合成孔径雷达（SAR）影像生成对于深入研究散射机制、构建可信的电磁场景模型，以及从根本上缓解制约该领域发展的数据稀缺瓶颈至关重要。然而，现有方法难以同时保障全球地理空间语义与微观散射机制两方面的高保真度，导致全球尺度生成面临严峻挑战。为此，我们提出\textbf{HuiYanEarth-SAR}——首个基于AlphaEarth并融合散射机制的基础性SAR影像生成模型。该模型通过注入地理空间先验以控制宏观结构，并利用隐式散射特性建模确保微观纹理的真实性，从而仅依据地理坐标即可实现全球任意位置高保真SAR影像的生成。本研究不仅构建了一个高效的SAR场景仿真器，更在方法论层面建立了连接地理学、散射机制与人工智能的桥梁；推动SAR研究范式从感知与理解拓展至仿真与生成，为构建高置信度地球数字孪生体提供关键技术支撑。

Synthetic Aperture Radar (SAR) imagery generation is essential for deepening the study of scattering mechanisms, establishing trustworthy electromagnetic scene models, and fundamentally alleviating the data scarcity bottleneck that constrains development in this field. However, existing methods find it difficult to simultaneously ensure high fidelity in both global geospatial semantics and microscopic scattering mechanisms, resulting in severe challenges for global generation. To address this, we propose \textbf{HuiYanEarth-SAR}, the first foundational SAR imagery generation model based on AlphaEarth and integrated scattering mechanisms. By injecting geospatial priors to control macroscopic structures and utilizing implicit scattering characteristic modeling to ensure the authenticity of microscopic textures, we achieve the capability of generating high-fidelity SAR images for global locations solely based on geographic coordinates. This study not only constructs an efficient SAR scene simulator but also establishes a bridge connecting geography, scatter mechanism, and artificial intelligence from a methodological standpoint. It advances SAR research by expanding the paradigm from perception and understanding to simulation and creation, providing key technical support for constructing a high-confidence digital twin of the Earth.