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.

Use of machine learning for monitoring the growth stages of an agricultural crop

“…Also, other researchers have indicated that NDVI has a stronger correlation when estimating white oat grain yield in comparison to other VI's. 35,36…”

“…Also, other researchers have indicated that NDVI has a stronger correlation when estimating white oat grain yield in comparison to other VI's. 35,36 Evaluation of thematic yield maps by kernel density estimation Yield maps using estimated and actual oats grain yield were generated by the statistical pattern analysis method of KDE. The oats grain yield map variation was highlighted as follows: low (red = 0-4.97 t per ha), medium (orange = 4.97-6.18 t per ha), high (yellow = 6.18-7.11 t per ha) and very high (green > 7.62 t per ha) yield areas.…”

Indicator-to-impact links to help improve agricultural drought preparedness in Thailand

“…maize in Bolton and Friedl, 2013; winter wheat, sorghum and corn in Kogan et al, 2012), South America (e.g. white oat in Brazil in Coelho et al, 2020), Europe (e.g. maize in Germany in Bachmair et al, 2018;cereals in Spain in García-León et al, 2019), Asia (e.g.…”

Indicator-to-impact links to help improve agricultural drought preparedness in Thailand

“…Some have looked specifically at agricultural droughts with efforts to validate drought indices using crop yield data (e.g. Brazil: Coelho et al, 2020;Spain: García-León et al, 2019;Ethiopia: Moussa Kourouma et al, 2021;U.S. : Peña-Gallardo et al, 2018).…”