Use of remote-sensing imagery to estimate corn grain yield

Remote sensing-the process of acquiring information about objects from remo te platforms such as ground-based booms, aircraft, or satellites-is a poten tially important source of data for site-specific crop management, providin g both spatial and temporal information. Our objective was to use remotely sensed imagery to compare different vegetation indices as a means of assess ing canopy variation and its resultant impact on corn (Zea mays L.) grain y ield. Treatments consisted of five N rates and four hybrids, which were gro wn 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, g reen, red, and near-infrared reflectance] digital camera system. Imagery wa s imported into a geographical information system (GIS) and then georegiste red, converted into reflectance, and used to compute three vegetation indic es. Grain yield for each plot was determined at maturity. Results showed th at green normalized difference vegetation index (GNDVI) values derived from images acquired during midgrain filling a ere tile 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 o bserved in 1997 (0.7 to 0.82) than in 1998 (0.92 to 0.95). This suggested G NDVI acquired during midgrain filling could be used to produce relative yie ld maps depicting spatial variability in fields, offering a potentially att ractive alternative to use of a combine field monitor.