Deriving percent crop cover over agriculture canopies using hyperspectral remote sensing

The objective of this study is to investigate the potential of hyperspectral remote sensing for providing green percent crop cover information over agricultural canopies for use in precision farming. Ground measurements and airborne hyperspectral Probe-1 data were acquired over two agricultural sites in July 1999 near Clinton, Ontario, Canada, and in July 2000 near Indian Head, Saskatchewan, Canada. A manual endmember selection and extraction approach was used to unmix the reflectance cubes. Linear constrained and weakly constrained unmixing was conducted to determine crop endmember fractions. Results indicate that the two spectral unmixing algorithms were equally successful. If prior knowledge of the fields exists, a manual endmember selection technique is a suitable approach for the selection of endmembers in an image scene. Correlations between ground data and Probe-1 crop fractions showed very good results (root mean square error (RMSE) of ±11.06% and ±11.20% and index of agreement (D) of 0.91 and 0.9...