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dc.contributor.authorBRANDÃO, Z. N.
dc.contributor.authorGREGO, C. R.
dc.contributor.authorGONDIM, T. M. de S.
dc.contributor.authorRODRIGUES, H. M.
dc.date.accessioned2024-09-23T07:48:47Z-
dc.date.available2024-09-23T07:48:47Z-
dc.date.created2024-09-22
dc.date.issued2024
dc.identifier.citationCaderno Pedagógico, v. 21, n. 6, p. 1-21, 2024.
dc.identifier.issn1983-0882
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1167517-
dc.descriptionSatellite images are valuable tools to assess the nutritional status of plants and, thus, understand the variability of cotton yield in farmers' fields. By identifying soil variability and nutritional crop reflectance, Precision Agriculture (PA) techniques enable more precise variable rate application of inputs such as fertilizers and pesticides. One important PA technique is geostatistics, resulting in interpolated maps that assist in evaluation during the crop cycle. These kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understand the factors leading to crop yield. This study combines conventional statistical analysis, spatial regression modeling of georeferenced data, and vegetation indices assessment from medium-resolution satelitte images to support decisions on improving cotton yield. The experiments were conducted in a 44.8 ha commercial field in Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field on 04/01/2011 and 04/10/2012 from the AWiF sensor during the peak flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georeferenced central points of 70 plots of a regular grid, each one measuring 80X80 m. Using descriptive statistics and geostatistical analyses, data were analyzed by building and setting semivariograms and kriging interpolation. The best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI, and SAVI were the best indices for estimating P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium-resolution satellite images, allowing the identification of distinct nutritional needs and growth status of canopy to cotton plants.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectÍndices de Vegetação
dc.subjectVegetation Indices
dc.subjectVariabilidade Espacial
dc.subjectKrigagem
dc.subjectSpatial Variability
dc.subjectImagem de satélite
dc.subjectSatellite image
dc.subjectDados georreferenciados
dc.subjectGeoreferenced data
dc.subjectAnálise Geoestatística
dc.subjectGeostatistical analysis
dc.subjectProductivity
dc.subjectGoiás
dc.subjectConsumo de pesticida
dc.titleEstimation of nitrogen and phosphorus content in cotton leaves from medium-resolution satellite images.
dc.typeArtigo de periódico
dc.subject.thesagroAlgodão
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAgricultura de Precisão
dc.subject.thesagroSatélite
dc.subject.thesagroNutrição Vegetal
dc.subject.thesagroProdutividade
dc.subject.thesagroInsumo
dc.subject.thesagroFertilizante
dc.subject.nalthesaurusKriging
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusCotton
dc.subject.nalthesaurusPlant nutrition
dc.subject.nalthesaurusFarm inputs
dc.subject.nalthesaurusPesticide application
dc.subject.nalthesaurusFertilizers
riaa.ainfo.id1167517
riaa.ainfo.lastupdate2024-09-22
dc.identifier.doi10.54033/cadpedv21n6-293
dc.contributor.institutionZIANY NEIVA BRANDÃO, CNPA; CÉLIA REGINA GREGO, CNPTIA; TARCISIO MARCOS DE SOUZA GONDIM, CNPA; HUGO MACHADO RODRIGUES, UFRRJ.
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