Farmland shelterbelts can be represented as typical narrow linear features in remote sensing imagery. However, the significant challenges remain in accurate segmentation, due to their strong global contextual dependencies and weak local features. In this study, a semantic segmentation model, named MFF-Net, was proposed to accurately and rapidly extract cultivated land and shelterbelts. Multi-feature fusion block (MFFB) and spatially gated fusion mechanism (SGFM) were also adaptively integrated with three complementary approaches: the long-range dependency modeling using a mamba-like linear attention (MLLA) operator, the local detail perception in convolutional networks, and the frequency-domain edge enhancement via fast Fourier transform (FFT). The feature representation was effectively balanced when segmenting elongated objects. Furthermore, a task- oriented super-resolution preprocessing was introduced into the network. This front-end step was designed to reconstruct high-resolution images. Thereby, the textual details were enhanced to sharpen the boundaries of subtle features, like shelterbelts. Superior input was provided for the subsequent segmentation model. A series of experiments was performed on the self-constructed dataset of the farmland shelterbelt. The results demonstrate that the superior performance of MFF-Net was achieved in the precision rates of 96.42% for cropland and 82.83% for shelterbelts, with a mean intersection over union (mIoU) of 83.45%, thus outperforming a range of advanced models, including RS³Mamba, DC-Swin, DeepLabV3+, and SegMAN. Ablation studies validated the effectiveness of each component within the multi-feature fusion. Frequency-domain features via FFTFormer contributed to a 2.80% increase in the shelterbelt IoU, indicating the enhanced boundary discrimination. The spatially gated fusion mechanism (SGFM) further boosted the overall mIoU by 1.71%, indicating the adaptive balance on the contribution rates from different feature domains. Compared with a baseline model, the full MFFB was improved mIoU of 5.14%. Super-resolution preprocessing was integrated with the highly effective auxiliary strategy. Taking 4x upsampled images as the input, there was a remarkable 6.61% increase in shelterbelt IoU and a 2.47% gain in overall mIoU. The difficulties with segmenting narrow targets were effectively mitigated to augment their pixel-width and edge clarity. A complete technical pipeline was established from pixel-level segmentation to vectorization and application. Accurate area estimation was achieved with average relative errors of 7.50% for cropland and 6.76% for shelterbelts, compared with the national survey data. An application analysis of shelterbelt closure degree was also made on individual farmland plots. The 91.95% of the plots met the national standard requirement (≥0.75). In conclusion, the MFF-Net model can be expected to effectively segment the narrow linear features in complex landscapes. Synergistic fusion of global, local, and frequency-domain features was combined with task-oriented super-resolution enhancement. This research can provide a robust technical pathway for the precise monitoring and dynamic evaluation of farmland shelterbelt networks in ecological conservation.