Mastering accurate spatial planting and distribution status of the crops is significantly important for the nation to guide the agricultural production and formulate agricultural policies from a macro perspective. In this paper, the Landsat-8 OLI satellite images were taken as the data sources. And as for the nine crop types within the study area, such as the wheat, rice, and other crops, three classification methods of the random forest classification (RFC), the support vector machine (SVM),

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... d the maximum likelihood classification (MLC) were applied in extracting the planting area of winter wheat in Wushi County of Xinjiang Uygur Autonomous Region. It can be seen from the results that, general classification accuracy of MLC, SVM, and RFC are respectively 80.58%, 87.95%, and 95.96%, while their Kappa coefficients are respectively 0.61, 0.76, and 0.86. The RFC method shows higher classification accuracy that those of MLC and SVM methods. The principal component analysis (PCA) was carried out on the original 7-band image to extract the first 4 principal components and calculate the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), wide dynamic range vegetation index (WDRVI), and normalized difference water index (NDWI). Meanwhile, the 6 additional auxiliary feature bands were superimposed on the original 7-band images to carry out reclassification, through which, the general accuracy of MLC increased by 3 percent while its Kappa coefficient increased by 0.06; the SVM general accuracy increased by 3.02 percent while its Kappa coefficient increased by 0.13; and the general accuracy of the RFC increased by 0.85 percent while its Kappa coefficient increased by 0.02. This indicates that, the adding of auxiliary information can improve the crop classification and identification ability and accuracy. Based on the comprehensive evaluation, the classification method of random forest is proved to have better performance in winter wheat identification.