Remote-sensing technology features and the environmental elements of surface complexity together determine mixed pixels in remote-sensing images. Many mature methods of hyper spectral mixed-pixel decomposition are available, but research on the multispectral decomposition of mixed pixels are rare. The purpose of this study is to decompose mixed pixels based on their multispectral imaging characteristics.Hyperspectral images with high spectral resolution may benefit from the spectral unmixing of end-members.By contrast,FY3 multispectral（MERSI）image shavea lower spectral resolution but a higher temporal resolution. Thus,MERSI-EVI time series is introduced in this paper to decompose mixed pixels. The basic parameters of the experiment areas are as follows: study area: Hebi City, Henan Province, China; data: 79 MERSI images acquired from May 1, 2013 to October 15, 2013（89 days had no data） and a Landsat 8 OLI image of the year; purpose: extraction of 2013 corn acreage from the data images. First, the remote-sensing images were processed, and the support-vector-machine classification method was used to extract information on farmlands with the use of a Landsat 8 OLI image. Then, SG-filtered MERSI time-series images were used to calculate EVI; the EVI growth curves of the mixed pixels and the crop end-numbers were then generated. The end-members were determined by field investigation. Corn is the main crop in the area. A total of 14 corn end-members were evenly selected in the space.Then, using the traditional method, the 14 corn end-members were combined with other end-members for unmixing. Finally, the spectral angle matching（SAM） method was used to improve the accuracy of the decomposition and adaptively select the most similar corn end-member with mixed pixels. In this case, a growth curve was used instead of a spectral curve. The results of the traditional decomposition methods vary widely; the extracted corn acreage ranges from 191.90 km2 to 574.83km2,whereas the generated corn acreage of the new decomposition method is 589.95 km2.The 2013 summer corn acreage in Hebi City is780.39 km2. Thus, compared with the best result generated by the traditional methods, the relative accuracy of the new method is improved by 2%. This study shows that using vegetation growth curves to decompose mixed pixels is effective for multispectral images.Of course, this study focused on plains, where crop planting structure is relatively simple. For areas with complex geographical environments and/or planting structures, the performance of the proposed method has yet to be confirmed.

混合像元分解;增强型植被指数;时间序列;风云MERSI数据;作物生长曲线