ARS | Publication request: STATISTICAL METHODS FOR IDENTIFYING IMAGERY SIGNATURES ASSOCIATED WITH CORN LEAF CHLOROPHYLL CONTENT AND GRAIN YIELD

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: July 23, 2000
Publication Date: July 23, 2000
Citation: Shanahan, J.F., Schepers, J.S., Francis, D.D., Schlemmer, M.R. 2000. Statistical methods for identifying imagery signatures associated with corn leaf chlorophyll content and grain yield. Agronomy Abstracts. p. 275

Technical Abstract: The goal of this work was to evaluate the use of multivariate statistical techniques for identifying important signatures associated with corn leaf chlorophyll content and grain yield from remotely sensed imagery. The work was conducted near Shelton, NE during the 1998 seasons. Treatments consisted of a combination of 4 corn hybrids, differing in canopy architecture, and 5 N rates. Remotely sensed data for the entire plot area were collected at two different crop growth stages using a four-band multi-spectral (blue, green, red, and near infrared) system from aircraft. Leaf chlorophyll content was determined with a SPAD chlorophyll meter on the same dates. Images (0.5-m spatial resolution) were geo-referenced and converted into various vegetation indices, including NDVI. Grain yield for each plot was also determined at maturity. Vegetation indices collected during mid grain fill were most highly correlated with grain yield. Stepwise multiple regression techniques revealed that the green band was the most important band of the four, followed by the near infrared, in predicting SPAD chlorophyll meter readings or grain yield.