Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen

Autores
Lapaz Olveira, Adrián; Castro Franco, Mauricio; Sainz Rozas, Hernan Rene; Carciochi, Walter; Balzarini, Mónica; Avila, Oscar; Ciampitti, Ignacio; Reussi Calvo, Nahuel Ignacio
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management.
EEA Balcarce
Fil: Lapaz Olveira, Adrián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Lapaz Olveira, Adrián. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.
Fil: Lapaz Olveira, Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Castro Franco, Mauricio. Universidad de los Llanos. Facultad de Ciencias Agropecuarias y Recursos Naturales; Colombia.
Fil: Saínz Rozas, Hernán. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.
Fil: Saínz Rozas, Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina.
Fil: Carciochi, Walter. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Carciochi, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina.
Fil: Balzarini, Mónica. Unidad de Fitopatología Y Modelización Agrícola; Argentina.
Fil: Avila, Oscar. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Avila, Oscar. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.
Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos.
Fil: Reussi Calvo, Nahuel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Reussi Calvo, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fuente
Precision Agriculture : 1-15 (Published: 04 August 2023)
Materia
Maíz
Nitrógeno
Índices de Vegetación
Nutrición
Vigilancia
Satélites
Suelo
Maize
Nitrogen
Vegetation Index
Nutrition
Monitoring
Satellites
Soil
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/15248
Acceder
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oai_identifier_str oai:localhost:20.500.12123/15248
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogenLapaz Olveira, AdriánCastro Franco, MauricioSainz Rozas, Hernan ReneCarciochi, WalterBalzarini, MónicaAvila, OscarCiampitti, IgnacioReussi Calvo, Nahuel IgnacioMaízNitrógenoÍndices de VegetaciónNutriciónVigilanciaSatélitesSueloMaizeNitrogenVegetation IndexNutritionMonitoringSatellitesSoilNitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management.EEA BalcarceFil: Lapaz Olveira, Adrián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Lapaz Olveira, Adrián. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.Fil: Lapaz Olveira, Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Castro Franco, Mauricio. Universidad de los Llanos. Facultad de Ciencias Agropecuarias y Recursos Naturales; Colombia.Fil: Saínz Rozas, Hernán. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.Fil: Saínz Rozas, Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina.Fil: Carciochi, Walter. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Carciochi, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina.Fil: Balzarini, Mónica. Unidad de Fitopatología Y Modelización Agrícola; Argentina.Fil: Avila, Oscar. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Avila, Oscar. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos.Fil: Reussi Calvo, Nahuel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Reussi Calvo, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Springer2023-09-19T16:39:04Z2023-09-19T16:39:04Z2023-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/15248https://link.springer.com/article/10.1007/s11119-023-10054-41573-1618 (online)1385-2256 (print)https://doi.org/10.1007/s11119-023-10054-4Precision Agriculture : 1-15 (Published: 04 August 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2019-PE-E9-I177-001, Desarrollo y aplicación de tecnologías de mecanización, precisión y digitalización de la Agriculturainfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-23T11:18:28Zoai:localhost:20.500.12123/15248instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:18:29.112INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
title Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
spellingShingle Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
Lapaz Olveira, Adrián
Maíz
Nitrógeno
Índices de Vegetación
Nutrición
Vigilancia
Satélites
Suelo
Maize
Nitrogen
Vegetation Index
Nutrition
Monitoring
Satellites
Soil
title_short Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
title_full Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
title_fullStr Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
title_full_unstemmed Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
title_sort Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
dc.creator.none.fl_str_mv Lapaz Olveira, Adrián
Castro Franco, Mauricio
Sainz Rozas, Hernan Rene
Carciochi, Walter
Balzarini, Mónica
Avila, Oscar
Ciampitti, Ignacio
Reussi Calvo, Nahuel Ignacio
author Lapaz Olveira, Adrián
author_facet Lapaz Olveira, Adrián
Castro Franco, Mauricio
Sainz Rozas, Hernan Rene
Carciochi, Walter
Balzarini, Mónica
Avila, Oscar
Ciampitti, Ignacio
Reussi Calvo, Nahuel Ignacio
author_role author
author2 Castro Franco, Mauricio
Sainz Rozas, Hernan Rene
Carciochi, Walter
Balzarini, Mónica
Avila, Oscar
Ciampitti, Ignacio
Reussi Calvo, Nahuel Ignacio
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Maíz
Nitrógeno
Índices de Vegetación
Nutrición
Vigilancia
Satélites
Suelo
Maize
Nitrogen
Vegetation Index
Nutrition
Monitoring
Satellites
Soil
topic Maíz
Nitrógeno
Índices de Vegetación
Nutrición
Vigilancia
Satélites
Suelo
Maize
Nitrogen
Vegetation Index
Nutrition
Monitoring
Satellites
Soil
dc.description.none.fl_txt_mv Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management.
EEA Balcarce
Fil: Lapaz Olveira, Adrián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Lapaz Olveira, Adrián. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.
Fil: Lapaz Olveira, Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Castro Franco, Mauricio. Universidad de los Llanos. Facultad de Ciencias Agropecuarias y Recursos Naturales; Colombia.
Fil: Saínz Rozas, Hernán. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.
Fil: Saínz Rozas, Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina.
Fil: Carciochi, Walter. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Carciochi, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina.
Fil: Balzarini, Mónica. Unidad de Fitopatología Y Modelización Agrícola; Argentina.
Fil: Avila, Oscar. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Avila, Oscar. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.
Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos.
Fil: Reussi Calvo, Nahuel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Reussi Calvo, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
description Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management.
publishDate 2023
dc.date.none.fl_str_mv 2023-09-19T16:39:04Z
2023-09-19T16:39:04Z
2023-08
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/20.500.12123/15248
https://link.springer.com/article/10.1007/s11119-023-10054-4
1573-1618 (online)
1385-2256 (print)
https://doi.org/10.1007/s11119-023-10054-4
url http://hdl.handle.net/20.500.12123/15248
https://link.springer.com/article/10.1007/s11119-023-10054-4
https://doi.org/10.1007/s11119-023-10054-4
identifier_str_mv 1573-1618 (online)
1385-2256 (print)
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2019-PE-E9-I177-001, Desarrollo y aplicación de tecnologías de mecanización, precisión y digitalización de la Agricultura
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Precision Agriculture : 1-15 (Published: 04 August 2023)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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