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Near-surface air temperature is a crucial weather parameter that significantly impacts human health and is widely utilized in numerical weather forecasting and climate prediction studies. However, the most common ground-based meteorological station observation and radar observation are often limited by geographic and natural constraints. With the advantages of global coverage and high spatiotemporal resolution, satellite remote sensing has become a valuable support in overcoming data scarcity issues related to ground-based station and radar observations in complex geographic and natural conditions. Although remote sensing indirectly reflects atmosphere variables (e.g., near-surface air temperature), accurately estimating the atmosphere variables through satellite remote sensing remains a significant challenge. This paper introduces a deep learning Transformer-based neural network (TaNet) for near-surface air temperature estimation. TaNet automatically extracts information from imageries captured by China&#x0027;s new-generation geostationary meteorological satellite FengYun-4A and generates grid near-surface air temperature data in near real-time. Extensive experiments conducted using the state-of-the-art operational reanalysis product ERA5 and meteorological station observations as benchmark standards demonstrate the effectiveness and superiority of TaNet. It achieves an impressive Pearson&#x0027;s correlation coefficient (CC) of 0.990 with ERA5 and 0.959 with station observations, outperforming the other products, such as CFSv2, CRA, and U-Net, on root mean square error (RMSE) and CC metrics. TaNet reduces the RMSE of CFSv2, CRA, and U-Net by a margin of 10.551&#x0025; (2.594  ${}^{\circ }\mathrm{C}$   vs. 2.900  ${}^{\circ }\mathrm{C}$  ), 2.261&#x0025; (2.594  ${}^{\circ }\mathrm{C}$   vs. 2.654  ${}^{\circ }\mathrm{C}$  ), and 5.535&#x0025; (2.594  ${}^{\circ }\mathrm{C}$   vs. 2.746  ${}^{\circ }\mathrm{C}$  ), respectively, using station observations as the benchmark.

Deep Learning for Near-Surface Air Temperature Estimation from FY-4A Satellite Data