Agronomy journal. May/June 2001. v. 93 (3), p. 583-589.
NALT Subjects:
Zea mays crop yield estimation remote sensing image analysis precision agriculture spatial distribution canopy nitrogen aerial photography geographic information systems reflectance seed productivity application rate hybrids grain yield Nebraska
Issue Date:
May-2001
Abstract:
Remote sensing-the process of acquiring information about objects from remote platforms such as ground-based booms, aircraft, or satellites - - is a potentially important source of data for site-specific crop management, providing both spatial and temporal information. Our objective was to use remotely sensed imagery to compare different vegetation indices as a means of assessing canopy variation and its resultant impact on corn (Zea mays L.) grain yield. Treatments consisted of five N rates and four hybrids, which were grown under irrigation near Shelton, NE on a Hord silt loam in 1997 and 1998. Imagery data with 0.5-m spatial resolution were collected from aircraft on several dates during both seasons using a multispectral, four-band [blue, green, red, and near-infrared reflectance] digital camera system. Imagery was imported into a geographical information system (GIS) and then georegistered, converted into reflectance, and used to compute three vegetation indices. Grain yield for each plot was determined at maturity. Results showed that green normalized difference vegetation index (GNDVI) values derived from images acquired during midgrain filling were the most highly correlated with grain yield; maximum correlations were 0.7 and 0.92 in 1997 and 1998, respectively. Normalizing GNDVI and grain yield variability within hybrids improved the correlations in both years, but more dramatic increases were observed in 1997 (0.7 to 0.82) than in 1998 (0.92 to 0.95). This suggested GNDVI acquired during midgrain filling could be used to produce relative yield maps depicting spatial variability in fields, offering a potentially attractive alternative to use of a combine yield monitor.
