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Remote sensing imagery is essential for environmental monitoring but often suffers from large gaps due to clouds, sensor failure, or acquisition gaps. Existing interpolation and generative methods struggle to maintain spatial, spectral, and temporal coherence. We present AlignDiff, a diffusion-based framework that formulates reconstruction as a spatiotemporal alignment problem. It employs a three-way strategy: (1) spatial alignment via DEM conditioning, (2) semantic alignment through prompt-based modulation, and (3) distributional alignment with a VGG-Adapter enforcing feature-level consistency. Experiments on Landsat-8 and EarthNet2021 show that AlignDiff surpasses state-of-the-art baselines on spatial and temporal completion, enabling scalable, reliable satellite image recovery.