Inferring methane emissions from African livestock by fusing drone, tower, and satellite data
Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Copernicus Publications
2025
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| Acceso en línea: | https://hdl.handle.net/10568/180521 |
| _version_ | 1855517051901181952 |
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| author | Hove, A. Aalstad, K. Lind, Vibeke Arndt, Claudia Odongo, Vincent O. Ceriani, Rodolfo Fava, Francesco P. Hulth, J. Pirk, N. |
| author_browse | Aalstad, K. Arndt, Claudia Ceriani, Rodolfo Fava, Francesco P. Hove, A. Hulth, J. Lind, Vibeke Odongo, Vincent O. Pirk, N. |
| author_facet | Hove, A. Aalstad, K. Lind, Vibeke Arndt, Claudia Odongo, Vincent O. Ceriani, Rodolfo Fava, Francesco P. Hulth, J. Pirk, N. |
| author_sort | Hove, A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. We used our novel Bayesian inference approach to estimate emissions from various ruminant livestock species in Kenya, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our Bayesian estimates aligned with Tier 2 emission values of the Intergovernmental Panel on Climate Change. In addition, we observed the hypothesized increase in methane emissions after feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a conventional mass balance approach using drone observations. Furthermore, the Bayesian inference method performed better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 g h−1. We found a ± 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduced to ± 12 % for stronger sources, like cattle herds emitting 1000–1500 g h−1. Finally, we showed that radiance anomalies identified in hyperspectral satellite data can inform the planning of flight paths for targeted drone missions in areas where source locations are unknown, as these anomalies may serve as indicators of potential methane sources. These promising results demonstrate the efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the improvement of emission inventories and verification of other emission estimation methods. |
| format | Journal Article |
| id | CGSpace180521 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Copernicus Publications |
| publisherStr | Copernicus Publications |
| record_format | dspace |
| spelling | CGSpace1805212026-01-23T13:04:58Z Inferring methane emissions from African livestock by fusing drone, tower, and satellite data Hove, A. Aalstad, K. Lind, Vibeke Arndt, Claudia Odongo, Vincent O. Ceriani, Rodolfo Fava, Francesco P. Hulth, J. Pirk, N. enteric methane rangelands pastoral systems Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. We used our novel Bayesian inference approach to estimate emissions from various ruminant livestock species in Kenya, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our Bayesian estimates aligned with Tier 2 emission values of the Intergovernmental Panel on Climate Change. In addition, we observed the hypothesized increase in methane emissions after feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a conventional mass balance approach using drone observations. Furthermore, the Bayesian inference method performed better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 g h−1. We found a ± 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduced to ± 12 % for stronger sources, like cattle herds emitting 1000–1500 g h−1. Finally, we showed that radiance anomalies identified in hyperspectral satellite data can inform the planning of flight paths for targeted drone missions in areas where source locations are unknown, as these anomalies may serve as indicators of potential methane sources. These promising results demonstrate the efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the improvement of emission inventories and verification of other emission estimation methods. 2025-08-26 2026-01-23T13:04:57Z 2026-01-23T13:04:57Z Journal Article https://hdl.handle.net/10568/180521 en Open Access Copernicus Publications van Hove, A., Aalstad, K., Lind, V., Arndt, C., Odongo, V., Ceriani, R., Fava, F., Hulth, J., and Pirk, N. 2025. Inferring methane emissions from African livestock by fusing drone, tower, and satellite data. Biogeosciences 22: 16. |
| spellingShingle | enteric methane rangelands pastoral systems Hove, A. Aalstad, K. Lind, Vibeke Arndt, Claudia Odongo, Vincent O. Ceriani, Rodolfo Fava, Francesco P. Hulth, J. Pirk, N. Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_full | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_fullStr | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_full_unstemmed | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_short | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_sort | inferring methane emissions from african livestock by fusing drone tower and satellite data |
| topic | enteric methane rangelands pastoral systems |
| url | https://hdl.handle.net/10568/180521 |
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