Abstract: To explore the difference of rapeseed extraction effect in the data characteristics of multi-source medium-high spatial resolution remote sensing data and the suitable extraction methods,based on Sentinel-2A and GF-1 WFV remote sensing data, the planting area of rapeseed in Xinhua city was extracted by using the nearest neighbor method and the BP neural network method in this paper; the confusion matrix was constructed based on sample points to verify the accuracy of classification. At the same time, combined with the official data and compared the relative error of Oilseed Rape planting area extracted by four extraction combination types (data + method). In addition, the spatial distribution of rapeseed extracted would be analyzed. Results showed that the extraction effects of the four extraction methods were all better. The rapeseed planting areas were mainly concentrated and contiguous in the western regions such as Canggu Township, Zhoufen Township and Putian Town, and the distribution in other areas were scattered. The Object-oriented classification method was better than pixel-based classification method in each parameter of accuracy evaluation, and it was more suitable for avoiding misclassification and leakage of mixed pixels. For the same data, the producer accuracy, user accuracy and rapeseed area accuracy of Sentinel-2A data extracted by the nearest neighbor method were 3.22%, 0.43% and 6.24% higher than that of BP neural network method, respectively, the producer accuracy, user accuracy and rape area accuracy of GF-1 WFV data extracted by nearest neighbor method were 3.74%, 0.10% and 9.58% higher than those of BP neural network method. For the same methods, the accuracy of Sentinel-2 data extracted by the two methods mentioned above was higher than that of GF-1 WFV because of it had higher spatial resolution and richer spectral information. So, the Sentinel-2A data was more suitable for crop information extraction in small-scale areas with complex terrain structure and fragmented plots.