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Landsat 8 remote sensingimages possess higher spatial resolution and higher temporal resolution.The timeseries Landsat 8-NDVI metrics could reflect the phenology calendar, planting pattern, planting structure and planting area information due to its high spatial resolution and high temporal resolution, thus it is an ideal data source for accurate extraction of maize planting area. In most extraction methods, the decision tree classification method is considered to be rapid and efficient, which could extract maize planting area using multithreshold. However, because of the mixedpixel, both the larger and smaller threshold will lead to errors. This problem could be resolved by mixedpixel unmixing method using endmember abundance calculation to eliminate the disturbance of heterogeneous classes. Therefore, taking timeseries Landsat 8-NDVI metrics as data source and using the combined method of decision tree and mixedpixel unmixing methods are effective way to extract crop planting area. The maize planting area in Hebei Province was extracted in this paper based ontimeseries Landsat 8-NDVI. Firstly, the features of timeseries Landsat 8-NDVI curves were analyzed and the decision tree was built to get the distribution of early sowing maize, interplanted summer maize and spring maize. Secondly, mixedness decomposition was calculated among three kinds of maize based on mean NDVI spectral curve of endmember, so maize planting area could be extracted accurately by using computed maize endmember abundance. The accuracy assessment results indicated that the overall classification accuracy of maize planting area was higher than 98% and Kappa coefficient was higher than 0.97. Generally speaking, the main planting crop was summer maize, and spring maize was mostly planted in the south part of Zhuozhou City. These results were accordant with field work data. The above quantitative and qualitative accuracy assessment results indicated that this method can be used to extract maize planted area quickly and accurately.