Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield

Improving crop productivity in drought-prone environments is a daunting challenge. Selection of advanced breeding materials for yield is a labor-intensive procedure and sometimes produces misleading results because of the complex genetic behavior of yield. Remote sensing techniques can provide an instantaneous, non-destructive, and quantitative assessment of a crop's ability to intercept radiation and photosynthesize. The objective of this study was to examine vegetation indices derived from aerial images as biomass and yield prediction tools for soybean [Glycine max (L.) Merr.] under different levels of water availability. Two commercial soybean cultivars with contrasting maturity were planted on a rooting-depth restriction installation. Multispectral aerial images were acquired at early flowering and during seed filling, and fifteen vegetation indices were calculated and their associations with yield and biomass assessed. The indices estimated using the near infrared (NIR), RED, and GREEN portions of the spectrum were weak predictors of soybean yield under severe water stress conditions. However, under moderate drought or unstressed conditions, the regressions were able to explain up to 80% of the data on the basis of R-2 values. The nominally best relationships with yield were found for NIR from images taken at seed fill and with biomass for RED bands extracted from images taken at flowering. Results suggest that aerial imaging shows potential as a tool for yield and biomass prediction of soybean cultivars.