Abstract: Remote sensing technology is a major method to obtain spatial distribution and quantity of winter wheat area, and classification method suitable for business operation is a key technology target of annual winter wheat remote sensing monitoring. Aimed at the conditions and demands of winter wheat background survey business operation in agriculture information service, this paper has proposed a weighted NDVI index (WWAI) based on normal difference vegetation index (NDVI) time sequence. By taking the extraction of 2013-2014 winter wheat area of Anping County, Hebei Province as an example, the algorithm is realized by using GF-1/WFV (wide field view) data. The main idea of the algorithm is to amplify the difference between winter wheat land type and other ground object types by establishing a winter wheat area index based on time sequence images, and to differentiate winter wheat land type from the others and thus to obtain the crop area of winter wheat by automated threshold value setting method. The algorithm includes the following 5 parts: acquisition of winter wheat time sequence images, sample points setting based on grid, establishment of winter wheat area index, identifying winter wheat area index estimation threshold value by iteration, and accuracy validation. Acquisition of images is based on the identification of growth time of winter wheat, and the principle is to ensure to get one GF-1/WFV cloudless image each month. Growth period of winter wheat in Anping County is from October 1st to June 30th of the next year, including 9 growing stages, i.e. seeding, germinating, tillering, overwintering, reviving, jointing, head sprouting, milking maturity and maturity. One GF-1/WFV cloudless image is selected in the middle 10 days of each month, and a total of 9 images are selected for pre-processing and NDVI calculation. Meanwhile, the study area is divided into a certain number of grids, and each grid is further divided into 2×2 sub-grids. The ground object types of central points in upper left and lower right grid are identified by visual interpretation, expert knowledge and field investigation. In this paper, a total of 10×10 equal interval grids with the average grid size of 4.1 km × 4.0 km, as well as 400 sub-grids with the size of 2.05 km × 2.0 km are obtained. The average NDVI values of winter wheat and other ground objects on all upper left centers of this period are calculated. If the winter wheat NDVI is higher than that of other ground objects, the weight of the images of the period is set to 1, and otherwise, set to -1. The winter wheat area index images can be obtained by using the weighted average of NDVI images of all time phases. After obtaining winter wheat area index, it is also necessary to set appropriate threshold value for winter wheat area extraction. The paper takes the visual interpretation classification results of lower right grid points as the basis for threshold value extraction. The specific method is to divide winter wheat area index from small to large with certain intervals, and then to make dimidiate extraction of winter wheat area indices of the lower right centers by taking each divided value as the extraction threshold value. By comparing with the visual interpretation result, the result with the highest accuracy is taken as the optimal winter wheat area index extraction threshold value, which is identified to be approximately 1 600 with self-adaptation approach finally. In all grids, accuracy validation is conducted by taking the 10 plots with equal probability. Accuracy validation results show that the overall classification accuracy has reached 94.4%, with Kappa coefficient of 0.88. The area extraction accuracy of this method is about 1.7% higher than that of conventional method based on NDVI time sequence images. By establishing winter wheat area index, this paper turns a complicated multiple-parameter problem into a single-parameter problem with clearly defined agricultural significance. This method is featured with high automatic degree and stable classification results, and it has been widely applied in the crop area remote sensing monitoring practices in China.