02063naa a2200253 a 450000100080000000500110000800800410001902200450006002400400010510000190014524501110016426000090027552012610028465000270154565000240157265000230159665300090161965300250162870000290165370000170168270000200169970000270171977300630174621383342023-02-13       2022    bl uuuu     u00u1 u    #d  a0100-316X (impresso); 1983-2125 (online)7 a10.1590/1983-21252022v35n111rc2DOI1 aANDRADE, T. G.  aSoybean yield prediction using remote sensing in Southwestern Piauí State, Brazil.h[electronic resource]  c2022  aRecent researches have shown promising results for the use of orbital data using the Normalized Difference Vegetation Index (NDVI) to monitor and predict soybean grain yield. The objective of this work was to evaluate propositions of multiple linear regression models to predict soybean grain yield using NDVI. The research was carried out at the Celeiro Farm, in Monte Alegre do Piauí, PI, Brazil, in an area of 200 ha. Five images were collected during the soybean crop cycle: one from the Landsat 8 and four from the Sentinel 2. Regression analyses were carried out between grain yield data (predicted variable) extracted from harvest maps and spectral data (predictor variables) from NDVI of soybean crops at different developmental stages. The promising models were selected by the Akaike Information Criterion (AIC). The models were validated using Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), considering the mean of soybean yield of the plot. The linear regression models developed with NDVI for the V5-V6 and R2 developmental stages showed promising results for the prediction of soybean grain yield, with mean error of predictions of 153.9 kg ha-1, representing 4.2% when compared to the data from field measures.  aAgricultural forecasts  aRegression analysis  aPrevisão de Safra  aNDVI  aRegressão múltipla1 aANDRADE JUNIOR, A. S. de1 aSOUZA, M. O.1 aLOPES, J. W. B.1 aVIEIRA, P. F. de M. J.  tRevista Caatingagv. 35, n. 1, p. 105-116, jan./mar. 2022.