Abstract: Chlorophyll is substance in vegetation for photosynthesis, ultimately affecting the net primary production, which can also indicate the healthy condition of vegetation living in a stressed environment. Hyperspectral remote sensing can provide a possibility for quick and accurate estimation of vegetation chlorophyll concentration in large areas. Soybean canopy reflectance data collected with ASD spectroradiometers (350～1050 nm), which were cultivated in water—fertilizer coupled control conditions, and chlorophyll content data were collected simultaneously. First, correlation between reflectance, derivative reflectance against chl-A and chl-B was conducted; second, RVI, RARSa and PSSRb regressed against chl-A and chl-B; and finally, ANN-BP was established for soybean chlorophyll concentration estimation, which had different nodes in hidden layers. It was found that soybean canopy reflectance shows a negative relationship with chl-A and chl-B, while it shows a positive relationship with chl-A and chl-B in near infrared region. Reflectance derivative has an intimate relationship with chl-A and chl-B in blue, green and red edge spectral region, with the maximum correlation coefficient in red edge region. Chlorophyll specified absorption vegetation index has intimate relationship with chl-A and chl-B, with regression determination coefficient R² greater than 0.736. ANN-BP model can greatly improve soybean chlorophyll concentration estimation accuracy. Determination coefficient (R²=0.94) obtained with four nodes in hidden layers, however, R² still can be improved with nodes in hidden layers increasing, and R² reached 0.98 with six nodes in hidden layers. By above analysis, it indicated that, ANN-BP model can be applied to in-situ collected hyperspectral data for vegetation chlorophyll content estimation with quite accurate prediction, and in the future, ANN-BP model still should be applied to hyperspectral data for other vegetation biophysical and biochemical parameters estimation.