Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1186887
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dc.contributor.authorVAZ, C. M. P.
dc.contributor.authorFERREIRA, E. J.
dc.contributor.authorSPERANZA, E. A.
dc.contributor.authorFRANCHINI, J. C.
dc.contributor.authorNAIME, J. de M.
dc.contributor.authorINAMASU, R. Y.
dc.contributor.authorLOPES, I. de O. N.
dc.contributor.authorCHAGAS, S. das
dc.contributor.authorSCHELP, M. X.
dc.contributor.authorVECCHI, L.
dc.contributor.authorGALBIERI, R.
dc.date.accessioned2026-05-18T13:49:23Z-
dc.date.available2026-05-18T13:49:23Z-
dc.date.created2026-05-15
dc.date.issued2025
dc.identifier.citationAgriEngineering, v. 7, n. 11, 390, Nov. 2025.
dc.identifier.issn2624-7402
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186887-
dc.descriptionYield 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectModelo de regressão
dc.subjectMapas
dc.subjectRegression model
dc.titleCotton yield map prediction using Sentinel-2 satellite imagery in the Brazilian Cerrado production system.
dc.typeArtigo de periódico
dc.subject.thesagroAgricultura de Precisão
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAlgodão
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusYield mapping
dc.subject.nalthesaurusCotton
riaa.ainfo.id1186887
riaa.ainfo.lastupdate2026-05-18
dc.identifier.doihttps://doi.org/10.3390/ agriengineering7110390
dc.contributor.institutionCARLOS 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.
Appears in Collections:Artigo em periódico indexado (CNPDIA)

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