Abstract. Wind speed and direction are crucial for environmental monitoring and meteorological research, yet current measurement techniques face challenges in obtaining high spatiotemporal-resolution wind data while maintaining operational flexibility and cost-effectiveness. This study presents a wind estimation method based on attitude changes of an unmanned aerial vehicle (UAV) through controlled wind wall experiments. The estimated wind parameters were compared with measurements from an onboard wind sensor. Results from meteorological tower validations and field campaigns demonstrate that both the attitude-based and sensor-based methods achieved good agreement with reference measurements during UAV hovering. However, sensor measurements showed significant errors at high vertical flight velocities, primarily due to increased UAV downwash, while the attitude-based method maintained accuracy during flights. Building on UAV attitude changes, a machine learning algorithm was further developed to estimate wind parameters with high accuracy, offering a practical solution for future field deployments. Successful application in coastal observations showcased that wind estimation based on UAV attitude dynamics provided important spatiotemporal wind data sets that could be used to investigate the fate and dispersion of air pollutants. This work presents a reliable, sensor-free algorithm that enables low-cost, high-resolution wind measurements across diverse operational scenarios. This advancement creates new opportunities at the intersection of environmental science and emerging low-altitude economy applications, which hold promise for urban air mobility safety assessment and microscale meteorology-enhanced environmental monitoring.
Chen et al. (Fri,) studied this question.