Abstract: Abstract: GF-1 Satellite is the first one of the high resolution satellite series in China. Since its launch on April 26, 2013, GF-1 Satellite has provided a large amount of satellite data with high spatial resolutions of 2, 8 and 16 m, and it has become one of the major data sources for agricultural remote sensing monitoring in China. By taking WFV (wide field view) Sensor carried on GF-1 Satellite with the spatial resolution of 16 m as its major data source, using the data of 4 time phases, i.e. October 2, October 17, November 07 and December 05, 2013, and taking the objects after multi-resolution segmentation as its basic classification units, the paper extracts the winter wheat area by employing hierarchical decision tree classification method, and verifies the accuracy of the classification result by using the ground sample data. The result shows that, the total winter wheat area in Shunyi District, Beijing City is 7 095 hm2, with the overall classification accuracy of 96.7% and mapping accuracy of 90.0%. Accuracy of other unclassified types is 97.3%, with the Kappa coefficient of 0.8. The sowing period of winter wheat in the study area is classified into 4 sowing types: Early sowing (October 1st-5th), mid-term sowing (October 6th-10th), mid-late sowing (October 11th-15th) and late sowing (October 16th-20th). It is found that the NDVI (normalized difference vegetation index) values of winter wheat in above 4 sowing periods show a changing pattern of high-low-secondary high-high, which is closely associated with the development features of winter wheat. The higher the NDVI value on October 2nd, the later the sowing period of winter wheat will be, and the higher the NDVI value on December 5th, the earlier the sowing period will be. The change of NDVI value of late sowing winter wheat is the most significant. Under the support of ground training samples, the threshold range of NDVI is classified, and the 4 winter wheat's sowing periods, i.e. early, mid-term, mid-late and late sowing are corresponding to different NDVI levels. With the NDVI values of different levels not overlapping, the paper calculates 32 parameters of 4 types, such as the reflectivity in Waveband 1-4, the sum total of the reflectivity of Waveband 1-4, the ratio between Waveband 4 and 3 and the ratio between Waveband 3 and 2. The threshold values of the 32 parameters are sequentially screened by employing decision tree classification method. Decision tree process includes the following steps: 1) To set up step length of 32 parameters; 2) To randomly select 10% of the step length combination; 3) To calculate the decision results of each combination; 4) To verify the accuracy of the results by relying on 10 training samples; 5) To select the combination with the highest accuracy as the threshold value of a decision tree node. Multi-temporal remote sensing data provided by GF-1/WFV can reliably reflect the changing law of winter wheat development. By data layering, the ground object types which are easy to be confused with winter wheat, such as grass lawn and peach tree, can be effectively eliminated, and the data can be taken as the foundation for accurate extraction of winter wheat area. Thus, GF-1/WFV has great development and application potential in remote sensing monitoring operations for crop area.