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Agricultural monitoring is essential for adequate management of food production and distribution. Crop land and crop type classification, using remote sensing time series, form an important tool to capture the agricultural production information. The recently launched Sentinel-2 satellites provide unprecedented monitoring capacities in terms of spatial resolution, swath width, and revisit frequency. The Sentinel-2 for Agriculture ( Sen2-Agri ) system has been developed to fully exploit those capacities, by providing four relevant earth observation products for agricultural monitoring. Under the Dragon 4 Program, the crop mapping with various satellite images and a specific focus on the Yellow River irrigated agricultural area in the Ningxia Hui Autonomous Region in China was carried out with the Sentinel-2 for Agriculture system ( Sent2Agri ) . 9 types of crops were classified and the crop type map in 2017 was produced based on 35 scenes Sentinel 2A/B images. The overall accuracy computed from the error confusion matrix is 88％, which includes the cropped and uncropped types. After the removal of the uncropped area, the overall accuracy for a cropped decrease to 73％. In order to further improve the crop classification accuracy, the training dataset should be further improved and tuned.