准确识别石漠化土地（RDL）对喀斯特地区的生态环境监测与管理至关重要。然而，由于现有方法未能充分利用多时相多模态信息，RDL提取常受限于地物混淆、季节性变化以及空间分布破碎、细小且不规则等问题。为解决这一问题，我们构建了首个包含时间序列的双模态RDL语义分割数据集（RD数据集），并提出了针对多时相多模态数据的协调稀疏融合框架（RSFF）。该框架采用双分支编码架构和多级时空交互机制，分别从光学影像和合成孔径雷达（SAR）影像中提取空间与时间特征。通过设计的卷积注意力协调模块（CARM）对多维增强特征进行协调，并利用稀疏交叉融合模块（SCFM）在降低计算复杂度的同时提升特征判别力，相较于传统密集注意力机制，实现了跨模态信息的深度互补。在RD数据集上的实验结果表明，RSFF能有效区分RDL与裸地、耕地等相似地物类型，并在保持高分类一致性的同时显著增强RDL边界 delineation。其平均交并比（mIoU）和总体精度（OA）分别达到82.94%和94.28%，优于其他最先进（SOTA）方法。此外，在公开PASTIS-R数据集上的实验进一步验证了RSFF在不同景观特征及细小破碎地块分布下的鲁棒性。RD数据集和代码可在 https://github.com/laopiao-lp/RSFF 获取。

Accurate identification of rocky desertification land (RDL) is crucial for ecological environment monitoring and management in karst regions. However, RDL extraction is often hindered by land-cover confusion, seasonal variability, and the fragmented, small, and irregular spatial distribution, when existing methods insufficiently exploit multi-temporal multimodal information. To address this issue, we construct the first dual-modal RDL semantic segmentation dataset with time series (RD dataset) and propose a reconciliation-sparse fusion framework (RSFF) for multi-temporal multimodal data. The framework adopts a dual-branch encoding architecture with multilevel spatio-temporal interaction to separately extract spatial and temporal features from optical and Synthetic Aperture Radar (SAR) imageries. Through the designed Convolutional Attention Reconciliation Module (CARM), multidimensional enhanced features are reconciled, and the Sparse Cross Fusion Module (SCFM) is employed to improve feature discriminability while reducing computational complexity compared with conventional dense attention mechanisms, enabling deep complementarity of cross-modal information. Experimental results on the RD dataset demonstrate that RSFF can effectively distinguish RDL from similar land-cover types, such as bare land and cropland, and significantly enhance RDL boundary delineation while maintaining high classification consistency. The mean intersection over union (mIoU) and overall accuracy (OA) reach 82.94% and 94.28%, respectively, outperforming other state-of-the-art (SOTA) methods. In addition, experiments on the public PASTIS-R dataset further verify the robustness of RSFF under different landscape characteristics and small, fragmented parcel distributions. The RD dataset and code are available at https://github.com/laopiao-lp/RSFF.