Bridging the gap between data-rich training regimes and observation-sparse deployment conditions remains a central challenge in spatiotemporal field reconstruction, particularly when target domains exhibit distributional shifts, heterogeneous structure, and multi-scale dynamics absent from available training data. We present SENDAI, a hierarchical Sparse-measurement, EfficieNt Data AssImilation Framework that reconstructs full spatial states from hyper sparse sensor observations by combining simulation-derived priors with learned discrepancy corrections. We demonstrate the performance on satellite remote sensing, reconstructing MODIS (Moderate Resolution Imaging Spectroradiometer) derived vegetation index fields across six globally distributed sites. Using seasonal periods as a proxy for domain shift, the framework consistently outperforms established baselines that require substantially denser observations -- SENDAI achieves a maximum SSIM improvement of 185% over traditional baselines and a 36% improvement over recent high-frequency-based methods. These gains are particularly pronounced for landscapes with sharp boundaries and sub-seasonal dynamics; more importantly, the framework effectively preserves diagnostically relevant structures -- such as field topologies, land cover discontinuities, and spatial gradients. By yielding corrections that are more structurally and spectrally separable, the reconstructed fields are better suited for downstream inference of indirectly observed variables. The results therefore highlight a lightweight and operationally viable framework for sparse-measurement reconstruction that is applicable to physically grounded inference, resource-limited deployment, and real-time monitor and control.

翻译：弥合数据丰富的训练机制与观测稀疏的部署条件之间的差距，仍然是时空场重构中的一个核心挑战，尤其是在目标域表现出分布偏移、异构结构以及训练数据中未包含的多尺度动态时。我们提出了SENDAI，一种分层稀疏测量高效数据同化框架，该框架通过将仿真导出的先验信息与学习到的差异校正相结合，从超稀疏传感器观测中重构完整的空间状态。我们在卫星遥感领域验证了其性能，重构了全球六个站点的MODIS（中分辨率成像光谱仪）导出的植被指数场。以季节性周期作为域偏移的代理，该框架始终优于需要更密集观测的现有基线方法——SENDAI相比传统基线实现了最高185%的结构相似性指数（SSIM）提升，相比近期基于高频的方法提升了36%。这些优势在具有尖锐边界和亚季节性动态的地貌中尤为显著；更重要的是，该框架有效保留了诊断相关的结构——例如场拓扑、土地覆盖不连续性以及空间梯度。通过生成在结构和光谱上更具可分离性的校正，重构的场更适用于间接观测变量的下游推断。因此，研究结果突显了一种轻量级且操作可行的稀疏测量重构框架，该框架适用于基于物理的推断、资源受限的部署以及实时监测与控制。