Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors. For the purpose of reducing the number of variables while retaining the most useful information, the factor analysis is an effective method to process hyperspectral data of rich useful information but much redundancy. The main objective of this study is to test if the factors of reflectance spectra of leaf can be used to inverse the chlorophyll concentration. The leaf reflectance spectra of 16 clover samples were collected using ASD (Analytical Spectral Devices) with the range of 325-1075nm and the spectral resolution of 3.5nm in September 23, 2010. The leaves were brought to laboratory to detect the chlorophyll concentration with 95% ethonal and ultraviolet spectrophotometer using the heat insulation barrel. To reduce the disturbance of systematic error, only the reflectance spectral range from 400nm to 900nm was analyzed in this paper. The reflectance data was standardized before the reflectance spectra of leaves was divided into two segments, visible light segment from 400nm to 760nm, and near infrared segment from 760nm to 900nm. And after doing that, the reflectance spectral range of 400-900nm, 400-760nm and 760-900nm were analyzed using factor analysis separately in SPSS 13.0. We generated 15 factors from 400-900nm, 7 factors from 400-760nm and 14 factors from 760-900nm. And according to the factor scores calculated from SPSS software, we generated the factor values of three different spectra ranges. The correlation coefficient between factors and chlorophyll concentration were calculated, and the spectral range of 400-760nm was taken as an example to analyze the impact of loading distribution and total loading capacity of factors on the correlation coefficient. Finally the inversion models for leaf chlorophyll concentration were established by using different factors with a stepwise regression method. These two models were compared with well established several spectral indexes. Only BRI, mND680, mND705, mSR705 have the determination coefficient R² above 0.5 among all the used spectral indexes. The R² of chlorophyll concentration inversion model established by overall factors, segment factors, BRI, mND680, mND705, mSR705 are 0.857, 0.869, 0.787, 0.728, 0.662, 0.597, and the relative errors are 15.3%, 14.3%, 23.7%, 21.5%, 24.9%, 29.7%. The result shows that: 1) To a great extent, the correlation coefficient between factors and leaf chlorophyll concentration was controlled by the loading distribution of factors rather than the loading capacity of factors. 2) Two chlorophyll concentration inversion models were established by factors, one using the first factor, the second factor and the eleventh factor of 400-900nm, and the other using the forth factor of 400-760nm and the first factor and the fourteenth factor of 760-900nm. The inversion model using factors from segmented reflectance spectral regions 400-760nm and 760-900nm, was slightly more accurate than the models estimated by factors from the full spectral range of 400-900nm. 3) Compared with spectral indexes including CARI, MCARI, mND680, mND705, mSR705, TVI, DmSR, BGI and BRI, the inversion models established by factors are more accurate due to a higher coefficient of determination R² and a lower relative error. This study demonstrated that factor analysis can be an effective method to process hyperspectral data and inverse chlorophyll concentration. However, this paper need improve the wider suitability of the models further, since only the clover samples were used, and in the future the research need simulate leaf reflectance spectra using physical models, such as PROSPECT and LIBERTY.