Page Banner

United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING AND GIS FOR DETECTING AND MAPPING INVASIVE WEEDS IN RIPARIAN AND WETLAND ECOSYSTEMS Title: Using spectral distance, spectral angle, and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Author
item Yang, Chenghai

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 8, 2011
Publication Date: January 25, 2012
Citation: Yang, C. 2012. Using spectral distance, spectral angle, and plant abundance derived from hyperspectral imagery to characterize crop yield variation. Precision Agriculture. 13(1):62-75.

Interpretive Summary: Broad-band vegetation indices derived from multispectral imagery are commonly used to characterize crop growing conditions and productivity. As hyperspectral imagery, which contains tens to hundreds of bands of spectral data, is becoming more available, new techniques are needed to extract information in hyperspectral data. This study examined three different spectral measures (spectral distance, spectral angle and plant abundance) derived from hyperspectral imagery for yield estimation. Correlation analysis showed that the three spectral measures were significantly related to yield data. These results indicate that these spectral measures derived from hyperspectral imagery can be used as relative yield maps to characterize crop growth and yield variability.

Technical Abstract: Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne hyperspectral images and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis showed that the modified soil-adjusted vegetation index (MSAVI) produced more consistent and higher r-values with yield than the other VIs among the selected bands. Spectral distance, spectral angle and abundance produced similar r-values to the best VIs. The results from this study suggest that either a MSAVI image based on one NIR band and one green band or a plant abundance image based on a pair of pure plant and soil spectra can be used to convert a hyperspectral image to a relative yield map. Plant abundance provides a direct fractional measure of crop canopy cover and is therefore more attractive than traditional VIs.

Last Modified: 10/25/2014
Footer Content Back to Top of Page