Abstract: Image classification of the TM remote sensing data of Mengla County, Yunnan Province in June of 2007 was conducted by EnMAPbox model with the support vector machine (SVM), attempting to search for the optimal parameters by grid search. The optimal C and g parameters were obtained within a set range, and the land cover classification was done by SVM with the optimized parameters and the remote sensing image. The results showed that the classification accuracy of SVM classifier was higher than that of the regular Maximum Likelihood Classifier (MLC), especially for the broadleaved forests and rubber plantations. The classification accuracy of the two methods would be similar for smaller secondary land types. Comparatively speaking, the overall accuracy of the SVM was 11.9% higher than that of MLC.