the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Feb 2026
Ultra-Lightweight Mid-IR Methane Sensor for UAV-based Measurements
Abstract. We developed an ultra-lightweight mid-infrared laser spectroscopic sensor for precise, rapid mobile measurements of atmospheric methane concentrations aboard small uncrewed aerial vehicles. The design is simple and compact, featuring a Herriott-type multi-pass cell in an open-path configuration. A single board computer with two on-chip microprocessor units allows rapid data acquisition and onboard wavelength modulation spectroscopy, making the sensor a stand-alone turnkey instrument. Including the dedicated battery, the sensor weighs 1.2 kg and consumes up to 11 W of electrical power under standard laboratory conditions making it one of the most lightweight sensors reported. The measurement resolution is 3.7 ppb at a 1 s averaging time. We deployed the sensor in controlled-release experiments and detected methane flux rates as low as 0.2 kg h-1. Consequently, it can be deployed to measure fugitive emissions from anthropogenic and natural sources that would be undetectable for other methods.
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Status: open (until 01 Apr 2026)
- RC1: 'Comment on egusphere-2026-137', Anonymous Referee #1, 19 Feb 2026 reply
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- 1
The manuscript presents an extension of previous work (Norooz Oliaee et al., 2022), advancing from instrument development toward field validation of UAV‑based CH₄ flux estimation using controlled‑release experiments. While the spectrometer design largely follows the earlier publication, the field deployment lacks a sufficiently rigorous assessment of the method under realistic conditions. In particular, the study does not include a systematic evaluation or quantification of uncertainties, biases, and errors related to interpolation approaches, instrumental performance, or atmospheric transport processes. With only two flights (of which just one was conducted under suitable wind conditions) the study does not provide valuable or generalizable insights for the reader.
The paper may be suitable for publication after the authors address the concerns listed below.
General comments:
Validation of plume‑mapping and flux‑estimation methods using sources with known emission rates has been extensively discussed in recent literature. Several studies (e.g., Andersen et al., 2021; Morales et al., 2022) present robust flight protocols and explicitly make clear suggestions for minimum wind speeds and other limiting factors that must be considered for appropriate quantitative flux analysis. These studies generally indicate optimal mean wind speeds of 2–6 m s⁻¹, while wind speeds <2 m s⁻¹ lead to unreliable flux estimates due to large plume variability. The authors are encouraged to review this literature and use these recommendations into the design of future field campaigns.
Existing flux‑estimation methods, including mass‑balance approaches (e.g., Morales et al., 2022) and Gaussian plume inversion modelling (e.g., Shah et al., 2020), typically show uncertainties ranging from ~17% to >200%, dominated by environmental conditions that control plume dispersion. Yet the present manuscript attributes its dominant uncertainty solely to the extrapolation between the lowest flight transect and the ground. It is unclear why this assumption was not verified using a ground‑based instrument (e.g., MIRA Pico Aeris) across the downwind plume (e.g. installed on a mobile platform such as a car).
A more detailed discussion on the accuracy of UAV‑based wind measurements is required, given that wind is a critical parameter in mass-balance calculations. The calibration and performance assessment of the spectrometer were carried out in the laboratory, which may not reflect real‑world conditions. The authors should clarify potential impacts of environmental perturbations: changes in airflow through the MPC (due to UAV speed, wind direction, hovering), variations in pressure and temperature, and the effect of these factors on the non‑linear calibration curve. Similarly, the potential dependence of measured fluxes on the true emission rate warrants discussion.
The manuscript does not sufficiently address the uncertainties introduced by the necessary interpolation across the vertical plume curtain. A more substantial dataset would be required to evaluate these effects robustly, which is not achievable within the reported 54‑minute field campaign.
The authors should also reconsider the title and abstract. The introduction suggests a focus on instrumentation, but the bulk text emphasizes flux estimation. Given that no substantive new instrumental development is presented, this contrast weakens the paper’s positioning. Furthermore, mass‑balance flux estimation involves more than “just” UAV‑based concentration measurements.
Abstract: Technical details of the data acquisition system are not central to the key findings and can be removed. The authors should instead summarize the field‑experiment results. The statement that the instrument “can be deployed to measure natural sources that would be undetectable for other methods” is too strong given that only a single suitable flight was analyzed. The authors should instead outline a realistic outlook, such as: "The instrument could be deployed in future controlled release experiments to validate its performance for detecting fugitive anthropogenic methane emissions (e.g., oil and gas infrastructure).”
Methods:
The rationale for selecting WMS over DAS is not explained. The authors should briefly discuss the limiting factors for DAS in this context.
The manuscript should clarify what quantity the analyzer measures. Greenhouse gas concentrations are typically reported as dry mole fractions—how was water‑vapor effect corrected? Although the analyzer records H₂O, this was not shown or discussed.
Flux estimation requires interpolation of measurements along the vertical direction. A description of the interpolation method is missing.
Specific Comments
L90: Remove “free space” before “laser”.
L91: Specify the tuning rate used for the spectral scan. In Fig. 2c, replace channel number with optical frequency (cm⁻¹).
L96/98: Remove unnecessary trailing zeros.
L106: Remove “has no moving part”.
L110: Compare the 1.2 kg sensor package with the MIRA Pico regarding weight, precision, stability, and response time.
L117: Discuss the impact of rapid ambient‑temperature changes on detector responsivity.
L130: The timing‑accuracy statement is redundant, given that bandwidth was already defined.
L137: Clarify why a modulation current of 15 mA was chosen. The modulation index appears sub‑optimal in Fig. 2c.
L237: Remove “per minute” and “an in line”.
L245: Discuss the sensor response to sudden changes in pressure/flow/temperature.
L249: The calibration uncertainties (e.g., ~0.45 ppm at 2.5 ppm CH₄) are substantial. More detailed assessment at ambient CH₄ levels is needed.
L259: Additional in‑flight noise is attributed to inductive effects. How does this scale without real‑time data streaming? How can it be mitigated? Is this typical for UAV‑mounted spectrometers?
L269: Rephrase “flat region in the laboratory Allan measurements”.
L270: Specify the hardware and software filters used, including 3 dB roll‑off frequencies.
L271: Reconsider the wording of “limited or flattened noise”.
Fig. 4: Strong filtering is observed here but not in Norooz Oliaee et al. (2022, Fig. 6). The reported 1‑s precision is also four times worse despite filtering. This discrepancy needs discussion.
L280: To avoid confusion, use consistent units (L min⁻¹ instead of introducing SLPM).
L294: The UAV wind speed is sampled at 2 Hz while concentration data are at 10 Hz. Explain the motivation for these differing sampling rates.
L305: Clarify what “very low” refers to regarding the 0.5 kg h⁻¹ emission rate.
References:
Andersen, T., Vinkovic, K., Vries, M.d., Kers,B., Necki, J., Swolkien, J., Roiger, A., Peters, W., Chen, H.: Quantifying methane emissions from coal mining ventilation shafts using an unmanned aerial vehicle (UAV)-based active AirCore system, Atmos. Environ, https://doi.org/10.1016/j.aeaoa.2021.100135, 2021.
Morales, R., Ravelid, J., Vinkovic, K., Korben, P., Tuzson, B., Emmenegger, L., Chen, H., Schmidt, M., Humbel, S., Brunner, D., 2022. Controlled-release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources. Atmos. Meas. Tech. 15, 2177–2198. https://doi.org/10.5194/amt-15-2177-2022.