Corn (Zea mays L.) yield prediction using multispectral and multidate reflectance

Yield predictive models based on multiple sampling dates may explain more yield variability than models based on a single sampling date. This study determined the influence of two approaches (multiple regression of either soil and crop multispectral and multidate reflectance or variables developed during principal-component analysis of reflectance data) on corn (Zea mays L.) yield predictions. Research was conducted in 1999, 2000, and 2001. Corn yield data from two 65-ha fields were collected with a yield monitor, and crop and soil radiance [green, red, and near-infrared (NIR) wavebands] was measured three times (April-May, July, and August-September). Relative radiance (reflectance) was determined by dividing the measured radiance by the radiance at invariant target. Stepwise multiple regression based on reflectance or principal components was used to develop predictive equations. Multiple-regression models based on 2 yr of reflectance data were biased and provided poor estimates of yield whereas a model based on variables developed during principal-component analysis of reflectance data measured in the spring and summer of 1999 and 200 was unbiased and explained 45% of the corn yield variability in 2001. Differences between these models were attributed to multicollinearity of the data. Models that included data from two or three sampling dates generally explained more yield variability than models that used only one sampling date. Reflectance measured early in the season provided information about soil water and color while reflectance measured in August and September provided information about plant conditions.