Title:	Estimation of nitrogen and phosphorus content in cotton leaves from medium-resolution satellite images.
Authors:	BRANDÃO, Z. N. GREGO, C. R. GONDIM, T. M. de S. RODRIGUES, H. M.
Affiliation:	ZIANY NEIVA BRANDÃO, CNPA; CÉLIA REGINA GREGO, CNPTIA; TARCISIO MARCOS DE SOUZA GONDIM, CNPA; HUGO MACHADO RODRIGUES, UFRRJ.
Date Issued:	2024
Citation:	Caderno Pedagógico, v. 21, n. 6, p. 1-21, 2024.
Description:	Satellite 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.
Thesagro:	Algodão Sensoriamento Remoto Agricultura de Precisão Satélite Nutrição Vegetal Produtividade Insumo Fertilizante
NAL Thesaurus:	Kriging Remote sensing Precision agriculture Cotton Plant nutrition Farm inputs Pesticide application Fertilizers
Keywords:	Índices de Vegetação Vegetation Indices Variabilidade Espacial Krigagem Spatial Variability Imagem de satélite Satellite image Dados georreferenciados Georeferenced data Análise Geoestatística Geostatistical analysis Productivity Goiás Consumo de pesticida
ISSN:	1983-0882
DOI:	10.54033/cadpedv21n6-293
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPA)