Maize crop lodging is a recurrent phenomenon which results in significant reduction of grain yield and quality in addition to the impediment of mechanical harvesting. The large-scale monitoring of maize crop lodging is important for production policy adjustment and agricultural insurance compensation. In this study, we derived a variety of features from multi-temporal GaoFen-1(GF-1) images before and after maize crop lodging. We screened the most sensitive features of the spectrum, texture, and vegetation index to monitor maize crop lodging. The recursive feature elimination method based on cross-validation (RFECV) and Mutual information (MI) were compared to obtain the optimal feature combination for monitoring the lodging extents of maize crop. The random forest (RF) classifier was used to classify the lodging extents. The results showed that the most sensitive features of the spectrum, texture, and vegetation indices of lodging extents included the difference of reflectance in blue, green and red bands, the difference of normalized difference vegetation index, the difference of ratio vegetation index, the difference of enhanced vegetation index difference, the difference of mean value of blue band, the difference of mean value of green band, and the difference of mean value of red band. The total accuracy of lodging extents classification was 87.50%, and the Kappa coefficient was 0.83 for testing samples. Based on multiple features derived from GF-1 images before and after lodging, the lodging extents of maize crop can be monitored on a large scale.