Abstract: On the basis of the traditional visible band and near infrared band, the German Rapid Eye satellite sensor provides red-edge waveband which is more conducive to the classification of surface vegetation and the monitor of vegetation growth. In order to quantitatively analyze the impact of recognition ability which Rapid Eye satellite images adding the red edge band, the area of corn and soybean in the study area was extracted with the monitoring classification method based on BP neural network by using the Rapid Eye satellite remote sensing data of Hadayang town, Hulunbuir, Inner Mongolia on July 25, 2017. Separable measure and confusion matrix of corn, soybean and other kinds of ground objects in the region were calculated under 2 kinds of waveband combinations with or without the involvement of red-edge waveband.Furthermore, the separability of crop extraction and classification accuracy based on confusion matrix was compared and the classification accuracy verification was evaluated by the study area visual interpretation results. The results show that ： The degree of separation between corns and soybeans, soybeans and others, as well as corn and others were improved from 1.71 to 1.99, from 1.97 to 1.99, from 1.91 to 1.93, respectively. The overall accuracy was increased from 79.57% to 84.14%,and the Kappa coefficient was increased from 0.63 to 0.71. The combined data obtained in this study demonstrated that the red-edge waveband significantly improved the ability to distinguish corn from soybean and other objects. There are more and more satellite loads on the red-edge waveband in foreign countries. The domestic satellite also plans to introduce the rededge waveband load technology, which provides more reliable data support for the agricultural administration sector. The results of this study provide reference for the application of red-edge waveband data in agriculture.