Spatiotemporal Satellite Image Downscaling with Transfer Encoders and Autoregressive Generative Models

TL;DR

This paper introduces a transfer-learning diffusion model that effectively reconstructs high-resolution satellite images from coarse data, preserving spatial and temporal features for environmental monitoring.

Contribution

It combines a pretrained U-Net encoder with a diffusion model for spatiotemporal downscaling, demonstrating improved performance and physical consistency over existing methods.

Findings

Achieved R2 scores of 0.65 to 0.94 across regions and seasons.

Outperformed deterministic U-Nets, VAEs, and baseline transfer models.

Preserved spatial variability and temporal autocorrelation in downscaled images.

Abstract

We present a transfer-learning generative downscaling framework to reconstruct fine resolution satellite images from coarse scale inputs. Our approach combines a lightweight U-Net transfer encoder with a diffusion-based generative model. The simpler U-Net is first pretrained on a long time series of coarse resolution data to learn spatiotemporal representations; its encoder is then frozen and transferred to a larger downscaling model as physically meaningful latent features. Our application uses NASA's MERRA-2 reanalysis as the low resolution source domain (50 km) and the GEOS-5 Nature Run (G5NR) as the high resolution target (7 km). Our study area included a large area in Asia, which was made computationally tractable by splitting into two subregions and four seasons. We conducted domain similarity analysis using Wasserstein distances confirmed minimal distributional shift between MERRA-2 and G5NR, validating the safety of parameter frozen transfer. Across seasonal regional splits, our model achieved excellent performance (R2 = 0.65 to 0.94), outperforming comparison models including deterministic U-Nets, variational autoencoders, and prior transfer learning baselines. Out of data evaluations using semivariograms, ACF/PACF, and lag-based RMSE/R2 demonstrated that the predicted downscaled images preserved physically consistent spatial variability and temporal autocorrelation, enabling stable autoregressive reconstruction beyond the G5NR record. These results show that transfer enhanced diffusion models provide a robust and physically coherent solution for downscaling a long time series of coarse resolution images with limited training periods. This advancement has significant implications for improving environmental exposure assessment and long term environmental monitoring.