{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T07:01:05Z","timestamp":1780297265615,"version":"3.54.0"},"reference-count":37,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ60188"],"award-info":[{"award-number":["2023JJ60188"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ60190"],"award-info":[{"award-number":["2023JJ60190"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transactions in GIS"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Among various carbon dioxide (CO\n                    <jats:sub>2<\/jats:sub>\n                    ) monitoring approaches, satellite observation and ground\u2010based observations are two widely adopted and reliable methods. However, the former is constrained by relatively low accuracy, whereas the latter suffers from limited spatial coverage. Therefore, this study develops a hybrid spatiotemporal modeling method integrating two Transformer networks on the basis of heterogeneous graphs to enhance the Copernicus atmosphere monitoring service global greenhouse gas reanalysis version 4 (CAMS\u2010EGG4) dataset by fusing orbiting carbon observatory\u20102 (OCO\u20102) satellite observations. Dynamic heterogeneous spatiotemporal graphs are constructed by integrating spatial proximity with corresponding temporal nodes, enabling an explicit representation of the intrinsic spatiotemporal dependencies in atmospheric CO\n                    <jats:sub>2<\/jats:sub>\n                    observations. This approach maintains the advantage of broad spatial coverage while achieving accuracy comparable to ground\u2010based observations. The proposed model demonstrates high predictive performance, with a coefficient of determination (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    ) of 0.97, a root mean square error (RMSE) of 0.99, and a mean absolute error (MAE) of 0.79. We further benchmarked the proposed model against four machine learning approaches, including random forest (RF), extreme gradient boosting (XGBoost), convolutional neural network (CNN), and graph convolutional network (GCN). Experimental results show that the Dual\u2010Transformer model consistently outperforms the four approaches in terms of\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    , RMSE, and MAE. In addition, validation against ground\u2010based measurements from the total carbon column observing network (TCCON) confirms that the predicted XCO\n                    <jats:sub>2<\/jats:sub>\n                    data exhibit substantial improvements over the original dataset. This methodological advance offers a scalable foundation for constructing refined CO\n                    <jats:sub>2<\/jats:sub>\n                    reanalysis products, with broad applicability in atmospheric science, earth system modeling, and evidence\u2010based climate policy development.\n                  <\/jats:p>","DOI":"10.1111\/tgis.70275","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T05:36:22Z","timestamp":1777700182000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Dual\u2010Transformer Network for Spatiotemporal Modeling of Carbon Dioxide Column Concentration (\n                    <scp>\n                      XCO\n                      <sub>2<\/sub>\n                    <\/scp>\n                    ) on the Basis of Dynamic Heterogeneous Graphs"],"prefix":"10.1111","volume":"30","author":[{"given":"Lifeng","family":"Yin","sequence":"first","affiliation":[{"name":"School of Software, Dalian Jiaotong University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunchang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Software, Dalian Jiaotong University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Surveying and Mapping Geography Hunan Vocational College of Engineering  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shujun","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Surveying and Mapping Geography Hunan Vocational College of Engineering  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenxi","family":"Fang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), School of Geosciences and Info\u2010Physics Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-9359","authenticated-orcid":false,"given":"Baoyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), School of Geosciences and Info\u2010Physics Central South University  Changsha China"},{"name":"Technology Innovation Center for Natural Ecosystem Carbon Sink, Ministry of Natural Resources  Kunming China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.5194\/acp\u201023\u20103829\u20102023"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489\u2010023\u201005053\u2010x"},{"key":"e_1_2_10_4_1","unstructured":"Copernicus Climate Change Service.2018.\u201cERA5 Hourly Data on Single Levels From 1940 to Present. Copernicus Climate Data Store (CDS).\u201dhttps:\/\/doi.org\/10.24381\/cds.Adbb2d47."},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt\u20105\u2010687\u20102012"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.2898457"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2025.06.033"},{"key":"e_1_2_10_8_1","unstructured":"Gori M. G.Monfardini andF.Scarselli.2005.\u201cA New Model for Learning in Graph Domains Proceedings.\u201d2005 IEEE International Joint Conference on Neural Networks 2005."},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11053\u2010022\u201010088\u2010x"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.5194\/gmd\u201011\u20103515\u20102018"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.103859"},{"key":"e_1_2_10_12_1","unstructured":"Hu Z. Y.Dong K.Wang andY.Sun.2020.\u201cHeterogeneous Graph Transformer.\u201dThe Web Conference 2020\u2013Proceedings of the World Wide Web Conference WWW 2020 29th International World Wide Web Conference WWW 2020 April 20 2020 \u2010 April 24 2020 Taipei Taiwan."},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14174422"},{"key":"e_1_2_10_14_1","unstructured":"Kipf T. N. andM.Welling.2017.\u201cSemi\u2010Supervised Classification With Graph Convolutional Networks.\u201d5th International Conference on Learning Representations ICLR 2017.https:\/\/doi.org\/10.48550\/arXiv.1609.02907."},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489\u2010023\u201004871\u20103"},{"key":"e_1_2_10_16_1","unstructured":"Morino I. O.Uchino Y.Tsutsumi et\u00a0al.2019.\u201cProgress on GOSAT and GOSAT\u20102 FTS SWIR L2 Validation.\u201dAGU Fall Meeting Abstracts San Francisco CA USA.https:\/\/agu.confex.com\/agu\/fm19\/meetingapp.cgi\/Paper\/575868."},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2022.3178125"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13050899"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2025.3556309"},{"key":"e_1_2_10_20_1","unstructured":"OCO\u20102\/OCO\u20103 Science Team.2022.\u201cOCO\u20102 Level 2 Bias\u2010Corrected XCO2 and Other Select Fields From the Full\u2010Physics Retrieval Aggregated as Daily Files Retrospective Processing V11.1r.\u201dNASA Goddard Earth Sciences Data and Information Services Center.https:\/\/doi.org\/10.5067\/8e4vlck16o6q."},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt\u20105\u201099\u20102012"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0708986104"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1029\/2005jd006157"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/20964471.2022.2033149"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnse.2023.3343927"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.112032"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt\u201016\u20103173\u20102023"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.5194\/essd\u201014\u2010325\u20102022"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.104117"},{"key":"e_1_2_10_30_1","doi-asserted-by":"crossref","unstructured":"Toon G.2009.\u201cTotal Column Carbon Observing Network (TCCON) Advances in Imaging OSA Technical Digest (CD) Vancouver Canada.\u201dhttps:\/\/doi.org\/10.1364\/FTS.2009.JMA3.","DOI":"10.1364\/FTS.2009.JMA3"},{"key":"e_1_2_10_31_1","unstructured":"Total Carbon Column Observing Network Team.2022.\u201c2020 TCCON Data Release.\u201dCaltechDATA.https:\/\/doi.org\/10.14291\/tccon.Ggg2020."},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.2166\/hydro.2024.226"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt\u201014\u20106601\u20102021"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt\u201010\u20102209\u20102017"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19051118"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.2151\/sola.2009\u2010041"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs10010155"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1003\u20106326(23)66299\u20105"}],"container-title":["Transactions in GIS"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/tgis.70275","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/tgis.70275","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/tgis.70275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T06:12:33Z","timestamp":1780294353000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/tgis.70275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":37,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1111\/tgis.70275"],"URL":"https:\/\/doi.org\/10.1111\/tgis.70275","archive":["Portico"],"relation":{},"ISSN":["1361-1682","1467-9671"],"issn-type":[{"value":"1361-1682","type":"print"},{"value":"1467-9671","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"2025-12-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70275"}}