Recent approaches in remote sensing have increasingly focused on multimodal data, driven by the growing availability of diverse earth observation datasets. Integrating complementary information from different modalities has shown substantial potential in enhancing semantic understanding. However, existing global multimodal datasets often lack the inclusion of Synthetic Aperture Radar (SAR) data, which excels at capturing texture and structural details. SAR, as a complementary perspective to other modalities, facilitates the utilization of spatial information for global land use and land cover (LULC). To address this gap, we introduce the Dynamic World+ dataset, expanding the current authoritative multispectral dataset, Dynamic World, with aligned SAR data. Additionally, to facilitate the combination of multispectral and SAR data, we propose a lightweight transformer architecture termed SpecSAR-Former. It incorporates two innovative modules, Dual Modal Enhancement Module (DMEM) and Mutual Modal Aggregation Module (MMAM), designed to exploit cross-information between the two modalities in a split-fusion manner. These modules enhance the model's ability to integrate spectral and spatial information, thereby improving the overall performance of global LULC semantic segmentation. Furthermore, we adopt an imbalanced parameter allocation strategy that assigns parameters to different modalities based on their importance and information density. Extensive experiments demonstrate that our network outperforms existing transformer and CNN-based models, achieving a mean Intersection over Union (mIoU) of 59.58%, an Overall Accuracy (OA) of 79.48%, and an F1 Score of 71.68% with only 26.70M parameters. The code will be available at https://github.com/Reagan1311/LULC_segmentation.

本研究针对全球多模态遥感数据中缺乏合成孔径雷达（SAR）数据的问题，通过引入Dynamic World+数据集，实现了光谱与SAR数据的有效结合。提出的SpecSAR-Former网络采用了双模增强模块和互模聚合模块，显著提升了土地利用和覆盖的语义分割性能，实验证明其在模型参数少的情况下优于现有模型，具有重要的应用潜力。

SpecSAR-Former：一种基于轻量级变换网络的全球土地利用与覆盖映射方法，结合了哨兵1号和哨兵2号数据