Asynchronous Remote Sensing Time-Series Fusion for Cloud Removal and Anytime Reconstruction

Researchers have developed AGFlow, a spatiotemporal flow-matching model that fuses asynchronous Sentinel-1 radar and Sentinel-2 optical satellite data to remove clouds and reconstruct missing time-series frames. The model enables on-demand generation of cloud-free images at both observed and user-specified timestamps, improving fully missing-frame reconstruction by 16-19% over existing methods. This advancement addresses a critical limitation in Earth surface monitoring caused by frequent cloud cover, particularly benefiting applications like dense vegetation monitoring.

arXiv:2605.27726v1 Announce Type: new Abstract: Frequent cloud cover severely limits the usability of Sentinel-2 S2 optical time series for Earth surface monitoring. Sentinel-1 S1 SAR provides all-weather complementary observations, but practical S1/S2 fusion remains difficult because acquisitions are irregular and asynchronous. Many existing approaches assume temporally aligned inputs or require external nearest-date matching and typically restore only observed timestamps, limiting reconstruction under long gaps and preventing on-demand synthesis. We propose AGFlow Time Aligned Generative Flow Matching , a spatiotemporal flow-matching model for S1/S2 cloud removal and time-series reconstruction with three capabilities: 1 timestamp-conditioned internal alignment that fuses asynchronous S1 and cloudy S2 observations without preprocessing-based pairing; 2 spatiotemporal, context-aware denoising that models spatial structure jointly with temporal dynamics rather than independent per-pixel time series ; and 3 anytime querying, enabling generation of cloud-free S2 frames at both observed and user-specified timestamps within the monitoring window. We evaluate on the RESTORE-DiT benchmark protocol with quantitative metrics, qualitative comparisons, and component ablations. AGFlow notably improves fully missing-frame reconstruction MAE and RMSE reduce by 16-19% over RESTORE-DiT and provides reliable reconstructions under persistent gaps, while also yielding competitive cloud removal performance and flexible temporal querying for downstream tasks such as dense vegetation monitoring.