A solution for the mixture problem in agricultural remote sensing

A novel conceptual approach to address the mixture problem in agricultural remote sensing is presented in this study. The method, referred to as Signal Unmixing, combines both in situ and hyperspectral data in an adapted spectral mixture analysis algorithm and allows the extraction of pure and complete hyperspectral vegetation spectra (400-2400 nm) from mixed image pixels. The technique is evaluated using images generated from ray tracing simulations of a fully calibrated virtual orchard. Results show a proper extraction of pure vegetation spectra with a relative root mean square error ¿ 0.075. As such, the undesired background effects in vegetation index calculations are reduced to a minimum, which in turn improved the monitoring of canopy water status and LAI.