the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Jan 2026
Evaluating the performance of a UAV-based in situ methane sensor for quantifying point source emissions
Abstract. Methane (CH4) is the second most important greenhouse gas, and accurate quantification of its emissions is critical for mitigating climate change. In this study, we thoroughly evaluated the performance of an in situ CH4 sensor (Axetris) for quantifying anthropogenic CH4 emissions when deployed on an unmanned aerial vehicle (UAV). Sensor stability was assessed through laboratory tests under controlled and varying temperature conditions. Under stable conditions, the sensor achieved a precision of 63 ppb at 2 Hz. Furthermore, the tests revealed the necessity of temperature control and provided a water vapour correction term to ensure accurate measurements. Additionally, the sensor was used to quantify whole-farm CH4 emissions, yielding a mean flux of 4.1 ± 1.6 gCH4/s averaged over four flights. This mean flux was comparable to the value of 4.2 ± 1.1 gCH4/s obtained from the established AirCore technique. Finally, an uncertainty analysis based on the Ornstein-Uhlenbeck method was used to determine the influence of various sources of uncertainty. This analysis revealed that both wind-related uncertainties and background determination can significantly increase the overall uncertainty when not properly constrained. Furthermore, instrumental errors play a dominant role for smaller fluxes, while meteorological uncertainties remain significant even with repeated flights. Nevertheless, careful flight planning, e.g., ensuring extensive sampling outside of the plume and comprehensive wind monitoring, can reduce these uncertainties. Overall, our results demonstrate that a cost-effective sensor can provide reliable CH4 flux estimates with uncertainties comparable to those of established UAV-based systems.
Competing interests: One of the co-authors is on the editorial board of AMT
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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RC1: 'Comment on egusphere-2025-6209', Anonymous Referee #1, 27 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6209/egusphere-2025-6209-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-6209-RC1 -
RC2: 'Comment on egusphere-2025-6209', Anonymous Referee #2, 19 Feb 2026
Dear Editor,
In this article, the authors present a declination of an already existing methodology of methane emissions quantification with the use of an alternative instrumentation. They present laboratory and real fields tests of this instrumentation, to better characterize its specificities. They also performed real field quantifications of methane sources, with both the reference instrumentation and the newly tested instrumentation in parallel, which allows a direct comparison of the performances of each method. They also performed a detailed statistical analysis of some of the main sources of uncertainties associated with this emissions quantification, based on one of the tests performed on the field.
They highlight the strengths of this methodology and the strong interest of such low-cost instrumentation for a generalization of this type of measurements of methane emissions.
This paper has a great interest in the current context where many groups are developing airborne methods based on Uncrewed Aircraft Systems (UAS) to monitor methane emissions at the facility scale, filling a significant gap in the greenhouse gases emissions reporting. Furthermore, the emphasis on the uncertainty analysis is very beneficial as this is crucial for the validation of such method.
The topic of this article corresponds to the scope of Atmospheric Measurement Techniques. This paper is generally well written and very clear. However, there are still minor aspects which could be improved before final publication.
General comments
The authors presented intercomparaison of emissions quantifications based on Axetris and active AirCore measurements. For the data treatment of the Axetris measurements, the authors applied an H2O correction based on AirCore H2O measurements. What is the reliability of H2O measurements of an active AirCore? In the case where the Axetris would be used as a stand-alone instrument without AirCore, the authors suggest that the H2O values can be derived from model or nearby meteorological station values. But they don’t present any estimates of the performance of the quantification method using such values, compared to AirCord H2O measurements. It would be interesting to evaluate the performances of the method without having access to AirCore H2O measurement, as the Axetris should at the end be employed as a standalone instrument. An alternative could also be to embark an extra H2O sensor on-board the UAS on top of the Axetris. It would also be interesting to compare the AirCore H2O measurements with the humidity values measured by the embarked Trisonica mini, if they have been logged.
Despite the good quality of the monitoring methodology and the uncertainty analysis, there are still some gaps in the protocol and the analysis, which are still limiting the scope of this study. One would expect more emphasis on these limits in the discussion.
Concerning the characterization of the instrument: the authors performed an experiment to evaluate the stability of the analyser to variations of the temperature of the air surrounding the instrument, but the temperature of the incoming air is not mentioned (probably laboratory temperature) and also not varying. This would be useful to test the influence of the temperature of the incoming air, as it might probably influence the cell temperature stability: different temperature gaps between the instrument regulated temperature and the incoming air temperature could be tested, to simulate the behaviour of the instrument in different types of environments or seasonality. One could also extent these tests to measure the impact of temporally varying temperatures on one side (simulating the variations which might be encountered if measurements are performed in an industrial environment with warm plumes (under the wind of natural gas flares for example). Regarding the humidity sensitivity experiment, the authors proposed a humidity generation method based on a heated wet paper towel. This allows a strong variation of the humidity of the incoming air, but the variation of the temperature of the incoming air is not discussed. Could it have an impact of the spectroscopic response of the instrument? Ideally, humidity sensitivity tests rather be performed at constant temperature. This also justifies the need of the already mentioned sensitivity test to incoming temperature.
Concerning the uncertainty analysis, it is based on the analysis of one case based on a single flight. Although it provides an overall good interpretation of the relative contributions of the different sources of uncertainty for this case, the conclusions are therefore difficult to generalize to any monitoring case, where many parameters might be different such as types of sources (punctual or diffuse, low or high elevations, with/without ejection speed, single or multiple sources), different topographies, presence of buildings/obstacles, varying distances between source and monitoring plane, different wind conditions (mean speed and turbulence). This uncertainty analysis could also be further developed to study the influence of other sources of uncertainties in this case, such as the quality of the wind direction measurements, or of the quality of the H2O measurements.
To put the uncertainty analysis of this single case in a broader context, it would be interesting to compare these uncertainties with the variability of the quantifications obtained between repeated flights on the same source. Are the estimated levels of uncertainties coherent with the real observed variability?
The proposed uncertainty analysis is limited to one case and does not allow a quantification of uncertainty for any single flight. I would also like to see in the discussions if the authors think that the proposed uncertainty analysis method could be applied to any single flight to evaluate the uncertainty of the flux quantification for each individual flight.
Specific comments
Wording: I think the gender-neutral policies of EGU suggests using the term “Uncrewed Aircraft Systems (UAS)” instead of “UAV”. Also replace “drone” with the appropriate term (either UAV or UAS) throughout the text.
Line 131: “the ground time » > the ground team?
Line 200: It seems that the LI-7810 Is used to monitor the dry-air CH4 concentrations, but the Licor is technically also measuring wet air. What correction did you use to calculate the dry-air CH4 concentrations from Licor measurements? Is it a manufacturer correction? Did you validate this function?
Lines 212-215: I agree with the following remarks that the errors introduced by water corrections would be negligible compared to other uncertainties. However, wouldn’t it be possible to use the same protocol at different CH4 concentrations to check the stability of this correction?
Line 250 (Figure 2): It is regrettable that the time series of the in-flight tests are not shown here, as for the laboratory tests.
Line 255-265: It is not clear whether the incoming air sample temperature was stable or varying as well during the tests. On the field, one does not necessarily expect strong air temperature changes during a flight (at least as long as no warm source such as fires or industrial sources are monitored), but there might also be a strong temperature difference between the cell temperature and the outside air temperature. Would it also affect the measurements or is the cell temperature regulation sufficient to compensate important outside-air to cell-temperature gradients?
Line 324: “was derived as the average the WindMaster Pro observations” > “the average of the”
Line 358-370: Information is missing here about the distance between source and observational plane.
Line 430: This method might work but is subject to interpretation and is time consuming. Alternative baseline fitting methods exist which could be employed to estimate the background concentrations, even with noisy datasets once the appropriate parameters have been found. They could be applied either to individual transects or the complete time series of observations at once.
Line 460: « completed eliminated » > « completely eliminated »
Line 467-468: In this complete section, the authors present the example of the second flight only, but here there is a mix of results from all flights and individual flights. It should be more coherent. Furthermore, the mean difference of all flights is discussed, but the average value does not appear either in the text at this point or in the Table. I suggest moving these remarks to the following paragraphs treating all flights (lines 486-499).
Line 470: The authors are referring to Figure D1 (appendix), where the legend could be more precise in terms of period of observations: what time period is exactly represented (it is the complete day or only the period when the flights occurred)? From observations at which frequency?
They could also refer to Table 1 where the mean wind speed and directions of each flight is presented.Line 471-472: The information about flight plan should be presented earlier in the “Fight strategy” section, in particular the distance between source and observations which is lacking in this section. You are also referring to Appendix G, where the figure are lacking a colorbar for CH4 concentrations (or you could only present UAS positions instead of CH4 values, which are hardly readable).
Line 615-625: Wind speed uncertainty here only represents the uncertainty of the mean wind speed. However, wind uncertainty can also affect the wind direction, which is not considered here. This would be interesting to add this additional uncertainty into the analysis.
Line 631: correct “may stem from to changing”
Line 639: “to accurate resolve” > “to accurately resolve”
Line 642: “improved spatial resolution the nominal plume” > “improved spatial resolution of the nominal plume”?
Line 676-691: I agree with the interpretation that the uncertainties associated with noise level and background concentrations estimate will probably be the factors that play a role in the dependency of the uncertainty to the emission rate. However, this would have been preferable to perform the same analysis for each individual source of uncertainty, to really justify this assumption.
Line 732: define “IGA »
Line 740 : « the use if a method » > “the use of a method”
Citation: https://doi.org/10.5194/egusphere-2025-6209-RC2 -
RC3: 'Comment on egusphere-2025-6209', Anonymous Referee #3, 20 Feb 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6209/egusphere-2025-6209-RC3-supplement.pdf
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