Most crop classification work use the ground reference data to training the classifier; but sometimes, the ground reference data cannot be obtained. In this paper, we tried to use the NDVI time series obtained during 2006 and 2013 to classify crop types in 2014 at 30 m spatial resolution. The experiment was conducted in Southeast Kansas, USA. Firstly, we extracted the NDVI time series using ground reference data between 2006 and 2013 from MODIS NDVI time series. Then, the composed Landsat NDVI data were transformed to MODIS NDVI using the linear correlation between the two data sets. Next, Random Forest (RF) was employed to classify crop types at 30 m resolution. The result showed that this procedure could accurately identify the major crops in the study area as the overall accuracy was 92.22% and the Kappa coefficient was 0.8758. In addition, two subsets of the study area showed that the result obtained in this study was similar to that of Crop Data Layer (CDL) provided by National Agricultural Statistics Service (NASS). Thus, the method proposed in this study could be an alternative way for crop classification when ground reference data cannot be acquired.