Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane Article Swipe
Daniel J. Jacob
,
Daniel J. Varon
,
Daniel Cusworth
,
Philip E. Dennison
,
Christian Frankenberg
,
Ritesh Gautam
,
Luis Guanter
,
J. L. Kelley
,
Jason McKeever
,
Lesley Ott
,
Benjamin Poulter
,
Zhen Qu
,
Andrew K. Thorpe
,
John R. Worden
,
Riley Duren
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/acp-22-9617-2022
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/acp-22-9617-2022
We review the capability of current and scheduled satellite observations of atmospheric methane in the shortwave infrared (SWIR) to quantify methane emissions from the global scale down to point sources. We cover retrieval methods, precision and accuracy requirements, inverse and mass balance methods for inferring emissions, source detection thresholds, and observing system completeness. We classify satellite instruments as area flux mappers and point source imagers, with complementary attributes. Area flux mappers are high-precision (<1 %) instruments with 0.1–10 km pixel size designed to quantify total methane emissions on regional to global scales. Point source imagers are fine-pixel (<60 m) instruments designed to quantify individual point sources by imaging of the plumes. Current area flux mappers include GOSAT (2009–present), which provides a high-quality record for interpretation of long-term methane trends, and TROPOMI (2018–present), which provides global continuous daily mapping to quantify emissions on regional scales. These instruments already provide a powerful resource to quantify national methane emissions in support of the Paris Agreement. Current point source imagers include the GHGSat constellation and several hyperspectral and multispectral land imaging sensors (PRISMA, Sentinel-2, Landsat-8/9, WorldView-3), with detection thresholds in the 100–10 000 kg h−1 range that enable monitoring of large point sources. Future area flux mappers, including MethaneSAT, GOSAT-GW, Sentinel-5, GeoCarb, and CO2M, will increase the capability to quantify emissions at high resolution, and the MERLIN lidar will improve observation of the Arctic. The averaging times required by area flux mappers to quantify regional emissions depend on pixel size, retrieval precision, observation density, fraction of successful retrievals, and return times in a way that varies with the spatial resolution desired. A similar interplay applies to point source imagers between detection threshold, spatial coverage, and return time, defining an observing system completeness. Expanding constellations of point source imagers including GHGSat and Carbon Mapper over the coming years will greatly improve observing system completeness for point sources through dense spatial coverage and frequent return times.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/acp-22-9617-2022
- OA Status
- gold
- Cited By
- 290
- References
- 137
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4288422980
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4288422980Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/acp-22-9617-2022Digital Object Identifier
- Title
-
Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methaneWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-29Full publication date if available
- Authors
-
Daniel J. Jacob, Daniel J. Varon, Daniel Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, J. L. Kelley, Jason McKeever, Lesley Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, Riley DurenList of authors in order
- Landing page
-
https://doi.org/10.5194/acp-22-9617-2022Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/acp-22-9617-2022Direct OA link when available
- Concepts
-
Remote sensing, Environmental science, Satellite, Methane, Atmospheric methane, Flux (metallurgy), Hyperspectral imaging, Atmospheric correction, Meteorology, Geography, Ecology, Aerospace engineering, Metallurgy, Materials science, Engineering, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
290Total citation count in OpenAlex
- Citations by year (recent)
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2025: 96, 2024: 108, 2023: 71, 2022: 14, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
137Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.at | 218 |
| abstract_inverted_index.by | 107, 235 |
| abstract_inverted_index.in | 14, 157, 186, 258 |
| abstract_inverted_index.kg | 190 |
| abstract_inverted_index.km | 79 |
| abstract_inverted_index.m) | 99 |
| abstract_inverted_index.of | 5, 11, 109, 126, 159, 196, 228, 252, 291 |
| abstract_inverted_index.on | 88, 142, 244 |
| abstract_inverted_index.to | 19, 28, 83, 90, 102, 139, 152, 215, 239, 272 |
| abstract_inverted_index.000 | 189 |
| abstract_inverted_index.The | 231 |
| abstract_inverted_index.and | 7, 36, 40, 50, 62, 130, 171, 174, 209, 221, 255, 281, 297, 317 |
| abstract_inverted_index.are | 72, 96 |
| abstract_inverted_index.for | 44, 124, 310 |
| abstract_inverted_index.the | 3, 15, 24, 110, 160, 168, 187, 213, 222, 229, 264, 301 |
| abstract_inverted_index.way | 260 |
| abstract_inverted_index.Area | 69 |
| abstract_inverted_index.area | 59, 113, 201, 236 |
| abstract_inverted_index.down | 27 |
| abstract_inverted_index.flux | 60, 70, 114, 202, 237 |
| abstract_inverted_index.from | 23 |
| abstract_inverted_index.high | 219 |
| abstract_inverted_index.land | 176 |
| abstract_inverted_index.mass | 41 |
| abstract_inverted_index.over | 300 |
| abstract_inverted_index.size | 81 |
| abstract_inverted_index.that | 193, 261 |
| abstract_inverted_index.will | 211, 225, 304 |
| abstract_inverted_index.with | 66, 77, 183, 263 |
| abstract_inverted_index.CO2M, | 210 |
| abstract_inverted_index.GOSAT | 117 |
| abstract_inverted_index.Paris | 161 |
| abstract_inverted_index.Point | 93 |
| abstract_inverted_index.These | 145 |
| abstract_inverted_index.cover | 32 |
| abstract_inverted_index.daily | 137 |
| abstract_inverted_index.dense | 314 |
| abstract_inverted_index.h−1 | 191 |
| abstract_inverted_index.large | 197 |
| abstract_inverted_index.lidar | 224 |
| abstract_inverted_index.pixel | 80, 245 |
| abstract_inverted_index.point | 29, 63, 105, 164, 198, 273, 292, 311 |
| abstract_inverted_index.range | 192 |
| abstract_inverted_index.scale | 26 |
| abstract_inverted_index.size, | 246 |
| abstract_inverted_index.time, | 283 |
| abstract_inverted_index.times | 233, 257 |
| abstract_inverted_index.total | 85 |
| abstract_inverted_index.which | 119, 133 |
| abstract_inverted_index.years | 303 |
| abstract_inverted_index.(<1 | 74 |
| abstract_inverted_index.(SWIR) | 18 |
| abstract_inverted_index.Carbon | 298 |
| abstract_inverted_index.Future | 200 |
| abstract_inverted_index.GHGSat | 169, 296 |
| abstract_inverted_index.MERLIN | 223 |
| abstract_inverted_index.Mapper | 299 |
| abstract_inverted_index.coming | 302 |
| abstract_inverted_index.depend | 243 |
| abstract_inverted_index.enable | 194 |
| abstract_inverted_index.global | 25, 91, 135 |
| abstract_inverted_index.record | 123 |
| abstract_inverted_index.return | 256, 282, 319 |
| abstract_inverted_index.review | 2 |
| abstract_inverted_index.source | 47, 64, 94, 165, 274, 293 |
| abstract_inverted_index.system | 52, 287, 308 |
| abstract_inverted_index.times. | 320 |
| abstract_inverted_index.varies | 262 |
| abstract_inverted_index.(<60 | 98 |
| abstract_inverted_index.Arctic. | 230 |
| abstract_inverted_index.Current | 112, 163 |
| abstract_inverted_index.TROPOMI | 131 |
| abstract_inverted_index.already | 147 |
| abstract_inverted_index.applies | 271 |
| abstract_inverted_index.balance | 42 |
| abstract_inverted_index.between | 276 |
| abstract_inverted_index.current | 6 |
| abstract_inverted_index.greatly | 305 |
| abstract_inverted_index.imagers | 95, 166, 275, 294 |
| abstract_inverted_index.imaging | 108, 177 |
| abstract_inverted_index.improve | 226, 306 |
| abstract_inverted_index.include | 116, 167 |
| abstract_inverted_index.inverse | 39 |
| abstract_inverted_index.mappers | 61, 71, 115, 238 |
| abstract_inverted_index.mapping | 138 |
| abstract_inverted_index.methane | 13, 21, 86, 128, 155 |
| abstract_inverted_index.methods | 43 |
| abstract_inverted_index.plumes. | 111 |
| abstract_inverted_index.provide | 148 |
| abstract_inverted_index.scales. | 92, 144 |
| abstract_inverted_index.sensors | 178 |
| abstract_inverted_index.several | 172 |
| abstract_inverted_index.similar | 269 |
| abstract_inverted_index.sources | 106, 312 |
| abstract_inverted_index.spatial | 265, 279, 315 |
| abstract_inverted_index.support | 158 |
| abstract_inverted_index.through | 313 |
| abstract_inverted_index.trends, | 129 |
| abstract_inverted_index.(PRISMA, | 179 |
| abstract_inverted_index.0.1–10 | 78 |
| abstract_inverted_index.100–10 | 188 |
| abstract_inverted_index.GeoCarb, | 208 |
| abstract_inverted_index.accuracy | 37 |
| abstract_inverted_index.classify | 55 |
| abstract_inverted_index.coverage | 316 |
| abstract_inverted_index.defining | 284 |
| abstract_inverted_index.density, | 250 |
| abstract_inverted_index.designed | 82, 101 |
| abstract_inverted_index.desired. | 267 |
| abstract_inverted_index.fraction | 251 |
| abstract_inverted_index.frequent | 318 |
| abstract_inverted_index.imagers, | 65 |
| abstract_inverted_index.increase | 212 |
| abstract_inverted_index.infrared | 17 |
| abstract_inverted_index.mappers, | 203 |
| abstract_inverted_index.methods, | 34 |
| abstract_inverted_index.national | 154 |
| abstract_inverted_index.powerful | 150 |
| abstract_inverted_index.provides | 120, 134 |
| abstract_inverted_index.quantify | 20, 84, 103, 140, 153, 216, 240 |
| abstract_inverted_index.regional | 89, 143, 241 |
| abstract_inverted_index.required | 234 |
| abstract_inverted_index.resource | 151 |
| abstract_inverted_index.sources. | 30, 199 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Expanding | 289 |
| abstract_inverted_index.GOSAT-GW, | 206 |
| abstract_inverted_index.averaging | 232 |
| abstract_inverted_index.coverage, | 280 |
| abstract_inverted_index.detection | 48, 184, 277 |
| abstract_inverted_index.emissions | 22, 87, 141, 156, 217, 242 |
| abstract_inverted_index.including | 204, 295 |
| abstract_inverted_index.inferring | 45 |
| abstract_inverted_index.interplay | 270 |
| abstract_inverted_index.long-term | 127 |
| abstract_inverted_index.observing | 51, 286, 307 |
| abstract_inverted_index.precision | 35 |
| abstract_inverted_index.retrieval | 33, 247 |
| abstract_inverted_index.satellite | 9, 56 |
| abstract_inverted_index.scheduled | 8 |
| abstract_inverted_index.shortwave | 16 |
| abstract_inverted_index.Agreement. | 162 |
| abstract_inverted_index.capability | 4, 214 |
| abstract_inverted_index.continuous | 136 |
| abstract_inverted_index.emissions, | 46 |
| abstract_inverted_index.fine-pixel | 97 |
| abstract_inverted_index.individual | 104 |
| abstract_inverted_index.monitoring | 195 |
| abstract_inverted_index.precision, | 248 |
| abstract_inverted_index.resolution | 266 |
| abstract_inverted_index.successful | 253 |
| abstract_inverted_index.threshold, | 278 |
| abstract_inverted_index.thresholds | 185 |
| abstract_inverted_index.MethaneSAT, | 205 |
| abstract_inverted_index.Sentinel-2, | 180 |
| abstract_inverted_index.Sentinel-5, | 207 |
| abstract_inverted_index.atmospheric | 12 |
| abstract_inverted_index.attributes. | 68 |
| abstract_inverted_index.instruments | 57, 76, 100, 146 |
| abstract_inverted_index.observation | 227, 249 |
| abstract_inverted_index.resolution, | 220 |
| abstract_inverted_index.retrievals, | 254 |
| abstract_inverted_index.thresholds, | 49 |
| abstract_inverted_index.Landsat-8/9, | 181 |
| abstract_inverted_index.completeness | 309 |
| abstract_inverted_index.high-quality | 122 |
| abstract_inverted_index.observations | 10 |
| abstract_inverted_index.WorldView-3), | 182 |
| abstract_inverted_index.complementary | 67 |
| abstract_inverted_index.completeness. | 53, 288 |
| abstract_inverted_index.constellation | 170 |
| abstract_inverted_index.hyperspectral | 173 |
| abstract_inverted_index.multispectral | 175 |
| abstract_inverted_index.requirements, | 38 |
| abstract_inverted_index.constellations | 290 |
| abstract_inverted_index.high-precision | 73 |
| abstract_inverted_index.interpretation | 125 |
| abstract_inverted_index.(2009–present), | 118 |
| abstract_inverted_index.(2018–present), | 132 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5065467022, https://openalex.org/A5080250417, https://openalex.org/A5068521942, https://openalex.org/A5055655717, https://openalex.org/A5029437860 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I122411786, https://openalex.org/I1306266525, https://openalex.org/I1334627681, https://openalex.org/I136199984, https://openalex.org/I4210131182, https://openalex.org/I60053951 |
| citation_normalized_percentile.value | 0.99924164 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |