Application of airborne hyperspectral data for precise agriculture

Hyperspectral remote sensing exploits the fact that all material reflects, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. Hyperspectral algorithms for the estimation of the concentrations of chlorophyll A and carotenoids can be developed using statistical approaches. Some algorithms for the estimation of the concentrations of chlorophyll A and carotenoids in rice leaves from airborne hyperspectral data were developed in this research. Algorithms based on reflectance band ratios and first derivative have been developed for the estimation of chlorophyll A and carotenoid content of rice leaves by using airborne hyperspectral data acquainted by Pushbroom Hyperspectral Imager (PHI). There was a strong R680/R825 and chlorophyll A relationship with a linear relationship between the ratio of reflectance at 680 nm and 825 nm. The first derivative at 686 nm and 601 nm correlated best with carotenoid. The relationship between the ratio of R680/R825 and chlorophyll A relationship, the first derivative at 686 nm and carotenoid concentration were used to develop predictive regression equations for the estimation of canopy chlorophyll A and carotenoid concentration respectively. The relationship was applied to the imagery and a chlorophyll A concentration map was generated.