Existing methods for cropland change detection are hindered by interference from coupled high and low frequency features and bi-temporal feature misalignment. To address these issues, we propose the Dual-Frequency Decoupling and Dynamic Fusion Network (DFDFusion), a dual-stream CNN-Transformer framework incorporating a three-stage synergistic optimization mechanism. First, the Dual-Frequency Domain Decomposition and Enhancement Module (DF \(^3\) -AE) decouples high-frequency textures and low-frequency morphologies using multi-scale dynamic weighting, enhancing feature representation for fragmented boundaries and macro contours. Second, the Bi-temporal Multi-Scale Attention Fusion Module (BMSAF) aligns bi-temporal features via channel-spatial attention, effectively mitigating seasonal interference. Finally, the Hierarchical Cooperative Attention Module (HCAM) progressively refines features through channel reweighting, region focusing, and pixel refinement, addressing edge blurring and semantic fragmentation. Experiments on the CLCD, PX-CLCD, and LEVIR-CD datasets demonstrate DFDFusion’s superior performance, achieving F1 scores of 74.90%, 95.96%, and 91.75%, respectively, surpassing state-of-the-art methods. Its robustness offers a reliable solution for sustainable cropland management.