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It is of great significance to accurately identify soybean planting areas for ensuring agricultural and industrial production. High-resolution satellite remotely sensed imagery has greatly facilitated the effective extraction of soybean planting areas but novel methods are required to further improve the identification accuracy. Two typical planting areas of Linhu Town and Baili Town in Northern Anhui Province, China, were selected to explore the accurate extraction method. The 10 m multispectral and 2 m panchromatic Gaofen-1 (GF-1) image data were first fused to produce training, test, and validation data sets after the min–max standardization and data augmentation. The deep learning U-Net model was then adopted to perform the accurate extraction of soybean planting areas. Two vital influencing factors on the accuracies of the U-Net model, including cropping size and training epoch, were compared and discussed. Specifically, three cropping sizes of 128 × 128, 256 × 256, and 512 × 512 px, and 20, 40, 60, 80, and 100 training epochs were compared to optimally determine the values of the two parameters. To verify the extraction effect of the U-Net model, comparison experiments were also conducted based on the SegNet and DeepLabv3+. The results show that U-Net achieves the highest Accuracy of 92.31% with a Mean Intersection over Union (mIoU) of 81.35%, which is higher than SegNet with an improvement of nearly 4% in Accuracy and 10% on mIoU. In addition, the mIoU has been also improved by 8.89% compared with DeepLabv3+. This study provides an effective and easily operated approach to accurately derive soybean planting areas from satellite images. [ABSTRACT FROM AUTHOR]