CloudBreaker: Breaking the cloud covers of Sentinel-2 images using multi-stage trained conditional flow matching on Sentinel-1

Cloud cover and nighttime conditions create significant blind spots in satellite remote sensing, frequently severing the temporal continuity of critical multi-spectral imagery. While Sentinel-1 Synthetic Aperture Radar (SAR) is immune to these atmospheric factors, it lacks the intuitive interpretability and spectral richness of optical data. Existing multi-modal SAR-optical fusion methods often prove inefficient in completely overcast scenarios. Furthermore, since Sentinel-1 and Sentinel-2 operate as distinct satellite missions with independent orbital tracks, these methods are severely constrained by the inevitable temporal misalignment between acquisitions. To bridge these gaps, this paper introduces CloudBreaker, a novel generative framework designed to synthesize high-fidelity Sentinel-2 imagery directly from single-modal Sentinel-1 inputs, effectively bypassing the requirement for concurrent optical data. Unlike existing SAR-to-optical translation approaches that are often limited to RGB synthesis, CloudBreaker generates comprehensive multi-spectral data, enabling the accurate derivation of essential vegetation and water indices (NDVI, NDWI). The methodology leverages a novel multi-stage training strategy based on conditional latent flow matching and introduces cosine scheduling—a technique under-utilized in this domain—to enhance convergence stability. Quantitatively, CloudBreaker achieves a Fréchet Inception Distance (FID) of 0.7432 and Structural Similarity (SSIM) scores exceeding 0.6 for derived indices. Qualitatively, the framework demonstrates exceptional utility in disaster response: by penetrating dense storm clouds and volcanic ash, CloudBreaker retrieves unobstructed optical visualizations and biophysical indices of active catastrophe fronts, including the Amazon wildfires and Hurricane Harvey. These results establish CloudBreaker as a vital tool for all-weather Earth observation where standard optical monitoring is rendered blind.