Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output
Abstract Due to the coarse spatial resolution, the column‐averaged dry‐air mole fraction of CO2 (XCO2) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO2. We developed a machine learning model to fill the data gaps in the Orbiting Carbon Observatory 2 satellit...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
|
| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2022GL098435 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591220168196096 |
|---|---|
| author | Changpei He Mingrui Ji Tao Li Xinyi Liu Die Tang Shifu Zhang Yuzhou Luo Michael L. Grieneisen Zihang Zhou Yu Zhan |
| author_facet | Changpei He Mingrui Ji Tao Li Xinyi Liu Die Tang Shifu Zhang Yuzhou Luo Michael L. Grieneisen Zihang Zhou Yu Zhan |
| author_sort | Changpei He |
| collection | DOAJ |
| description | Abstract Due to the coarse spatial resolution, the column‐averaged dry‐air mole fraction of CO2 (XCO2) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO2. We developed a machine learning model to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals across China during 2015–2018, with cross‐validation R2 = 0.95 and RMSE = 0.91 ppm. Based on the gap‐filled data set, the multiyear average XCO2 was the highest in East China (405.71 ± 3.72 ppm) and the lowest in Northwest China (403.99 ± 3.47 ppm). At the national level, the multiyear seasonal XCO2 varied from 402.54 ± 3.95 ppm in summer to 406.28 ± 3.19 ppm in spring. While the XCO2 kept increasing, the rate of increase declined from 3.23 to 2.10 ppm/year. The machine learning approach is feasible for downscaling and calibrating the CarbonTracker XCO2 data. The full‐coverage and fine‐scale XCO2 data set is expected to advance our understanding of the carbon cycles. |
| format | Article |
| id | doaj-art-34f37c98447d4c0a8696087981a4891e |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-34f37c98447d4c0a8696087981a4891e2025-01-22T14:38:16ZengWileyGeophysical Research Letters0094-82761944-80072022-06-014912n/an/a10.1029/2022GL098435Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker OutputChangpei He0Mingrui Ji1Tao Li2Xinyi Liu3Die Tang4Shifu Zhang5Yuzhou Luo6Michael L. Grieneisen7Zihang Zhou8Yu Zhan9Department of Environmental Science and Engineering Sichuan University Chengdu Sichuan ChinaDepartment of Environmental Science and Engineering Sichuan University Chengdu Sichuan ChinaDepartment of Environmental Science and Engineering Sichuan University Chengdu Sichuan ChinaDepartment of Environmental Science and Engineering Sichuan University Chengdu Sichuan ChinaDepartment of Environmental Science and Engineering Sichuan University Chengdu Sichuan ChinaDepartment of Environmental Science and Engineering Sichuan University Chengdu Sichuan ChinaDepartment of Land, Air, and Water Resources University of California Davis CA USADepartment of Land, Air, and Water Resources University of California Davis CA USAChengdu Academy of Environmental Sciences Chengdu ChinaDepartment of Environmental Science and Engineering Sichuan University Chengdu Sichuan ChinaAbstract Due to the coarse spatial resolution, the column‐averaged dry‐air mole fraction of CO2 (XCO2) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO2. We developed a machine learning model to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals across China during 2015–2018, with cross‐validation R2 = 0.95 and RMSE = 0.91 ppm. Based on the gap‐filled data set, the multiyear average XCO2 was the highest in East China (405.71 ± 3.72 ppm) and the lowest in Northwest China (403.99 ± 3.47 ppm). At the national level, the multiyear seasonal XCO2 varied from 402.54 ± 3.95 ppm in summer to 406.28 ± 3.19 ppm in spring. While the XCO2 kept increasing, the rate of increase declined from 3.23 to 2.10 ppm/year. The machine learning approach is feasible for downscaling and calibrating the CarbonTracker XCO2 data. The full‐coverage and fine‐scale XCO2 data set is expected to advance our understanding of the carbon cycles.https://doi.org/10.1029/2022GL098435 |
| spellingShingle | Changpei He Mingrui Ji Tao Li Xinyi Liu Die Tang Shifu Zhang Yuzhou Luo Michael L. Grieneisen Zihang Zhou Yu Zhan Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output Geophysical Research Letters |
| title | Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output |
| title_full | Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output |
| title_fullStr | Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output |
| title_full_unstemmed | Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output |
| title_short | Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output |
| title_sort | deriving full coverage and fine scale xco2 across china based on oco 2 satellite retrievals and carbontracker output |
| url | https://doi.org/10.1029/2022GL098435 |
| work_keys_str_mv | AT changpeihe derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT mingruiji derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT taoli derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT xinyiliu derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT dietang derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT shifuzhang derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT yuzhouluo derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT michaellgrieneisen derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT zihangzhou derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput AT yuzhan derivingfullcoverageandfinescalexco2acrosschinabasedonoco2satelliteretrievalsandcarbontrackeroutput |