In this study, a simulated foliar spectral dataset based on the empirical PROSPECT model was generated according to variations of chlorophyll content (Cab) , carotenoid content (Car) , and leaf water content (LWC) .The spectra data were then resampled to a gradient of spectral resolutions and conducted a CWA analysis.The analysis of the spectral resolution impact on retrieving the plant biophysical and biochemical parameters was then performed.The results showed that: (1) CWA can be used to successfully extract sensitive features and to establish retrieving models of parameters including Cab, Car and LWC with high accuracy. (2) With decline of spectral resolution, the number of sensitive features, their correlation, and retrieving accuracy tend to decrease.However, the decline amplitude and the inflection point of the decline curves are all different, which reflected the different impact of the spectral resolution different for different parameters. (3) A significant difference on the sensitivity of spectral resolution was found among different plant biophysical and biochemical parameters, with the LWC appeared to be the most insensitive, followed by Cab, and Car.Based on this result, in retrieving Car, Cab and LWC with CWA, a reasonable result is expected only if the spectral resolution is no lower than 8 nm, 32 nm and 64 nm, respectively.The present study provides a basic understanding in selection of hyperspectral sensors for retrieving and monitoring of plant biophysical and biochemical parameters with the CWA method.