Correction: Zhu et al. UAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air. Drones 2025, 9, 466

There was an error in the original publication [1]. The illustration of the work by Kistner et al. (2024) in Section 1 Introduction is inaccurate and could potentially mislead readers; the authors intend to remove this sentence in the revised version.

A correction has been made to Section 1 Introduction, Paragraph 2:

Instrument-based methods employ dedicated sensors such as ultrasonic anemometers or pitot tubes mounted on unmanned aerial vehicles (UAVs). For instance, Vaisala’s WINDCAP^® ultrasonic sensors (Helsinki, Finland) measure the wind speed using time-of-flight differences between ultrasonic pulses. These sensors offer robustness against mechanical wear and achieve high temporal resolution (up to 20 Hz). However, the integration of such sensors into UAV systems presents challenges, including aerodynamic interference caused by the UAV’s propellers and alignment errors during dynamic maneuvers. In addition, the direct measurement methods, which rely on instruments integrated directly into UAVs, are also affected by factors such as vehicle vibrations, rotor-induced turbulence, and changes in the center-of-gravity position during extreme conditions (e.g., strong winds or rain). These challenges can lead to degraded sensor performance and operational issues. Bruschi et al. (2019) [1] found that 2D anemometers mounted on quadcopters overestimated wind speeds by 20% when the UAV tilted by more than 10°. To address these limitations, Donnell et al. (2018) [2] proposed that calibrations in controlled environments can mitigate sensor errors but require extensive testing in wind tunnels or other controlled conditions. An alternative instrument-based method known as wind pressure orthogonal decomposition (WPOD) was introduced by Hou et al. (2023) [3]. The WPOD method requires a multicopper onboard sensor consisting of four rigid tubes, which capture wind pressure variations from the incoming airflow; the variations are then converted into wind speed via orthogonal decomposition.

With this correction, the order of some references has been adjusted accordingly. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.