to the use of the Ordinary Kriging interpolation method alone.
Fil: Aguirre, Cesar Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina. Universidad Tecnológica Nacional. Facultad Regional Paraná; Argentina
Fil: Rondan, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
Fil: Sedano, Carlos German. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
Fil: Cardozo, Macarena. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
Fil: Rut, Tatiana. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina
description The estimation of soil moisture contents on crop growth is the most important variable and, in many cases, determines yields. Many simulation models predict this variable by considering atmospheric conditions, crop water needs, and soil characteristics. In general, these models simulate soil moisture content for an aver-age plant in the cultivated field without taking into account spatial variability. Relief is one of the characteris-tics that can explain this variability, so obtaining maps of soil moisture in the root zone has been addressed in this work through spatial interpolations obtained in sampling campaigns, together with the application of the Bayesian data fusion technique. In this work, soil moisture measurements were carried out in the first half of 2021 and the second half of 2022. With these data, several topographic indices were analyzed, finding that the inverse of the topographic wetness index and the digital terrain model best explain the spatial variability of soil moisture. Subsequently, data fusion techniques were applied by combining the results of the Ordinary Kriging interpolation method and these topographic indices. An analysis of the estimation errors was carried out using an independent set of data that did not participate in the spatial interpolations. It is observed that the application of the Bayesian data fusion method, considering these topographic indices, improves the soil mois-ture estimates compared to the use of the Ordinary Kriging interpolation method alone.
publishDate 2025
dc.date.none.fl_str_mv 2025-05
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/11336/282494
Aguirre, Cesar Augusto; Rondan, Guillermo Antonio; Sedano, Carlos German; Cardozo, Macarena; Rut, Tatiana; Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices; Zhejiang A&F University; Agricultural and Rural Studies; 3; 2; 5-2025; 1-24
2959-9784
CONICET Digital
CONICET
url http://hdl.handle.net/11336/282494
identifier_str_mv Aguirre, Cesar Augusto; Rondan, Guillermo Antonio; Sedano, Carlos German; Cardozo, Macarena; Rut, Tatiana; Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices; Zhejiang A&F University; Agricultural and Rural Studies; 3; 2; 5-2025; 1-24
2959-9784
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://sccpress.com/ars/article/view/114
info:eu-repo/semantics/altIdentifier/doi/10.59978/ar03020009
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Zhejiang A&F University
publisher.none.fl_str_mv Zhejiang A&F University
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 12.977003

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