Chlorophyll can be an indicator in photosynthesis capacity and vegetation developmental stages,which is also one of important indicators to monitor health status of wetland vegetation growth. Hyperspectral remote sensing technology can provide a simple, effective and non-destructive data acquisition, which can offer processing method for quantifying diagnosis plant chlorophyll content as well. This study used the Fieldspec 3 spectrometer and a plant probe leaf clip spectral detector to guarantee the spectrum are detected in the same area of the leaf, it is also eliminating the background reflectance, spectral fluctuations caused by bending of the blade surface and the impact caused by leaf internal variability. This study determined the typical wetland plants leaf hyperspectral reflectance data at Wild Duck Lake, and at the same time the corresponding leaf chlorophyll content was measured using a spectrophotometer indoor. The relationship between chlorophyll content and the Trilateral parameters, as well as the ratio of spectral index model (SR) and normalized difference spectral index (ND) were established respectively using linear regression model., then 3-Fold Cross Validation(3K-CV) was used to test the accuracy of the estimation model. The results showed that most of the "trilateral" parameters were significantly correlated with plant leaf chlorophyll content; the maximum correlation coefficient reached 0.867. The correlation coefficient between ratio (SR) and normalized (ND) and chlorophyll content were high in general. Suitable band combinations were 550-700 nm,700-1400nm, 550-700 nm and 1600-1900 nm. The best indices with highest correlation with chlorophyll content were SR (calculated from bands 565 nm and 740 nm) and ND (calculated from bands 565 nm and 735 nm). And then by choosing the best correlation spectrum characteristic parameters based on the Trilateral parameters and ND model index, a plant chlorophyll estimation model was constructed. Among them,a chlorophyll content estimation model established by Red edge position (WP_r) of spectral characteristic parameters and ND (565nm, 735nm) spectral index achieved better test results, and R² both reached above 0.8, the estimation model were y=0.113x-78.74, y=5.5762x +4.4828. Using 3K-CV method for testing and validation, the prediction accuracies of both plant leaf chlorophyll content estimation models were 93.9% and 90.7%, respectively. The quantitative analysis of hyperspectral remote sensing technology shows a strong advantage in detecting vegetation weak spectral differences and provides an important theoretical basis and technical support for the practical application in the diagnosis of plant chlorophyll content.