Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion

Autores
Lapaz Olveira, Adrián Marcelo; Sainz Rozas, Hernan Rene; Castro Franco, Mauricio; Carciochi, Walter Daniel; Nieto, Luciana; Balzarini, Monica Graciela; Ciampitti, Ignacio Antonio; Reussi Calvo, Nahuel Ignacio
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.
Fil: Lapaz Olveira, Adrián Marcelo. Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina
Fil: Sainz Rozas, Hernan Rene. Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Los Llanos; Colombia
Fil: Carciochi, Walter Daniel. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina
Fil: Nieto, Luciana. Kansas State University; Estados Unidos
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Matemáticas. Cátedra de Estadística y Biometría; Argentina
Fil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unidos
Fil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina
Materia
C-SAR BACKSCATTER
SENTINEL-1
SENTINEL-2
SPECTRAL BANDS
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:11336/224122
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/224122
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data FusionLapaz Olveira, Adrián MarceloSainz Rozas, Hernan ReneCastro Franco, MauricioCarciochi, Walter DanielNieto, LucianaBalzarini, Monica GracielaCiampitti, Ignacio AntonioReussi Calvo, Nahuel IgnacioC-SAR BACKSCATTERSENTINEL-1SENTINEL-2SPECTRAL BANDShttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.Fil: Lapaz Olveira, Adrián Marcelo. Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Sainz Rozas, Hernan Rene. Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Los Llanos; ColombiaFil: Carciochi, Walter Daniel. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Nieto, Luciana. Kansas State University; Estados UnidosFil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Matemáticas. Cátedra de Estadística y Biometría; ArgentinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; ArgentinaMDPI2023-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/224122Lapaz Olveira, Adrián Marcelo; Sainz Rozas, Hernan Rene; Castro Franco, Mauricio; Carciochi, Walter Daniel; Nieto, Luciana; et al.; Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion; MDPI; Remote Sensing; 15; 3; 2-2023; 1-172072-4292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/15/3/824info:eu-repo/semantics/altIdentifier/doi/10.3390/rs15030824info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:34:25Zoai:ri.conicet.gov.ar:11336/224122instacron: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 09:34:26.267CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
title Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
spellingShingle Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
Lapaz Olveira, Adrián Marcelo
C-SAR BACKSCATTER
SENTINEL-1
SENTINEL-2
SPECTRAL BANDS
title_short Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
title_full Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
title_fullStr Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
title_full_unstemmed Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
title_sort Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion
dc.creator.none.fl_str_mv Lapaz Olveira, Adrián Marcelo
Sainz Rozas, Hernan Rene
Castro Franco, Mauricio
Carciochi, Walter Daniel
Nieto, Luciana
Balzarini, Monica Graciela
Ciampitti, Ignacio Antonio
Reussi Calvo, Nahuel Ignacio
author Lapaz Olveira, Adrián Marcelo
author_facet Lapaz Olveira, Adrián Marcelo
Sainz Rozas, Hernan Rene
Castro Franco, Mauricio
Carciochi, Walter Daniel
Nieto, Luciana
Balzarini, Monica Graciela
Ciampitti, Ignacio Antonio
Reussi Calvo, Nahuel Ignacio
author_role author
author2 Sainz Rozas, Hernan Rene
Castro Franco, Mauricio
Carciochi, Walter Daniel
Nieto, Luciana
Balzarini, Monica Graciela
Ciampitti, Ignacio Antonio
Reussi Calvo, Nahuel Ignacio
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv C-SAR BACKSCATTER
SENTINEL-1
SENTINEL-2
SPECTRAL BANDS
topic C-SAR BACKSCATTER
SENTINEL-1
SENTINEL-2
SPECTRAL BANDS
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.
Fil: Lapaz Olveira, Adrián Marcelo. Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina
Fil: Sainz Rozas, Hernan Rene. Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Los Llanos; Colombia
Fil: Carciochi, Walter Daniel. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina
Fil: Nieto, Luciana. Kansas State University; Estados Unidos
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Matemáticas. Cátedra de Estadística y Biometría; Argentina
Fil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unidos
Fil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina
description Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.
publishDate 2023
dc.date.none.fl_str_mv 2023-02
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/11336/224122
Lapaz Olveira, Adrián Marcelo; Sainz Rozas, Hernan Rene; Castro Franco, Mauricio; Carciochi, Walter Daniel; Nieto, Luciana; et al.; Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion; MDPI; Remote Sensing; 15; 3; 2-2023; 1-17
2072-4292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/224122
identifier_str_mv Lapaz Olveira, Adrián Marcelo; Sainz Rozas, Hernan Rene; Castro Franco, Mauricio; Carciochi, Walter Daniel; Nieto, Luciana; et al.; Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion; MDPI; Remote Sensing; 15; 3; 2-2023; 1-17
2072-4292
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://www.mdpi.com/2072-4292/15/3/824
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs15030824
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
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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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