Using Hyperspectral Remote Sensing to Estimate Canopy Chlorophyll Density of Wheat under Yellow Rust Stress

The canopy reflectance of winter wheat infected with different severity yellow rust was collected in the fields and canopy chlorophyll density (CCD) of the whole wheat was measured in the laboratory. The correlation was analyzed between hyperspectral indices and CCDs, the indices with relationship coefficients more than 0. 7 were selected to build the inversion models, and comparing the predicted results and measured results to test the models, the results showed the first derivative index (D(750)-D(550)) (D(750) + D(550)) has higher prediction precision than other indices, while the next is first derivative index (D(725)-D(702))/(D(725)+D(702)). Saturation analysis was performed for the above indices, the result indicated that when CCD was more than 12 mu g . cm(-2), the first derivative index (D(750)-D(550))/(D(750)+D(550)) was easiest to get to saturation level. Therefore, when CCD was less than 12 mu g . cm(-2), the first derivative index (D(750)-D(550))/(D(750)-D(550)) could be used to estimate wheat CCD and had higher prediction precision than other indices; and when CCD was more than 12 mu g . cm(-2), the first derivative index (D(725)-D(702))/( D(725) + D(702)) was not easiest to reach saturation level and could be used to estimate wheat CCD. There is a significant minus correlation between CCD and disease index (DI), moreover, accurate estimation of CCD by using hyperspectral remote sensing not only can monitor wheat growth, but also can provide assistant information for identification of wheat disease. Therefore, this study has important meaning for prevention and reduction of disaster in agricultural field.