Validation of white oat yield estimation models using vegetation indices

Abstract

The use of remote sensing in agriculture presents some practical applications in crop production forecast. In this context, studies with remote sensing are scarce for crops such as white oats, which may indicate the capacity of using this technique in the crop. The aim of this study was to evaluate the accuracy in validation of white oat biomass and grain yield estimates by spectral models previously calibrated using two vegetation indices (NDVI and IRVI) at three phenological stages. The mean values of NDVI and IRVI were correlated with the grain and biomass yield of white oats to obtain regression equations. The accuracy was verified by the determination coefficient (R-2), root mean square error (RMSE) and mean bias error (MBE). The models were calibrated using data from a field experiment carried out in 2017 and validated with data from the same experiment, but conducted in 2018. The models had good generalization capacity for estimating yield of white oats, especially for biomass yield. Parametrized models in more advanced phenological stages, showed lower error of estimation. Models calibrated with the vegetation index IRVI had lower error of estimation than when calibrated with NDVI.