Abstract: Field imaging spectrometer can be used to analyze growth information of individual and group crop relying on its data advantage with combination image and spectra as one, so it has great application potential in agricultural quantitative research. In this research, hyperspectral images of corn and soybean in different growth period were collect using visible and near-infrared imaging spectrometer (VNIS), and spectra of four components as illuminated soil, shadow soil, illuminated vegetation and shadow vegetation were gradually extracted, then spectral vegetation index was constructed based on different sensitive bands. On the basis, through analyzing bands correlation between chlorophyll density and spectral vegetation index, those influences for different components on chlorophyll density inversion of crop were explored. Some results can be found that when spectral information came from mixed canopy including vegetation and soil, sensitive bands for chlorophyll density were red light and near-infrared light. When soil spectra was removed, sensitive bands enlarged and showed in blue and green light region, and when spectra of shadow leaves were removed, sensitive bands indicated that visible light bands increased and near-infrared light bands decreased, there was the highest determination coefficient in red light region. Those change characteristics had same trend in different crops, this paper has important meaning for exploring inversion of biochemistry parameters on crop using data with combination image and spectra as one.