Título:	Cotton yield map prediction using Sentinel-2 satellite imagery in the Brazilian Cerrado production system.
Autoria:	VAZ, C. M. P. FERREIRA, E. J. SPERANZA, E. A. FRANCHINI, J. C. NAIME, J. de M. INAMASU, R. Y. LOPES, I. de O. N. CHAGAS, S. das SCHELP, M. X. VECCHI, L. GALBIERI, R.
Afiliação:	CARLOS MANOEL PEDRO VAZ, CNPDIA; EDNALDO JOSE FERREIRA, CNPDIA; EDUARDO ANTONIO SPERANZA, CNPTIA; JULIO CEZAR FRANCHINI DOS SANTOS, CNPSO; JOAO DE MENDONCA NAIME, CNPDIA; RICARDO YASSUSHI INAMASU, CNPDIA; IVANI DE OLIVEIRA NEGRAO LOPES, CNPSO; SÉRGIO DAS CHAGAS, AMAGGI GROUP; MATHIAS XAVIER SCHELP, BOSCH BRAZIL; LEONARDO VECCHI, BOSCH BRAZIL; RAFAEL GALBIERI, INSTITUTO MATO-GROSSENSE DO ALGODÃO.
Ano de publicação:	2025
Referência:	AgriEngineering, v. 7, n. 11, 390, Nov. 2025.
Conteúdo:	Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in largescale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, and complex post-processing. Orbital remote sensing offers an alternative by providing consistent vegetation index (VI) data for crop monitoring and yield estimation. This study developed regression models relating Sentinel-2 VIs (EVI, TVI, NDVI, and NDRE) to cotton yield data obtained from combine harvesters across 30 commercial plots in Mato Grosso, Brazil, over six cropping seasons (2019–2024), totaling 76 plot-season datasets. Vegetation indices were grouped into 15-day intervals based on days after sowing, and a logistic growth function was applied in the regression modeling. Model performance evaluated using 15 independent plot-seasons showed good pixel-level accuracy, with RMSE of 0.695 t ha−1 and R2 of 0.78, with EVI performing slightly better. At the plot scale, mean yield predictions across all datasets achieved an RMSE of 0.41 t ha−1, reflecting the higher reliability of module-based yield measurements. These results demonstrate the potential of Sentinel-2 VIs combined with logistic regression to predict cotton yields in the Cerrado, complementing or replacing harvester-based monitoring.
Thesagro:	Agricultura de Precisão Sensoriamento Remoto Algodão
NAL Thesaurus:	Precision agriculture Remote sensing Yield mapping Cotton
Palavras-chave:	Modelo de regressão Mapas Regression model
ISSN:	2624-7402
Digital Object Identifier:	https://doi.org/10.3390/ agriengineering7110390
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPDIA)