The robust matching of multitemporal and multimodal remote sensing images is a fundamental prerequisite for image registration，change detection （CD），target localization，image fusion，and three-dimensional reconstruction in aerial and satellite remote sensing applications. However，stable correspondence estimation remains highly challenging when the matching process is simultaneously affected by significant spectral discrepancy，complex seasonal variation，repetitive textures，weakly textured regions，and large appearance inconsistency across acquisition times. In visible-to-near-infrared （NIR） remote sensing scenarios，the same geographic area may present markedly different reflectance patterns，local structures，and texture distributions under different seasons and spectral bands，making traditional hand-crafted descriptors and many existing deep matching methods unreliable when they depend mainly on local photometric similarity or low-level geometric consistency. To address these issues，this paper studies the problem from the joint perspectives of benchmark construction and semantically enhanced matching design. With the goal of establishing reliable geometric supervision under a unified evaluation protocol，we constructed a standardized benchmark based on the public seasonal contrast （SeCo） dataset. This is achieved by organizing cross-temporal visible and near-IR image pairs and introducing random homography perturbations. The resulting benchmark contains 15 000 image pairs，including 12 000 training pairs and 3 000 testing pairs，and provides a reproducible experimental setting for quantitative comparison under severe temporal and spectral changes. The proposed protocol also evaluates models using homography reprojection error and multithreshold matching homography accuracy （MHA），thus enabling a unified assessment of geometric precision and matching robustness. Building upon this benchmark，a semantically enhanced cross-modal feature matching framework is proposed on the basis of XoFTR —— a hierarchical coarse-to-fine architecture for cross-modal correspondence estimation. The proposed method injects dense semantic priors extracted by a frozen DINOv3 model into the coarse matching stage of XoFTR through a lightweight Semantic Boosting Module，thereby enabling joint modeling of high-level semantic consistency and geometric correspondence. For each modality branch，the DINOv3 features are aligned to the coarse feature resolution and fused with geometric tokens，allowing the model to emphasize semantically stable regions while preserving local geometric sensitivity. This design is important because the coarse stage determines the candidate regions explored by subsequent fine matching； here，later local refinement is unlikely to recover correct matches once the global correspondence field is biased by unstable appearance cues. The overall pipeline follows a progressive coarse-to-fine paradigm in which multiscale geometric features are first extracted from visible and near-infrared inputs，coarse tokens are enhanced by DINOv3-derived semantic priors，global correspondences are established through transformer-based context interaction，and local fine-level refinement is subsequently performed to improve correspondence precision. Furthermore，training is conducted in a single-stage in-domain optimization manner in which the DINOv3 encoder is kept frozen throughout optimization and the XoFTR backbone is jointly learned together with the semantic boosting components. This strategy allows the method to leverage semantic priors from large vision models in a lightweight and reproducible manner without introducing excessive trainable parameters or unstable large-model adaptation behavior. Extensive experiments under the unified SeCo evaluation protocol demonstrate that the proposed method outperforms the XoFTR baselines across all MHA thresholds. On the constructed benchmark，the proposed model achieves 33.03%，37.03%，and 39.17% in MHA@3，MHA@5，and MHA@7，respectively. These outperform the original pretrained XoFTR model by 12.91%，7.68%，and 4.62% and surpass the direct in-domain fine-tuning baseline by 14.64%，15.85%，and 16.61%，respectively. Qualitative comparisons further reveal that the proposed framework suppresses cross-region mismatches and yields denser correct correspondences in scenes with strong temporal appearance shifts and cross-modal ambiguity. These results indicate that simply adapting a geometric matching network to the target domain is insufficient for robust multitemporal multimodal remote sensing matching. In comparison，integrating high-level semantic priors from a vision foundation model can significantly enhance the stability of global correspondence establishment and improve final geometric estimation accuracy. The study reveals that high-level semantic representations learned by large-scale vision models can effectively alleviate the representation discrepancy caused by temporal evolution and spectral inconsistency. Furthermore，these can be incorporated into a hierarchical cross-modal matching framework in a computationally efficient and practically effective manner，thereby providing a lightweight and reproducible semantic injection route for large-model-empowered high-precision remote sensing image matching. The source code is publicly available at https://github.com/heng-shan/Dino_ft.