MSF-Net: A Multimodal SAR–Optical Fusion Network for Agricultural Land Use Classification in Smallholder Landscapes of Northern Benin

Accurate crop type mapping in Sub-Saharan Africa is a challenging task, due to the presence of smallholder farming systems characterized by fragmented landscapes and heterogeneous cropping practices. Persistent cloud cover, particularly significant during the cropping season, systematically limits the exploitation of optical satellite image time series, making things even harder. This study proposes MSF-Net (Multimodal Sentinel Fusion Network), a convolutional neural network-based late-fusion framework that combines Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral time series for multi-class crop classification in the complex agricultural landscapes of central and northern Benin. The model was evaluated across six sites and three growing seasons (2022–2024) covering 12 land cover classes and compared with a Sentinel-2-only Temporal Convolutional Neural Network (TempCNN), a SAR-only baseline (S1-Branch), an ablated version of the proposed method, and two external state-of-the-art multimodal architectures, TSViT and TWINNS. MSF-Net achieved the highest or joint-highest overall accuracy in 10 of 14 site–year configurations, with overall accuracy ranging from 82.61% to 91.15% and kappa coefficients from 0.79 to 0.89, consistently outperforming both external baselines across all site–year configurations. The largest gains over TempCNN reached up to 30 percentage points for spectrally ambiguous classes such as Shrubby Savannah, Cotton, and Open Forest. In addition, MSF-Net produced more spatially coherent maps, with reduced salt-and-pepper noise, improved parcel-level homogeneity, and fewer modality-specific artefacts. These results demonstrate the value of SAR-optical fusion for operational crop monitoring in tropical West Africa.