The wide field of view (WFV) imaging system onboard the Chinese GF-1 optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible and one near-infrared bands. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery over land. The MFC algorithm first implements threshold segmentation based on the spectral features, and guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated on 16 scenes randomly selected from different land areas of China. The results indicate that MFC performs well under different land conditions, and the average cloud classification accuracy of MFC is as high as 98.3%. Through the contrastive analysis with cloud detection methods for Landsat imagery, MFC achieved a high accuracy of the cloud and cloud shadow in GF-1 WFV imagery with less spectral bands.