With the proliferation of multisource sensors and the rapid progress of multimodal data fusion and intelligent interpretation techniques，aerial remote sensing continues to evolve from traditional single-modal perceptions toward multimodal perception and understanding. This progress demonstrates the significant potential for intelligent monitoring and decision-making in precision agriculture，urban environmental monitoring，ecological protection，and natural disaster assessment. Due to ongoing advancements in Earth observation capabilities，multisource remote sensing data，including optical imagery，synthetic aperture radar （SAR），and hyperspectral imagery are being increasingly characterized by high spatial resolution，multitemporal coverage，and multidimensional richness. These developments，in turn，offering a solid foundation for fine-grained land-cover identification and dynamic change analysis. Nevertheless，traditional remote sensing interpretation methods，which rely on single-source data and single-task learning models，struggle to cope with the complexities of real-world scenarios in which object scales vary dramatically，semantic layers are rich and entangled，and spatiotemporal heterogeneity is strong. Because such methods cannot adequately balance holistic scene semantics with local fine-grained details，this limits their capacity for high-level semantic understanding and comprehensive decision-making. Compared with satellite remote sensing，aerial platforms that typically employ unmanned aerial vehicles （UAVs） and low-altitude aircraft offer high spatial resolution and great observational flexibility，facilitating the completion of several tasks，such as fine urban modeling，small-object recognition，and emergency monitoring. However，these advantages also introduce severe scale variations，highly complex backgrounds，and inconsistent imaging conditions，making it extremely difficult for conventional models to capture both global scene semantics and subtle local textures simultaneously. Recent advances in deep learning and artificial intelligence （AI） have transformed remote sensing interpretation from manual inspection toward automation and intelligence，achieving notable results in scene classification，object detection，and semantic segmentation. Yet single-task and single-modality paradigms remain inadequate in terms of fully exploiting complementary information in heterogeneous multisource data. As such，these paradigms fall short of supporting the high-level semantic comprehension and integrated decision-making required for complex applications. The emergence of multimodal large models provides a transformative pathway for intelligent aerial remote sensing interpretation. These models can jointly perform image understanding and linguistic reasoning by integrating visual encoders with large language models（LLMs） through instruction tuning and cross-modal alignment，thus achieving breakthroughs in visual-language fusion，cross-modal reasoning，and task-instruction-guided analysis. At the same time，within the remote sensing community，researchers have begun to construct large-scale image-text datasets to train vision-language models and employ linguistic guidance to enhance the comprehension of complex land-cover semantics，thus marking a critical shift from purely visual modeling toward semantically augmented modeling and laying the groundwork for the subsequent development of multimodal large models. A systematic review reveals that the evolution of remote sensing large models has followed a clear paradigm progression： from early approaches that relied solely on single visual modalities，through vision-language models that introduced textual semantics but are still confined to specific tasks，to the current stage in which unified multimodal large models enable cross-task collaboration and complex reasoning. Despite these promising advances，the application of multimodal large models to aerial remote sensing still faces several formidable challenges. The substantial disparities in spatiotemporal references and radiometric properties among heterogeneous data sources，such as optical，SAR，and hyperspectral imagery，render cross-modal alignment extremely difficult，often giving rise to semantic misalignment and information loss. While fine-grained spatial structures and multiscale objects inherent in aerial imagery impose stringent demands on spatial cognition and multiscale reasoning，general-purpose large models often lack sufficient domain-specific spatial priors to accurately capture geometric and structural relationships. High-resolution scenes that impose massive computational and storage burdens also make real-time inference on edge devices highly challenging，resulting in the failure to meet the stringent timeliness and generalization requirements of time-sensitive tasks，such as disaster response and UAV-based inspection. Moreover，the black-box nature of model decision-making leads to insufficient interpretability and trustworthiness that hinders deployment in safety-critical scenarios. Meanwhile，data privacy concerns have become increasingly prominent within distributed collaborative learning frameworks. In response to the abovementioned challenges，this paper systematically reviews recent advances in multimodal data fusion and large model technologies for aerial remote sensing by tracing the shift from single-source to multimodal integration. Driven by large models，remote sensing interpretation has gradually evolved from low-level perception to cross-modal reasoning and semantic understanding. Nevertheless，substantial challenges remain in multimodal alignment，spatial cognition，task reliability，and practical deployment in real-world scenarios. To help resolve these problems，we analyze cross-sensor fusion and emphasize how disparities in geometry，radiometry，and acquisition timing among optical，SAR，and LiDAR data severely impede cross-modal semantic alignment. Furthermore，despite advances in vision-language pretraining，high resolution and complex backgrounds often degrade alignment accuracy，thus requiring fine-grained multimodal representations through geometric correction，radiometric normalization，and spatiotemporal consistency modeling. For multiscale spatial reasoning，we emphasize that complex tasks require holistic understanding of structural relations and spatial distributions. We argue that while existing models partially strengthen spatial reasoning via region-based interaction and spatial question answering，a unified framework for global spatial relation modeling is needed to elevate task awareness and multilevel reasoning. Generative AI for few-shot interpretation has the potential to alleviate annotation scarcity and support time-critical missions，such as disaster response. Regarding trustworthiness and interpretability，peculiar data distributions and insufficient instruction data in remote sensing exacerbate hallucinations in large vision-language models. Thus，domain knowledge，task constraints，and dedicated evaluation systems are required to enforce reasoning consistency and reliable high-stakes decisions. For efficient edge deployment and privacy，high-resolution large-format data impose heavy computational and storage burdens. Furthermore，although lightweight networks continue to emerge，trade-offs among real-time performance，stability，and complex scene adaptability remain，which means that model compression，inference optimization，and hardware-software co-design become critical. Related to this，federated learning offers a privacy-preserving mechanism for multisource data integration. In summary，addressing these challenges is important not only for advancing the theoretical understanding of cross-modal semantic reasoning and multiscale spatial cognition but also for facilitating the practical deployment of intelligent aerial remote sensing systems in high-stakes，real-world applications.