Data resample and vegetation index calculation were used to deal with the observed eight kinds of crop canopy spectra in Jianghuai watershed area, and the crop species recognition ability for four common indexes and six sensors was analyzed. At the same time, the data transformation form with the highest recognition efficiency was used to construct the BP neural network model. The results showed that eight kinds of crop spectral curves had large differences and the recognition ability of 6 sensors from big to small: ETM +, QUICKBIRD, IKONOS, MODIS, ASTER, HRG. Crops recognition ability of normalized difference vegetation index (NDVI) and simple ratio (SR) computed by near infrared and red band reflectance of ETM + and QUICKBIRD was stronger. First order differential (FD) (wavelength interval 6 nm) had the highest identification accuracy among different data transformation forms and the identification accuracy was 87.3%. The BP neural network model with 15 hidden layer nodes built by FD (wavelength interval 6 nm) had the highest recognition accuracy, up to 90.0%.