Skip to content
DraconiticResearch
Search Sources API
186,541 studies31 sourcesUpdated 17 min ago
PreprintCC BY 4.0EarthArXiv

Advances in UAV-Based Methane Emission Monitoring: Sensors, Sampling Geometry, and Quantification

Abstract

Unmanned aerial vehicles (UAVs) are one key platforms for monitoring methane due to their flexible deployment with spatial scales between ground-based sensors and crewed aircraft This review synthesises research on UAV sensing technologies, flight and sampling strategies, quantification methods, and validation practices. It finds that quantitative performance depends on the combined choice of sensor, platform, flight geometry, wind measurement, and estimation method. The key UAV-deployable sensor categories are contrasted based on sensitivity, response, payload restrictions, and field of use, which includes open-path tunable diode laser absorption spectroscopy (TDLAS) and closed-path TDLAS, quantum cascade laser absorption spectroscopy (QCLAS), active AirCore, non-dispersive infrared (NDIR), and thermal imaging sensors. Multirotor UAVs support repeated transects and vertical plume sampling, while fixed-wing platforms are primarily suited to broader-area screening. Mass-balance and Gaussian inversion methods can estimate emission rates, but their results are sensitive to wind uncertainty and incomplete plume coverage. Tracer-based approaches can reduce direct reliance on wind measurements when tracer and methane plumes disperse together. Controlled-release studies also show that small average bias can coexist with substantial errors in individual flights. Across sectors, methods are more developed for oil and gas facilities, landfills, and coal-mine ventilation shafts than for agriculture and wastewater. The literature reviewed identified no dedicated peer-reviewed UAV methane quantification study at an active surface coal mine. Wider use of UAVs for emissions assessment will require survey designs matched to source characteristics, stronger field validation, and more consistent uncertainty reporting.

Related

  1. Automated identification, industrial attribution and conditional emission-rate quantification of recurrent CO enhancement source regions from TROPOMI observations
    Haoyi Wu, Renwei Meng
    EarthArXivPreprintPublisher termsFull textAtmospheric science & meteorologyEarthArXiv
  2. Effects of waveguide tube geometry and surrounding medium conditions on acoustic emission signal attenuation
    Fengling Chen, Shuyuan Duan, Guodong Zhang
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textEnvironmental science & pollutionCrossref
  3. Sampling and extraction methods for soil microplastics: current advances and limitations
    Tilahun Kasa, Daniel Reddythota, Melsew Endalew Berihun +2
    Environmental ChallengesPeer-reviewedCC BYFull textEnvironmental science & pollutionDOAJ
  4. Auditing Representation-Induced Geometry in Acoustic-Emission Fracture Transients
    Jose Sanchez-Andreu
    arXivPreprintarXiv licenceFull textGeophysics & seismologyCondensed matterarXiv
  5. Reliable VOC Monitoring in Industrial Coastal Areas: Uncertainty Quantification and Variability in the Berre Basin
    Zahra Benmouhoub, Liselotte Tinel, Grégory Gille +3
    Atmospheric Measurement Techniques DiscussionsPeer-reviewedCC BYFull textTheoretical & computational chemistryClimate scienceHAL
BibTeX
@misc{bhandari2026advances,
  title = {{Advances in UAV-Based Methane Emission Monitoring: Sensors, Sampling Geometry, and Quantification}},
  author = {Nabin Bhandari and Akshansha Chauhan and Simit Raval},
  howpublished = {EarthArXiv},
  year = {2026},
  month = oct,
  doi = {10.31223/x5ff8z},
  url = {https://eartharxiv.org/repository/view/15851/},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Advances in UAV-Based Methane Emission Monitoring: Sensors, Sampling Geometry, and Quantification
AU  - Nabin Bhandari
AU  - Akshansha Chauhan
AU  - Simit Raval
PY  - 2026
DA  - 2026/10/30
PB  - EarthArXiv
DO  - 10.31223/x5ff8z
UR  - https://eartharxiv.org/repository/view/15851/
AB  - Unmanned aerial vehicles (UAVs) are one key platforms for monitoring methane due to their flexible deployment with spatial scales between ground-based sensors and crewed aircraft This review synthesises research on UAV sensing technologies, flight and sampling strategies, quantification methods, and validation practices. It finds that quantitative performance depends on the combined choice of sensor, platform, flight geometry, wind measurement, and estimation method. The key UAV-deployable sensor categories are contrasted based on sensitivity, response, payload restrictions, and field of use, which includes open-path tunable diode laser absorption spectroscopy (TDLAS) and closed-path TDLAS, quantum cascade laser absorption spectroscopy (QCLAS), active AirCore, non-dispersive infrared (NDIR), and thermal imaging sensors. Multirotor UAVs support repeated transects and vertical plume sampling, while fixed-wing platforms are primarily suited to broader-area screening. Mass-balance and Gaussian inversion methods can estimate emission rates, but their results are sensitive to wind uncertainty and incomplete plume coverage. Tracer-based approaches can reduce direct reliance on wind measurements when tracer and methane plumes disperse together. Controlled-release studies also show that small average bias can coexist with substantial errors in individual flights. Across sectors, methods are more developed for oil and gas facilities, landfills, and coal-mine ventilation shafts than for agriculture and wastewater. The literature reviewed identified no dedicated peer-reviewed UAV methane quantification study at an active surface coal mine. Wider use of UAVs for emissions assessment will require survey designs matched to source characteristics, stronger field validation, and more consistent uncertainty reporting.
ER  -