Cotton yield map prediction using Sentinel-2 satellite imagery in the Brazilian Cerrado production system.
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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.
Palabras clave
Modelo de regressão, Mapas, Regression model, Agricultura de Precisão, Sensoriamento Remoto, Algodão, Precision agriculture, Remote sensing, Yield mapping, Cotton