Spectral indices from aerial images and their relationship with properties of a corn crop

Autores
Farrell, Mauricio Aníbal; Gili, Adriana Anahí; Noellemeyer, Elke
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Identification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120 kg ha−1 of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).
EEA Anguil
Fil: Farrell, Mauricio Aníbal. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentina
Fil: Gili, Adriana Anahí. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina
Fil: Noellemeyer, Elke. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina
Fuente
Precision agriculture : 1–11. (28 March 2018)
Materia
Maíz
Maize
Zea Mays
Vegetation Index
Indice de Vegetación
Rendimiento
Yields
Multispectral Imagery
Imágenes Multiespectrales
Nitrogen Fertilizers
Abonos Nitrogenados
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/3748
Acceder
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oai_identifier_str oai:localhost:20.500.12123/3748
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spelling Spectral indices from aerial images and their relationship with properties of a corn cropFarrell, Mauricio AníbalGili, Adriana AnahíNoellemeyer, ElkeMaízMaizeZea MaysVegetation IndexIndice de VegetaciónRendimientoYieldsMultispectral ImageryImágenes MultiespectralesNitrogen FertilizersAbonos NitrogenadosIdentification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120 kg ha−1 of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).EEA AnguilFil: Farrell, Mauricio Aníbal. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; ArgentinaFil: Gili, Adriana Anahí. Universidad Nacional de La Pampa. Facultad de Agronomía; ArgentinaFil: Noellemeyer, Elke. Universidad Nacional de La Pampa. Facultad de Agronomía; ArgentinaSpringer2018-10-30T18:19:46Z2018-10-30T18:19:46Z2018-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/3748https://link.springer.com/article/10.1007%2Fs11119-018-9570-9#citeas1385-22561573-1618 (Online)https://doi.org/10.1007/s11119-018-9570-9Precision agriculture : 1–11. (28 March 2018)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2026-09-24T11:34:01Zoai:localhost:20.500.12123/3748instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-09-24 11:34:02.294INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Spectral indices from aerial images and their relationship with properties of a corn crop
title Spectral indices from aerial images and their relationship with properties of a corn crop
spellingShingle Spectral indices from aerial images and their relationship with properties of a corn crop
Farrell, Mauricio Aníbal
Maíz
Maize
Zea Mays
Vegetation Index
Indice de Vegetación
Rendimiento
Yields
Multispectral Imagery
Imágenes Multiespectrales
Nitrogen Fertilizers
Abonos Nitrogenados
title_short Spectral indices from aerial images and their relationship with properties of a corn crop
title_full Spectral indices from aerial images and their relationship with properties of a corn crop
title_fullStr Spectral indices from aerial images and their relationship with properties of a corn crop
title_full_unstemmed Spectral indices from aerial images and their relationship with properties of a corn crop
title_sort Spectral indices from aerial images and their relationship with properties of a corn crop
dc.creator.none.fl_str_mv Farrell, Mauricio Aníbal
Gili, Adriana Anahí
Noellemeyer, Elke
author Farrell, Mauricio Aníbal
author_facet Farrell, Mauricio Aníbal
Gili, Adriana Anahí
Noellemeyer, Elke
author_role author
author2 Gili, Adriana Anahí
Noellemeyer, Elke
author2_role author
author
dc.subject.none.fl_str_mv Maíz
Maize
Zea Mays
Vegetation Index
Indice de Vegetación
Rendimiento
Yields
Multispectral Imagery
Imágenes Multiespectrales
Nitrogen Fertilizers
Abonos Nitrogenados
topic Maíz
Maize
Zea Mays
Vegetation Index
Indice de Vegetación
Rendimiento
Yields
Multispectral Imagery
Imágenes Multiespectrales
Nitrogen Fertilizers
Abonos Nitrogenados
dc.description.none.fl_txt_mv Identification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120 kg ha−1 of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).
EEA Anguil
Fil: Farrell, Mauricio Aníbal. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentina
Fil: Gili, Adriana Anahí. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina
Fil: Noellemeyer, Elke. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina
description Identification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120 kg ha−1 of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).
publishDate 2018
dc.date.none.fl_str_mv 2018-10-30T18:19:46Z
2018-10-30T18:19:46Z
2018-03
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/20.500.12123/3748
https://link.springer.com/article/10.1007%2Fs11119-018-9570-9#citeas
1385-2256
1573-1618 (Online)
https://doi.org/10.1007/s11119-018-9570-9
url http://hdl.handle.net/20.500.12123/3748
https://link.springer.com/article/10.1007%2Fs11119-018-9570-9#citeas
https://doi.org/10.1007/s11119-018-9570-9
identifier_str_mv 1385-2256
1573-1618 (Online)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
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 : 1–11. (28 March 2018)
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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score 12.754232

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