Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 5972
• Main Authors: Zheng, Jiaqi, Wu, Xi, Li, Xiaojie, Peng, Jing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.09.2024; MDPI
• Subjects: Accuracy; Analysis; Artificial satellites in remote sensing; conditional generative adversarial network; Deep learning; Meteorological research; Meteorological satellites; Methods; multi-scale; near-surface air temperature; Precipitation; Remote sensing; Satellites; self-attention mechanism; Temperature; Vegetation; self-attention mechanism; deep learning; remote sensing; conditional generative adversarial network; multi-scale; near-surface air temperature
• DOI: 10.3390/s24185972
• ISSN: 1424-8220

To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network’s perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson’s correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.