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dc.contributor.author
Lapaz Olveira, Adrián Marcelo  
dc.contributor.author
Castro Franco, Mauricio  
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Sainz Rozas, Hernan Rene  
dc.contributor.author
Carciochi, Walter Daniel  
dc.contributor.author
Balzarini, Monica Graciela  
dc.contributor.author
Avila, Oscar  
dc.contributor.author
Ciampitti, Ignacio Antonio  
dc.contributor.author
Reussi Calvo, Nahuel Ignacio  
dc.date.available
2024-01-05T13:16:12Z  
dc.date.issued
2023-08  
dc.identifier.citation
Lapaz Olveira, Adrián Marcelo; Castro Franco, Mauricio; Sainz Rozas, Hernan Rene; Carciochi, Walter Daniel; Balzarini, Monica Graciela; et al.; Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen; Springer; Precision Agriculture; 24; 6; 8-2023; 2592-2606  
dc.identifier.issn
1385-2256  
dc.identifier.uri
http://hdl.handle.net/11336/222554  
dc.description.abstract
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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Springer  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
C-SAR  
dc.subject
NITROGEN STATUS  
dc.subject
RED-EDGE BANDS  
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SENTINEL 1  
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SENTINEL 2  
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VEGETATION INDICES  
dc.subject.classification
Agricultura  
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Agricultura, Silvicultura y Pesca  
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CIENCIAS AGRÍCOLAS  
dc.title
Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2024-01-03T12:27:15Z  
dc.journal.volume
24  
dc.journal.number
6  
dc.journal.pagination
2592-2606  
dc.journal.pais
Alemania  
dc.description.fil
Fil: Lapaz Olveira, Adrián Marcelo. Universidad Nacional de Mar del Plata; Argentina. 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  
dc.description.fil
Fil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Los Llanos; Colombia  
dc.description.fil
Fil: Sainz Rozas, Hernan Rene. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina  
dc.description.fil
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  
dc.description.fil
Fil: Balzarini, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina  
dc.description.fil
Fil: Avila, Oscar. Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. Universidad Nacional de Mar del Plata; Argentina  
dc.description.fil
Fil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unidos  
dc.description.fil
Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina  
dc.journal.title
Precision Agriculture  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s11119-023-10054-4  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s11119-023-10054-4