In order to establish a hyperspectral estimation model of water content in ramie leaves，the objective of the experiments is to develop a key method for fast and nondestructive monitoring leaf water content in ramie.We collected 360 hyperspectral data on ramie leaves and corresponding water content under the condition of field cultivation.The leverage were used to exclude the outliers, and the sample sets were divided with the concentration gradient method. Moreover, the PLSR models were respectively built for many spectral preprocessing methods and the effects of these models were compared. Among these methods, the OSC preprocessing method has the best effect, the prediction sets used here are R²=0.8503 and RMSEp=0.0235. In order to reduce the number of variables, the Effective Bands(EB)were selected as the input variables through the Regression Coefficient(RC)of the OSC_PLSR model. Then, this study presented a new featureextracting method, which extracts RC’s effective wavelength(EW)again in the PLSR model built on the basis of RCEB, to further reduce the computation. What can be observed from the modeling results involve:the variables of the two featureextracting methods are reduced significantly, the number of variables used in the fullwave band, the RCEB and the RCEB_EW is respectively  2 031, 508 and 16; the RCEB_PLS model has the best indicators of prediction sets(R²=0.8546，RMSEp=0.0232); when compared with the RCED_PLS model, the indicators for the prediction sets of the RCEB_EW_PLSR model is slightly lower(R²=0.8499，RMSEp=0.0234), but this method has the fewest variables, and thus has the best comprehensive effect.This study explored the quantitative relationship between hyperspectral and water content of leaves，and established the hyperspectral leaf’s water content model,which has practical significance for realtime monitoring and accurate diagnosis of water in crop cultivation.