VEGETATION INDICES FOR IRRIGATED CORN MONITORING

Detalhes bibliográficos
Autor(a) principal: Alvino,Francisco C. G.
Data de Publicação: 2020
Outros Autores: Aleman,Catariny C., Filgueiras,Roberto, Althoff,Daniel, da Cunha,Fernando F.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia Agrícola
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300322
Resumo: ABSTRACT Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in São Desidério, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.
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spelling VEGETATION INDICES FOR IRRIGATED CORN MONITORINGvegetation coverdecision-makingremote sensingABSTRACT Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in São Desidério, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.Associação Brasileira de Engenharia Agrícola2020-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300322Engenharia Agrícola v.40 n.3 2020reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v40n3p322-333/2020info:eu-repo/semantics/openAccessAlvino,Francisco C. G.Aleman,Catariny C.Filgueiras,RobertoAlthoff,Danielda Cunha,Fernando F.eng2020-08-25T00:00:00Zoai:scielo:S0100-69162020000300322Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2020-08-25T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false
dc.title.none.fl_str_mv VEGETATION INDICES FOR IRRIGATED CORN MONITORING
title VEGETATION INDICES FOR IRRIGATED CORN MONITORING
spellingShingle VEGETATION INDICES FOR IRRIGATED CORN MONITORING
Alvino,Francisco C. G.
vegetation cover
decision-making
remote sensing
title_short VEGETATION INDICES FOR IRRIGATED CORN MONITORING
title_full VEGETATION INDICES FOR IRRIGATED CORN MONITORING
title_fullStr VEGETATION INDICES FOR IRRIGATED CORN MONITORING
title_full_unstemmed VEGETATION INDICES FOR IRRIGATED CORN MONITORING
title_sort VEGETATION INDICES FOR IRRIGATED CORN MONITORING
author Alvino,Francisco C. G.
author_facet Alvino,Francisco C. G.
Aleman,Catariny C.
Filgueiras,Roberto
Althoff,Daniel
da Cunha,Fernando F.
author_role author
author2 Aleman,Catariny C.
Filgueiras,Roberto
Althoff,Daniel
da Cunha,Fernando F.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Alvino,Francisco C. G.
Aleman,Catariny C.
Filgueiras,Roberto
Althoff,Daniel
da Cunha,Fernando F.
dc.subject.por.fl_str_mv vegetation cover
decision-making
remote sensing
topic vegetation cover
decision-making
remote sensing
description ABSTRACT Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in São Desidério, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300322
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300322
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1809-4430-eng.agric.v40n3p322-333/2020
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
dc.source.none.fl_str_mv Engenharia Agrícola v.40 n.3 2020
reponame:Engenharia Agrícola
instname:Associação Brasileira de Engenharia Agrícola (SBEA)
instacron:SBEA
instname_str Associação Brasileira de Engenharia Agrícola (SBEA)
instacron_str SBEA
institution SBEA
reponame_str Engenharia Agrícola
collection Engenharia Agrícola
repository.name.fl_str_mv Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)
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