|Tringe, James - FORMER ARS,LINCOLN NE|
Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 15, 2001
Publication Date: June 1, 2001
Citation: Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.W., Tringe, J.S., Schlemmer, M.R. 2001. Use of remote sensing imagery to estimate corn grain yield. Agronomy Journal 93:583-589. Interpretive Summary: Remote sensing is the science of gathering information about an object by acquiring data with a device not in contact with the object. Healthy corn leaves appear green to our eyes because they contain a pigment called chlorophyll, which absorbs blue and red radiation, leaving green light to be reflected, which is the predominant color perceived by our eyes or other remote sensors. Near-infrared radiation is not visible to the human eye and must be remotely sensed. Remotely sensed multi-wave and reflectance data allow creation of images that show ratios of certain wavelengths called vegetation indexes, which have been found useful in assessing crop canopies and identifying stresses. The objective was to use remotely sensed imagery to compare how different vegetation indices compared with corn grain yield. We used four hybrids and five nitrogen fertilizer levels to create a range in canopy variation. Remotely sensed data were collected from aircraft on several dates during two growing seasons (1997-98) using a four-band (blue, green, red, and near infrared) digital camera system. Camera imagery was corrected for geometric and atmospheric distortion, converted into reflect values, and then used to compute three vegetation indices. Results showed that the vegetation index computed from the green and near infrared wavebands (Green NDVI) derived from imagery acquired during grain filling were the most highly corrected with final grain yield. Index values could be used to produce relative yield maps and facilitate variable applications of crop nutrients for subsequent crops.
Technical Abstract: Remote sensing - the process of acquiring information from remote locations such as an airplane or satellite - is a potentially important source of data for site-specific crop management, providing both spatially and temporally important information. Remotely-sensed multiple band reflectance data allow creation of images that show ratios of certain bands called vegetation indexes, which have been found useful in assessing crop canopies and identifying stresses. Our objective was to use remotely-sensed imagery to compare different vegetation indices (NDVI, TSAVI, and GNDVI) as a means of assessing canopy variation and the resultant impact on final grain yield for corn (Zea mays L.). Treatments consisting of four hybrids and five N fertilizer levels were imposed in small plots grown during the 1997-98 growing seasons near Shelton, NE on a Hord silt loam to create canopy variation. Remotely-sensed data for the research plot area were collected from aircraft on several dates during both growing seasons using a multi-spectral four-band (blue, green, red, and NIR) digital camera system. Digital camera imagery was inputted into a GIS, corrected for various factors, converted into reflectance, and finally used to compute three vegetation indices. Grain yield for each plot was determined at maturity. Our results suggest that the GNDVI derived from remote sensing of corn canopies during grain filling can offer a suitable means for estimating final grain yield. Normalizing GNDVI variability within a field was found to improve the reliability for yield estimation.