Land cover represents the physical material on the surface of the Earth and affects land-atmosphere interaction such as energy exchange, the biogeochemical cycle. Land cover map is a primary input to land surface schemes used in numerical and climate models. Therefore accurate land cover classification map is needed to simulate the land-atmosphere interactions precisely. This study focuses on the improvement of the MODIS(MODerate resolution Imaging Spectroradiometer) land cover map over the Asia-Oceania region through the reclassification of the misclassified pixels. The misclassified pixels are defined where the number of land cover types are greater than 3 from the 12 years of the MODIS land cover map. The ratio of misclassified pixels in this region amounts to 17.53%. The MODIS NDVI(Normalized Difference Vegetation Index) time series over the correctly classified pixels showed that continuous variation with time without noises. However, there are so many unreasonable fluctuations in the NDVI time series for the misclassified pixels. To improve the quality of input data for the reclassification, we corrected the MODIS NDVI using CSaTC (Correction based on Spatial and Temporal Continuity) developed by Cho and Suh (2013). ISODATA(Iterative Self-Organizing Data Analysis) was used for the clustering of NDVI data over the misclassified pixels and land cover types was determined based on the seasonal variation pattern of NDVI. The final land cover map was generated through the merging of correctly classified MODIS land cover map and reclassified land cover map. The validation results using the 138 ground truth data showed that the overall accuracy of classification is improved from 68% of original MODIS land cover map to 74% of reclassified land cover map.