Because of cloudy and rainy weather in Southern,optical remote sensing data are often not readily available.With penetration and access to timely features,Radar data is increasingly used to monitor crop area and yield estimates.Different remote sensing data source contains a variety of spatial and spectral information.After data fusion,we can get more information and interpret the rich spectrum of effects.This paper based on the ARSIS strategy,using the wavelet transform and the interaction between the band structure model (IBSM),progress the ENVISAT satellite SAR image and the HJ-1A satellite CCD image wavelet decomposition,and low and high- frequency coefficient reconstruction,and then get the fusion images through the inverse wavelet transform.For low and high-frequency characteristics of different regions of the image,by taking different fusion rules that can enhance the integration process of self-adaptive.Based on the subjective and the corresponding quantitative evaluation,and compared with the PCA transformation,IHS transformation and other traditional methods.Finally,extract bands and NDVI values around the fusion from GPS samples,and do further analysis and explanation of the fusion effect.The results show that the Spectral distortion of wavelet fusion,IHS transform,PCA transform images are 0.1016,0.3261 and 1.2772,respectively.Entropy are 14.7015,11.8993 and 13.2293,respectively.The wavelet fusion is the highest.This method maintained good spectral capability,while improved the spatial resolution and visual effects.Information interpretation effect is much better than the other two methods.NDVI between wavelet fusion image and multi-spectral images in the same period has better correlation.When in the rainy weather the multi-spectral images can not be obtained,the using of the fitted model can convert images to obtain the similar NDVI data of the multi-spectral image in the same period,in order to achieve the monitoring crop growth purposes in the same period with radar remote sensing data.