Comparison of Airborne Multispectral and Hyperspectral Imagery for Estimating Grain Sorghum Yield

Abstract

Both multispectral and hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare these two types of imagery for assessing crop growth and yields. The objective of this study was to compare airborne multispectral imagery with airborne hyperspectral imagery for mapping yield variability in grain sorghum fields. Airborne color-infrared (CIR) imagery and airborne hyperspectral imagery along with yield monitor data collected from four fields were used in this study. Three-band imagery with wavebands corresponding to the collected CIR imagery and four-band imagery with wavebands similar to QuickBird satellite imagery were generated from the 102-band hyperspectral imagery. All four types of imagery (two actual and two simulated) were aggregated to increase pixel size to match the yield data resolution. Principal components and all possible normalized difference vegetation indices (NDVIs) were derived from each type of imagery and related to yield. Statistical analysis showed that the hyperspectral imagery accounted for more variability in yield than the other three types of multispectral imagery and that the best narrow-band NDVIs among the 5151 NDVIs derived from each hyperspectral image explained more variability than the best NDVIs derived from any of the actual or simulated multispectral images. These results indicate that hyperspectral imagery has the potential for improving yield estimation accuracy.