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摘要： 玉米是黑河中游种植面积最大的农作物，生长期需水量大、蒸散量高。准确获取玉米种植面积对该区域农作物种植结构调整、水资源合理规划有重要参考意义。基于2019年4月至9月Sentinel-2多时相影像，采用随机森林算法开展了黑河中游玉米种植面积提取研究。研究方法分为两类—直接提取法和两步提取法。进一步探讨了多时间信息量对玉米种植面积提取精度的影响以及各输入特征参数在玉米面积提取过程中的重要性表现。结果表明：基于Sentinel-2多时相影像，直接提取法和两步提取法均可高精度地提取研究区玉米种植面积，特别是两步提取法，玉米分类总体精度可达85.03%，F1_Score为0.70，Kappa系数为0.83；与单幅影像相比，多时相影像可获取不同作物的物候信息，有效减少作物错分/漏分，提高作物分类精度。该方法对基于高分辨率光学影像结合机器学习方法获取具有高度异质性的作物信息具有重要的参考价值。 关键词： 玉米种植面积;  多时相卫星影像;  Sentinel-2;  随机森林     Abstract: Maize is the crop with the largest planting area in the middle reaches of the Heihe River， with large water requirements and high evapotranspiration during the growing period. Accurately obtaining the maize planting area has important significances for the adjustment of crop planting structure and reasonable planning of water resources in the region. The object of this paper is to assess the value of multi-temporal Sentinel-2 data for extraction of maize planting area in the middle reaches of the Heihe River from April to September 2019. The random forest algorithm was adopted in this work. The research methods were divided into two categories： extraction directly and two-step extraction. Further discussed the impact of multi- temporal information as input on the classification accuracy， and analyzed the importance of the input feature parameters of the model in the extraction process. The results showed that the two-step extraction method based on Sentinel-2 multi-temporal images could accurately extract the maize planting area in the study area with the overall classification accuracy of 85.03%， F1_Score of 0.70， and Kappa coefficient of 0.83. Compared with single image， multi-temporal images could effectively improve the accuracy of crop classification， obtaining differently crop phenology information. The research demonstrates the value of obtaining highly heterogeneous crop information based on high-resolution optical image combined with machine learning method.

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