Accurate and real-time wind field data play a crucial role in ensuring the safety of civil aviation flights.In addressing the precise reconstruction of wind fields,this paper proposes a method based on aircraft monitoring data.The approach aims to utilize joint observations of automatic dependent surveillance-broadcast(ADS-B)and Mode-S enhanced surveillance (Mode-S EHS)to calculate wind observation values in the airspace.By integrating a Gaussian Process Regression model from machine learning and utilizing temporally and spatially discrete wind observation values for model training,the method achieves a complete reconstruction of the target airspace wind field.Experimental results demonstrate that the average absolute error of wind speed reconstructed is 2.72 m/s,with a relative error of 8.21%,and the average absolute error of wind direction is 3.66 degrees.This validates the method's capability to rapidly and accurately reconstruct wind fields in real-time.