Soybean and maize are important raw materials for the production of food and livestock feed. Accurate mapping of these two crops is of great significance to crop management, yield estimation, and crop-damage control. In this study, two towns in Guoyang County, Anhui Province, China, were selected as the study area, and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing season. The data obtained on August 18 (early pod-setting stage of soybean) was determined to be the most applicable to soybean and maize mapping by means of the Jeffries–Matusita (JM) distance. Subsequently, three machine-learning algorithms, i.e., random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth plots. It appeared that RF with a Kappa of 0.83 was superior to the other two methods. Furthermore, twenty candidate features containing the reflectance of ten spectral bands (spatial resolution at 10 m or 20 m) and ten remote-sensing indices were input into the RF algorithm to conduct an important assessment. Seven features were screened out and served as the optimum subset, the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle (UAV) images covering six ground samples. The optimum feature-subset achieved high-accuracy crop mapping, with a reduction of data volume by 65% compared with the total twenty features, which also overrode the performance of ten spectral bands. Therefore, feature-optimization had great potential in the identification of the two crops. Generally, the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure.
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