Accurately delineating soil elements from satellite imagery is fundamental for regional geological mapping and survey. However, vegetation cover and complex geomorphological conditions often obscure diagnostic surface information, weakening the visibility of key geological features. Additionally, long-term tectonic deformation and weathering processes reshape the spatial organization of soil elements, resulting in substantial within-class variability, inter-class spectral overlap, and fragmented structural patterns—all of which hinder reliable segmentation performance for conventional deep learning approaches. To mitigate these challenges, this study introduces a Reinforced Feature and Multiscale Feature Fusion Network (RFMFFNet) tailored for semantic interpretation of soil elements. The model incorporates a rectangular calibration attention (RCA) module into a ResNet101 backbone to recalibrate feature responses in critical regions, thereby improving scale adaptability and the preservation of fine geological structures. A complementary multiscale feature fusion (MFF) component is further designed by combining sparse self-attention with pyramid pooling, enabling richer context aggregation while reducing computational redundancy. Comprehensive experiments on the Landsat-8 and Sentinel-2 datasets verify the effectiveness of the proposed framework. RFMFFNet consistently achieves superior segmentation performance compared with several mainstream deep learning models. On the Landsat-8 dataset, the oPA and mIoU increase by 2.4% and 2.6%, respectively; on the Sentinel-2 dataset, the corresponding improvements reach 4.3% and 4.1%.