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Mapping the crop planting pattern and cropped area rapidly and accurately in Heilongjiang Province is important for agricultural monitoring,。MOD09 and MOD13 were selected as data source for its high time resolution and good quality. To explore the optimal feature and classification method which can obtain the spatial distribution of the main crops in Heilongjiang Province, NDVI, EVI, WDRVI, LSWI and NDSI were selected as input data for crop classification based on time-series of MODIS data and combined with field survey sample points. The results showed that the combination of NDVI, EVI and LSWI joint with support vector machine (SVM) achieved the best accuracy, the overall classification accuracy was 74.18% and the Kappa coefficient was 0.60. The results showed that the support vector machine algorithm outperformed the maximum likelihood algorithm and the random forest algorithm. In Heilongjiang Province, the best period for sorting rice is the transplanting period in May, which can be characterized by LSWI. Theoptimal period for distinguishing between corn and soybean was from the end of September to the beginning of October, which was the period when the soybean was harvested and the corn was not, and the optimal classification feature was EVI. This method provided a reference value for cropped area mapping in other agricultural regions.