Deep learning models have shown great promise in diverse remote sensing applications. However, they often struggle to generalize across geographic regions unseen during training due to domain shifts. Domain shifts occur when data distributions differ between the training region and new target regions, due to variations in land cover, climate, and environmental conditions. Test-time adaptation (TTA) has emerged as a solution to such shifts, but existing methods are primarily designed for classification and are not directly applicable to regression tasks. In this work, we address the regression task of spatio-temporal fusion (STF) for land surface temperature estimation. We propose an uncertainty-aware TTA framework that updates only the fusion module of a pre-trained STF model, guided by epistemic uncertainty, land use and land cover consistency, and bias correction, without requiring source data or labeled target samples. Experiments on four target regions with diverse climates, namely Rome in Italy, Cairo in Egypt, Madrid in Spain, and Montpellier in France, show consistent improvements in RMSE and MAE for a pre-trained model in Orléans, France. The average gains are 24.2% and 27.9%, respectively, even with limited unlabeled target data and only 10 TTA epochs.

翻译：深度学习模型在众多遥感应用中展现出巨大潜力。然而，由于训练区域与目标区域之间的土地覆盖、气候及环境条件差异所导致的域偏移，模型常难以在训练期间未出现的地理区域泛化。测试时自适应（TTA）方法已成为解决此类偏移的一种方案，但现有方法主要针对分类任务设计，无法直接适用于回归任务。本研究针对地表温度估计的时空融合（STF）回归任务，提出了一种不确定性感知的TTA框架。该框架仅更新预训练STF模型中的融合模块，其更新过程由认知不确定性、土地利用与土地覆盖一致性以及偏差校正共同引导，且无需源数据或标注的目标样本。在四个气候各异的实验目标区域（意大利罗马、埃及开罗、西班牙马德里和法国蒙彼利埃）上，该方法使以法国奥尔良为预训练区域的模型在RMSE和MAE指标上均取得了一致性改进。即便在仅有少量未标注目标数据且仅进行10次TTA迭代的情况下，平均性能提升分别达到24.2%和27.9%。