Use of Spectral Angle Mapper (SAM) and Hyperspectral Imagery for Yield Estimation

Abstract

Vegetation indices (VIs) derived from remotely sensed imagery are commonly used to estimate crop yields. Spectral angle mapper (SAM) provides an alternative approach to quantifying the spectral differences among all pixels in imagery and therefore has the potential for mapping yield variability. The objective of this study was to apply the SAM technique to airborne hyperspectral imagery for mapping yield variability. Airborne hyperspectral imagery was acquired from two grain sorghum fields in south Texas and yield data were collected using a grain yield monitor. SAM was used to generate spectral angle images from the hyperspectral imagery. Statistical analysis showed that yield was significantly related to the SAM images. For comparison, all 5,151 possible normalized difference vegetation indices (NDVIs) were derived from the 102-band images and related to yield. Results showed that SAM provided higher r-values than 80% and 95% of the 5,151 NDVIs for fields 1 and 2, respectively, but the best NDVIs had better correlations with yield. Nevertheless, SAM provides a useful tool to convert a hyperspectral image to a single layer image to characterize yield variability without using actual yield data, while the best NDVIs can only be identified based on actual yield data. These results indicate that the SAM technique can be used alone or in conjunction with other VIs for yield estimation from hyperspectral imagery.