As one of the most phytotoxic heavy metals, cadmium (Cd) is easily taken up by vegetables. However, more than 0.2 mg/kg Cd content in leafy vegetables will seriously impact human health according to the standard of maximum levels of contaminants in food (GB2762-2012). Thus, it is of great significance to closely monitor the content of Cd content in leaves of the vegetables. Hyperspectral remote sensing (RS) techniques could monitor the content of metal and non-metal in crops, through a rapid and non-destructive way compared with traditional methods. In order to explore a suitable method for monitoring the heavy metal contents in the leaves of Brassica rapa chinesis under different Cd contaminations, hyperspectral remote sensing techniques were adopted for this research. In the mean time, the most sensitive parameter for certain Cd content in the leaves could also be explored. Six treatments including 0 (CK), 0.5 (T1), 1 (T2), 5 (T3), 10 (T4), 20 (T5) mg/kg of Cd in soils (calculated according to dry weight) were applied for growing B. rapa chinesis. ASD portable field spectrometer was utilized to scan the hyperspectral reflective rate of leaf samples, and Flame atomic absorption spectrometer was used to measure the Cd concentrations, on the 15th and 30th day after the beginning of treatments, respectively. After correlation analysis and stepwise regression between original spectral datum, first derivative spectral datum, spectrum parameters and Cd contents, the sensitive parameters were determined. According to these sensitive parameters, fitting models used to estimate the Cd content in vegetable leaves were established. Results showed that: (1) Both near wavelength of 540 nm and near infrared bands, the spectral reflectance of leaves were generally decreased with increasing of Cd concentration, while no significant difference was detected between the graphs of B. rapa chinesis under T1 and CK. (2) Sensitive bands of the original spectrum were mainly distributed from 690 nm through 1300 nm and the correlation coefficient of wavelength 782 nm was the highest. For first derivative spectra, a range of sensitive bands that was correlative with the Cd content located in the yellow edge, infrared, near infrared and far infrared; (3) Sensitive parameters MCARI (Modified Chlorophyll Absorption Reflectance Index), SDy (Yellow Edge Area), WI (Water Index), DCWI (Disease Water Stress Index), SDr (Red Edge Area) and Dr (The Amplitude of the Red Edge), which reflected the changes of pigment, water content and cell structure, could be used to estimate the Cd content of the leaves. The nonlinear inverse fitting models of the 6 sensitive parameters can well predict the Cd contents of leaves of B. rapa chinesis under different Cd stresses; and (4) The nonlinear inverse fitting models of the SDr derived from the data collected on the 15th day was best fitted for the Cd content in B. rapa chinesis leaves on 30 d. This study showed that the red edge area parameters can be used to estimate the Cd content in B. rapa chinesis leaves. Hyperspectral remote sensing technique is suitable for evaluating the edible security of B. rapa chinesis, and can provide fundamental information for the detection of Cd content in vegetables.