Fusing abundant satellite data with sparse ground measurements constitutes a major challenge in climate modeling. To address this, we propose a strategy to augment the training dataset by introducing unlabeled satellite images paired with pseudo-labels generated through a spatial interpolation technique known as ordinary kriging, thereby making full use of the available satellite data resources. We show that the proposed data augmentation strategy helps enhance the performance of the state-of-the-art convolutional neural network-random forest (CNN-RF) model by a reasonable amount, resulting in a noteworthy improvement in spatial correlation and a reduction in prediction error.

翻译：将丰富的卫星数据与稀疏的地面测量数据融合是气候建模中的一项重大挑战。针对这一问题，我们提出一种通过引入未标记卫星图像及其对应伪标签来扩充训练数据集的策略。其中伪标签通过称为普通克里金法的空间插值技术生成，从而充分利用可用的卫星数据资源。我们证明，该数据增强策略能够合理提升现有最优卷积神经网络-随机森林（CNN-RF）模型的性能，显著改善空间相关性并降低预测误差。