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.
Abstract (translated)
我们提出了一种基于迁移学习的生成式降尺度框架，用于从粗分辨率输入重建高分辨率卫星图像。我们的方法结合了一个轻量级的U-Net迁移编码器和一个扩散模型为基础的生成模型。首先，在长时间序列的低分辨率数据上预训练简单的U-Net，以学习时空表示；随后冻结其编码器，并将其转移到更大的降尺度模型中作为具有物理意义的潜在特征。我们的应用使用了NASA的MERRA-2再分析（50公里分辨率）作为低分辨率源域和GEOS-5自然运行（G5NR，7公里分辨率）作为高分辨率目标域。研究区域涵盖亚洲的一个大范围地区，为了使计算可行，将其划分为两个子区域，并按四个季节进行处理。 我们使用Wasserstein距离进行了领域相似性分析，确认了MERRA-2和G5NR之间的分布变化很小，验证了参数冻结迁移的安全性。在跨季节和区域性分割上，我们的模型取得了卓越的性能（R² = 0.65到0.94），优于包括确定性的U-Nets、变分自编码器以及之前的迁移学习基准在内的对比模型。 通过半方差图、ACF/PACF及基于滞后误差（RMSE/R²）的外部数据评估，表明预测降尺度图像保留了物理一致的空间变异性和时间自相关性，从而能够实现稳定的自回归重构，超出G5NR记录范围。这些结果表明，迁移增强型扩散模型为长时间序列低分辨率影像降尺度提供了一种稳健且具有物理连贯性的解决方案，在有限训练周期内效果显著。 这项进展对改善环境暴露评估和长期环境监测有着重要的影响。
URL
https://arxiv.org/abs/2512.05139
