Accurate estimation of chlorophyll content has great significance in the study of the ecological effects of vegetation. In order to investigate inversion of vegetation chlorophyll content based hyperspectral data,the authors introduced the composition of HyperScan hyper-spectral remote sensing imaging system,the characteristics of remote sensor,the principle of radiometric calibration and the algorithm of remote sensing reflection efficiency. Based on the hyper-spectral image data collected with HyperScan,the authors adopted 8 vegetation index inversion models,i.e., NDVI,SR,CI,SAVI,DVI,MSAVI2,TVI and CARI, implemented the band merging experiment, and made a comparative study of the accuracy of each model under the circumstance of gradual band merging. The results show that, among the vegetation indexes collected in this paper,the two-band vegetation index generally has higher accuracy than the three-band vegetation index. In the band merging experiment,the model accuracy was decreasing gradually with the band mergence. The model accuracy is generally high in this study,which suggests that the use of ground hyper-spectral images to implement the inversion of chlorophyll is feasible,and the relatively high inversion accuracy can be achieved by adopting the mean value of internal optical spectrum reflection to implement the inversion of the volume of chlorophyll.