精确且高效地绘制冬小麦、玉米、大豆等作物的分布图，对于农业资源管理、作物种植规划和农业政策制定等都具有重要作用。聚焦于乌克兰农业区域，基于Sentinel-2遥感影像，结合Spearman相关系数和SHAP特征优选方法，优化了冬小麦、玉米和大豆等主要作物的制图流程；并评估了7种特征组合策略与3种机器学习模型在区分主要作物类型上的效能。结果表明：(1)Spearman相关系数与SHAP相结合的特征选择方法效果最佳；(2)基于Spearman相关系数与SHAP相结合得到的最优特征，LightGBM算法的分类精度最高，冬小麦制图的总体精度和Kappa系数为95.42%和90.83%，玉米、大豆制图的总体精度和Kappa系数为96.19%和94.28%，分类结果与统计年鉴数据高度一致，冬小麦、大豆和玉米的面积提取精度分别达到97.31%、97.55%和99.61%。

The precise and efficient delineation of distribution of winter wheat, corn, and soybeans is of vital importance for agricultural resource management, crop planting planning, and agricultural policy formulation. Focused on the agricultural regions of Ukraine, based on Sentinel-2 remote sensing images, we combined the feature selection method of Spearman correlation coefficient with SHAP, to optimize the mapping process of major crops such as winter wheat, corn, and soybeans. We explored the effectiveness of seven combination strategies with three machine learning models in distinguishing major crop types. The results showed that(1)the feature selection method combining Spearman correlation coefficient with SHAP demonstrated the best performance.(2)Based on the optimal features derived from Spearman correlation coefficient and SHAP, the LightGBM algorithm achieved the highest classification accuracy with overall accuracy and Kappa coefficient of95.42% and 90.83% for winter wheat mapping, and overall accuracy and Kappa coefficient of 96.19% and 94.28% for corn, soybeans mapping, respectively. The classification results demonstrated high consistency with statistical yearbook data, achieving area extraction accuracy of 97.31% for winter wheat, 97.55% for soybean, and 99.61% for corn.