{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:27:38Z","timestamp":1773966458612,"version":"3.50.1"},"reference-count":86,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T00:00:00Z","timestamp":1698019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41890854"],"award-info":[{"award-number":["41890854"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771114"],"award-info":[{"award-number":["41771114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA23100202"],"award-info":[{"award-number":["XDA23100202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KPI005"],"award-info":[{"award-number":["KPI005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chinese Academy of Sciences Class A Strategic Pilot Science and Technology Project","award":["41890854"],"award-info":[{"award-number":["41890854"]}]},{"name":"Chinese Academy of Sciences Class A Strategic Pilot Science and Technology Project","award":["41771114"],"award-info":[{"award-number":["41771114"]}]},{"name":"Chinese Academy of Sciences Class A Strategic Pilot Science and Technology Project","award":["XDA23100202"],"award-info":[{"award-number":["XDA23100202"]}]},{"name":"Chinese Academy of Sciences Class A Strategic Pilot Science and Technology Project","award":["KPI005"],"award-info":[{"award-number":["KPI005"]}]},{"name":"Innovation Project of LREIS","award":["41890854"],"award-info":[{"award-number":["41890854"]}]},{"name":"Innovation Project of LREIS","award":["41771114"],"award-info":[{"award-number":["41771114"]}]},{"name":"Innovation Project of LREIS","award":["XDA23100202"],"award-info":[{"award-number":["XDA23100202"]}]},{"name":"Innovation Project of LREIS","award":["KPI005"],"award-info":[{"award-number":["KPI005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate global monitoring of carbon dioxide (CO2) is essential for understanding climate change and informing policy decisions. This study compares column-averaged dry-air mole fractions of CO2 (XCO2) between ACOS_L2_Lite_FP V9r for Japan\u2019s Greenhouse Gases Observing Satellite (GOSAT), OCO-2_L2_Lite_FP V10r for the USA\u2019s Orbiting Carbon Observatory-2 (OCO-2), and IAPCAS V2.0 for China\u2019s Carbon Dioxide Observation Satellite (TANSAT) collectively referred to as GOT, with data from the Total Carbon Column Observing Network (TCCON). Our findings are as follows: (1) Significant data quantity differences exist between OCO-2 and the other satellites, with OCO-2 boasting a data volume 100 times greater. GOT shows the highest data volume between 30\u201345\u00b0N and 20\u201330\u00b0S, but data availability is notably lower near the equator. (2) XCO2 from GOT exhibits similar seasonal variations, with lower concentrations during June, July, and August (JJA) (402.72\u2013403.74 ppm) and higher concentrations during December, January, and February (DJF) (405.74\u2013407.14 ppm). XCO2 levels are higher in the Northern Hemisphere during March, April, and May (MAM) and DJF, while slightly lower during JJA and September, October, and November (SON). (3) The differences in XCO2 (\u0394XCO2) reveal that \u0394XCO2 between OCO-2 and TANSAT are minor (\u22120.47 \u00b1 0.28 ppm), whereas the most significant difference is observed between GOSAT and TANSAT (\u22121.13 \u00b1 0.15 ppm). Minimal differences are seen in SON (with the biggest difference between GOSAT and TANSAT: \u22120.84 \u00b1 0.12 ppm), while notable differences occur in DJF (with the biggest difference between GOSAT and TANSAT: \u22121.43 \u00b1 0.17 ppm). Regarding latitudinal variations, distinctions between OCO-2 and TANSAT are most pronounced in JJA and SON. (4) Compared to TCCON, XCO2 from GOT exhibits relatively high determination coefficients (R2 &gt; 0.8), with GOSAT having the highest root mean square error (RMSE = 1.226 ppm, &lt;1.5 ppm), indicating a strong relationship between ground-based observed and retrieved values. This research contributes significantly to our understanding of the spatial characteristics of global XCO2. Furthermore, it offers insights that can inform the analysis of differences in the inversion of carbon sources and sinks within assimilation systems when incorporating XCO2 data from satellite observations.<\/jats:p>","DOI":"10.3390\/rs15205073","type":"journal-article","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T10:32:24Z","timestamp":1698057144000},"page":"5073","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Global Evaluation and Intercomparison of XCO2 Retrievals from GOSAT, OCO-2, and TANSAT with TCCON"],"prefix":"10.3390","volume":"15","author":[{"given":"Junjun","family":"Fang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8972-706X","authenticated-orcid":false,"given":"Baozhang","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Huifang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7295-0361","authenticated-orcid":false,"given":"Adil","family":"Dilawar","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3288-1485","authenticated-orcid":false,"given":"Man","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Geographical Sciences, Faculty of Science and Engineering, University of Nottingham, Ningbo 315100, China"}]},{"given":"Chunlin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Shu\u2019an","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Tewekel Melese","family":"Gemechu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xingying","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1080\/10643389.2010.497442","article-title":"Global Warming Effect of Leakage from CO2 Storage","volume":"41","author":"Lenzen","year":"2011","journal-title":"Crit. 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