Land　use　and　land　cover　(LULC)　classification　through　remote　sensing　imagery　serves　as　a　cornerstone　for　environmental　monitoring,　resource　management,　and　evidence-based　urban　planning.　While　Synthetic　Aperture　Radar　(SAR)　and　optical　sensors　individually　capture　distinct　aspects　of　Earth＇s　surface,　their　complementary　nature　SAR　excelling　in　structural　and　all-weather　observation　and　optical　sensors　providing　rich　spectral　information-offers　untapped　potential　for　improving　classification　robustness.　However,　the　intrinsic　differences　in　their　imaging　mechanisms　(e.g.,　SAR＇s　coherent　scattering　versus　optical＇s　reflectance　properties)　pose　significant　challenges　in　achieving　effective　multimodal　fusion　for　LULC　analysis.　To　address　this　gap,　we　propose　a　multimodal　deep-learning　framework　that　systematically　integrates　SAR　and　optical　imagery.　Our　approach　employs　a　dual-branch　neural　network,　with　two　fusion　paradigms　being　rigorously　compared:　the　Early　Fusion　strategy　and　the　Late　Fusion　strategy.　Experiments　on　the　SEN12MS　dataset-a　benchmark　containing　globally　diverse　land　cover　categories-demonstrate　the　framework＇s　efficacy.　Our　Early　Fusion　strategy　achieved　88%　accuracy　(F1　score:　87%),　outperforming　the　Late　Fusion　approach　(84%　accuracy,　F1　score:　82%).　The　results　indicate　that　optical　data　provide　detailed　spectral　signatures　useful　for　identifying　vegetation,　water　bodies,　and　urban　areas,　whereas　SAR　data　contribute　valuable　texture　and　structural　details.　Early　Fusion＇s　superiority　stems　from　synergistic　low-level　feature　extraction,　capturing　cross-modal　correlations　lost　in　late-stage　fusion.　Compared　to　state-of-the-art　baselines,　our　proposed　methods　show　a　significant　improvement　in　classification　accuracy,　demonstrating　that　multimodal　fusion　mitigates　single-sensor　limitations　(e.g.,　optical　cloud　obstruction　and　SAR　speckle　noise).　This　study　advances　remote　sensing　technology　by　providing　a　precise　and　effective　method　for　LULC　classification.