The absence of medium and high spatial resolution image data is the main limiting factor for extraction of spatial distribution of crops with high spatial resolution. A multi-source remote sensing vegetation index data fusion model based on fuzzy C-clustering algorithm was proposed to solve the problem of no satellite image data coverage in the critical growth period of crop extraction, and it was used to generate vegetation index data with high temporal and spatial resolution by combining Landsat with MODIS vegetation index data. Standard series EVI curve was obtained by ground sample, and the fuzzy C-clustering algorithm was used to classify the vegetation index data generated by the data fusion model into several classes, and series EVI curve of each classes was obtained by using the average value of each class as the class value. The spatial distribution of rice was extracted by spectral correlation similarity analysis of standard series EVI curve and class series curve. Accuracy of the method was tested by Google Earth image and ground sample, and the accuracy were 0.92 and 0.94, respectively, thus it was thought that the method can get relatively high accuracy. The method can be applied to extract the spatial distribution information of crops that had high spatial resolution in the areas of lacking high resolution remote sensing image data. And the multi-source remote sensing vegetation index data fusion models can be used to generate vegetation index data with high spatial and temporal resolution.