CosmDiff: Integrating Multitemporal Optical-SAR Data With Conditional Diffusion Models for Optical Satellite Time Series Reconstruction

Optical satellite time series data play a crucial role in monitoring vegetation dynamics and land surface changes. However, persistent cloud cover often leads to missing data, particularly during critical phenological stages, which significantly diminishes data quality and hinders downstream applications. To address this issue, we present CosmDiff (Conditional Optical-SAR Multi-temporal Diffusion), a novel framework for reconstructing optical satellite time series by integrating multimodal, multi-temporal optical and Synthetic Aperture Radar (SAR) data using conditional diffusion models. In CosmDiff, the reconstruction task is formulated as a multivariate time series imputation problem, where missing values are modeled as conditionally dependent on both cloudfree optical observations and synergic SAR time series. The framework incorporates a Transformer-based network within the diffusion process, introducing a novel dimensional decomposition attention mechanism that fuses optical-SAR time series across both temporal and feature dimensions. This mechanism enables the dynamic extraction of essential and complementary features from both modalities. Additionally, linearly interpolated optical time series are used as auxiliary inputs to further guide the imputation process. Experimental results on Sentinel-1/-2 datasets demonstrate that CosmDiff consistently outperforms both traditional interpolation methods and advanced deep learning approaches, achieving a 3.8% reduction in mean absolute error (MAE) and a 6.8% improvement in spectral angle mapper (SAM) compared to competing methods. Furthermore, CosmDiff provides comprehensive uncertainty estimates for its predictions, which are particularly valuable for decision-making applications.