Attention-Driven and Multi-Scale Feature Integrated Approach for Earth Surface Temperature Data Reconstruction

Abstract. High-resolution observations are essential for the study of surface temperatures characterized by complex changes, especially in the surface air temperature of the ocean region, which is an important indicator of coupled changes in sea and air. Because of the scarcity of conventional observations of surface atmospheric temperature in these areas, high-resolution surface atmospheric temperature data obtained from satellite inversion has become the main source of information. However, the lack of data due to such factors as orbital spacing, cloud volume, sensor errors and other interference of polar satellites poses a major challenge to the estimation of the Earth's surface temperature (EST). In this paper, we present ESTD-Net, a new model based on deep learning designed for surface temperature data repair. ESTD-Net combines enhanced multi-header context attention and improved transformer blocks to capture long-range pixel dependencies, improving the model's ability to focus on boundary areas. In addition, we have integrated a convolutional U-Net to optimize high-frequency details and leverage texture enhancements from convolutional neural networks (CNN) to further improve the quality of reconstructed images. The model was enhanced by two key innovations: (1) weighted reconstruction losses, which prioritized masking areas to ensure accurate reconstruction of missing data; (2) Gradient consistency regularizes to minimize gradient differences between real and reconstructed images to ensure structural coherence and consistency. The evaluation showed that ESTD-Net outperformed existing methods in terms of pixel-level accuracy and perceived quality. Our approach provides a robust and reliable solution for restoring surface temperature data.