{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T01:51:33Z","timestamp":1790041893788,"version":"4.0.1"},"reference-count":107,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"DOI":"10.1038\/s41597-024-04135-w","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T12:31:44Z","timestamp":1739536304000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Global Daily Column Average CO2 at 0.1\u00b0\u2009\u00d7\u20090.1\u00b0 Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning"],"prefix":"10.1038","volume":"12","author":[{"given":"Franz Pablo","family":"Antezana Lopez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2789-6989","authenticated-orcid":false,"given":"Guanhua","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guifei","family":"Jing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangfu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumin","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"4135_CR1","doi-asserted-by":"publisher","unstructured":"Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022 \u2013 Impacts, Adaptation and Vulnerability. https:\/\/doi.org\/10.1017\/9781009325844 (Cambridge University Press, 2023).","DOI":"10.1017\/9781009325844"},{"key":"4135_CR2","doi-asserted-by":"publisher","first-page":"123006","DOI":"10.1088\/1748-9326\/ab4557","volume":"14","author":"GC Hegerl","year":"2019","unstructured":"Hegerl, G. C. et al. Causes of climate change over the historical record. Environmental Research Letters 14, 123006, https:\/\/doi.org\/10.1088\/1748-9326\/ab4557 (2019).","journal-title":"Environmental Research Letters"},{"key":"4135_CR3","doi-asserted-by":"publisher","first-page":"4422","DOI":"10.3390\/rs14174422","volume":"14","author":"Y Jiang","year":"2022","unstructured":"Jiang, Y., Gao, Z., He, J., Wu, J. & Christakos, G. Application and Analysis of XCO2 Data from OCO Satellite Using a Synthetic DINEOF\u2013BME Spatiotemporal Interpolation Framework. Remote Sens (Basel) 14, 4422, https:\/\/doi.org\/10.3390\/rs14174422 (2022).","journal-title":"Remote Sens (Basel)"},{"key":"4135_CR4","doi-asserted-by":"publisher","unstructured":"Adak, S., Mandal, N., Mukhopadhyay, A., Maity, P. P. & Sen, S. Current State and Prediction of Future Global Climate Change and Variability in Terms of CO2 Levels and Temperature. in Enhancing Resilience of Dryland Agriculture Under Changing Climate 15\u201343 https:\/\/doi.org\/10.1007\/978-981-19-9159-2_2 (Springer Nature Singapore, 2023).","DOI":"10.1007\/978-981-19-9159-2_2"},{"key":"4135_CR5","doi-asserted-by":"publisher","unstructured":"Keeling, Ralph F; Keeling, Charles D. Atmospheric Monthly In Situ CO2 Data - Mauna Loa Observatory, Hawaii (Archive 2024-11-13). In Scripps CO2 Program Data. UC San Diego Library Digital Collections. https:\/\/doi.org\/10.6075\/J08W3BHW (2017).","DOI":"10.6075\/J08W3BHW"},{"key":"4135_CR6","doi-asserted-by":"publisher","first-page":"7","DOI":"10.5604\/01.3001.0016.3124","volume":"66","author":"J Kozyra","year":"2023","unstructured":"Kozyra, J., \u0141ukasik, Z., Ku\u015bmi\u0144ska-Fija\u0142kowska, A. & Janota, A. Reduction of CO2 emission as a result of the use of 48-volt electrical installations in passenger cars. Archives of Transport 66, 7\u201320, https:\/\/doi.org\/10.5604\/01.3001.0016.3124 (2023).","journal-title":"Archives of Transport"},{"key":"4135_CR7","unstructured":"Recent global CO2. GOSAT [online]. National Institute for Environmental Studies (NIES). https:\/\/www.gosat.nies.go.jp\/en\/recent-global-co2.html."},{"key":"4135_CR8","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1007\/s11430-013-4807-y","volume":"57","author":"L Lei","year":"2014","unstructured":"Lei, L. et al. A comparison of atmospheric CO2 concentration GOSAT-based observations and model simulations. Sci China Earth Sci 57, 1393\u20131402, https:\/\/doi.org\/10.1007\/s11430-013-4807-y (2014).","journal-title":"Sci China Earth Sci"},{"key":"4135_CR9","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1080\/09692290.2023.2167849","volume":"31","author":"JM Leal","year":"2024","unstructured":"Leal, J. M. & Paterson, M. Transnational city networks, global political economy, and climate governance: C40 in Mexico and Lima. Rev Int Polit Econ 31, 26\u201346, https:\/\/doi.org\/10.1080\/09692290.2023.2167849 (2024).","journal-title":"Rev Int Polit Econ"},{"key":"4135_CR10","doi-asserted-by":"publisher","first-page":"5261","DOI":"10.5194\/amt-15-5261-2022","volume":"15","author":"E Potier","year":"2022","unstructured":"Potier, E. et al. Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale. Atmos Meas Tech 15, 5261\u20135288, https:\/\/doi.org\/10.5194\/amt-15-5261-2022 (2022).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR11","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.5194\/amt-10-2209-2017","volume":"10","author":"D Wunch","year":"2017","unstructured":"Wunch, D. et al. Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) CO2 measurements with TCCON. Atmos Meas Tech 10, 2209\u20132238, https:\/\/doi.org\/10.5194\/amt-10-2209-2017 (2017).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR12","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.rse.2013.04.024","volume":"162","author":"M Buchwitz","year":"2015","unstructured":"Buchwitz, M. et al. The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets. Remote Sens Environ 162, 344\u2013362, https:\/\/doi.org\/10.1016\/j.rse.2013.04.024 (2015).","journal-title":"Remote Sens Environ"},{"key":"4135_CR13","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.5194\/amt-16-3173-2023","volume":"16","author":"TE Taylor","year":"2023","unstructured":"Taylor, T. E. et al. Evaluating the consistency between OCO-2 and OCO-3 XCO 2 estimates derived from the NASA ACOS version 10 retrieval algorithm. Atmos Meas Tech 16, 3173\u20133209, https:\/\/doi.org\/10.5194\/amt-16-3173-2023 (2023).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR14","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.5194\/amt-16-1461-2023","volume":"16","author":"S Mauceri","year":"2023","unstructured":"Mauceri, S., Massie, S. & Schmidt, S. Correcting 3D cloud effects in X CO2 retrievals from the Orbiting Carbon Observatory-2 (OCO-2). Atmos Meas Tech 16, 1461\u20131476, https:\/\/doi.org\/10.5194\/amt-16-1461-2023 (2023).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR15","doi-asserted-by":"publisher","first-page":"687","DOI":"10.5194\/amt-5-687-2012","volume":"5","author":"D Crisp","year":"2012","unstructured":"Crisp, D. et al. The ACOS CO2 retrieval algorithm \u2013 Part II: Global XCO2 data characterization. Atmos Meas Tech 5, 687\u2013707, https:\/\/doi.org\/10.5194\/amt-5-687-2012 (2012).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR16","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1007\/s11434-014-0215-8","volume":"59","author":"Y Liu","year":"2014","unstructured":"Liu, Y. et al. Effects of spectral sampling rate and range of CO2 absorption bands on XCO2 retrieval from TanSat hyperspectral spectrometer. Chinese Science Bulletin 59, 1485\u20131491, https:\/\/doi.org\/10.1007\/s11434-014-0215-8 (2014).","journal-title":"Chinese Science Bulletin"},{"key":"4135_CR17","doi-asserted-by":"publisher","first-page":"5227","DOI":"10.5194\/amt-9-5227-2016","volume":"9","author":"B Connor","year":"2016","unstructured":"Connor, B. et al. Quantification of uncertainties in OCO-2 measurements of XCO2 simulations and linear error analysis. Atmos Meas Tech 9, 5227\u20135238, https:\/\/doi.org\/10.5194\/amt-9-5227-2016 (2016).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.isprsjprs.2022.10.012","volume":"194","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Yuan, Q., Zhou, S. & Zhang, L. Global spatiotemporal completion of daily high-resolution TCCO from TROPOMI over land using a swath-based local ensemble learning method. ISPRS Journal of Photogrammetry and Remote Sensing 194, 167\u2013180, https:\/\/doi.org\/10.1016\/j.isprsjprs.2022.10.012 (2022).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"4135_CR19","doi-asserted-by":"publisher","unstructured":"Park, M., Randel, W. J., Emmons, L. K. & Livesey, N. J. Transport pathways of carbon monoxide in the Asian summer monsoon diagnosed from Model of Ozone and Related Tracers (MOZART). Journal of Geophysical Research: Atmospheres 114 https:\/\/doi.org\/10.1029\/2008JD010621 (2009).","DOI":"10.1029\/2008JD010621"},{"key":"4135_CR20","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.1029\/2018JD029151","volume":"124","author":"W Tang","year":"2019","unstructured":"Tang, W. et al. Source Contributions to Carbon Monoxide Concentrations During KORUS\u2010AQ Based on CAM\u2010chem Model Applications. Journal of Geophysical Research: Atmospheres 124, 2796\u20132822, https:\/\/doi.org\/10.1029\/2018JD029151 (2019).","journal-title":"Journal of Geophysical Research: Atmospheres"},{"key":"4135_CR21","doi-asserted-by":"publisher","first-page":"E2669","DOI":"10.1175\/BAMS-D-21-0315.1","volume":"103","author":"C Buontempo","year":"2022","unstructured":"Buontempo, C. et al. The Copernicus Climate Change Service: Climate Science in Action. Bull Am Meteorol Soc 103, E2669\u2013E2687, https:\/\/doi.org\/10.1175\/BAMS-D-21-0315.1 (2022).","journal-title":"Bull Am Meteorol Soc"},{"key":"4135_CR22","doi-asserted-by":"publisher","first-page":"E2650","DOI":"10.1175\/BAMS-D-21-0314.1","volume":"103","author":"V-H Peuch","year":"2022","unstructured":"Peuch, V.-H. et al. The Copernicus Atmosphere Monitoring Service: From Research to Operations. Bull Am Meteorol Soc 103, E2650\u2013E2668, https:\/\/doi.org\/10.1175\/BAMS-D-21-0314.1 (2022).","journal-title":"Bull Am Meteorol Soc"},{"key":"4135_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.159588","volume":"858","author":"M Zhang","year":"2023","unstructured":"Zhang, M. & Liu, G. Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019. Science of The Total Environment 858, 159588, https:\/\/doi.org\/10.1016\/j.scitotenv.2022.159588 (2023).","journal-title":"Science of The Total Environment"},{"key":"4135_CR24","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.5194\/gmd-15-2183-2022","volume":"15","author":"A Barth","year":"2022","unstructured":"Barth, A., Alvera-Azc\u00e1rate, A., Troupin, C. & Beckers, J.-M. DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations. Geosci Model Dev 15, 2183\u20132196, https:\/\/doi.org\/10.5194\/gmd-15-2183-2022 (2022).","journal-title":"Geosci Model Dev"},{"key":"4135_CR25","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.5194\/gmd-13-1609-2020","volume":"13","author":"A Barth","year":"2020","unstructured":"Barth, A., Alvera-Azc\u00e1rate, A., Licer, M. & Beckers, J.-M. DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations. Geosci Model Dev 13, 1609\u20131622, https:\/\/doi.org\/10.5194\/gmd-13-1609-2020 (2020).","journal-title":"Geosci Model Dev"},{"key":"4135_CR26","doi-asserted-by":"publisher","first-page":"C03008","DOI":"10.1029\/2006JC003660","volume":"112","author":"A Alvera-Azc\u00e1rate","year":"2007","unstructured":"Alvera-Azc\u00e1rate, A., Barth, A., Beckers, J.-M. & Weisberg, R. H. Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields. J Geophys Res 112, C03008, https:\/\/doi.org\/10.1029\/2006JC003660 (2007).","journal-title":"J Geophys Res"},{"key":"4135_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.12681\/mms.64","volume":"12","author":"A Alvera-Azc\u00e1rate","year":"2011","unstructured":"Alvera-Azc\u00e1rate, A., Barth, A., Sirjacobs, D., Lenartz, F. & Beckers, J. M. Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses. Mediterr Mar Sci 12, 5, https:\/\/doi.org\/10.12681\/mms.64 (2011).","journal-title":"Mediterr Mar Sci"},{"key":"4135_CR28","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.ocemod.2004.08.001","volume":"9","author":"A Alvera-Azc\u00e1rate","year":"2005","unstructured":"Alvera-Azc\u00e1rate, A., Barth, A., Rixen, M. & Beckers, J. M. Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature. Ocean Model (Oxf) 9, 325\u2013346, https:\/\/doi.org\/10.1016\/j.ocemod.2004.08.001 (2005).","journal-title":"Ocean Model (Oxf)"},{"key":"4135_CR29","doi-asserted-by":"publisher","first-page":"2622","DOI":"10.3390\/rs14112622","volume":"14","author":"FPA Lopez","year":"2022","unstructured":"Lopez, F. P. A., Zhou, G., Jing, G., Zhang, K. & Tan, Y. XCO2 and XCH4 Reconstruction Using GOSAT Satellite Data Based on EOF-Algorithm. Remote Sens (Basel) 14, 2622, https:\/\/doi.org\/10.3390\/rs14112622 (2022).","journal-title":"Remote Sens (Basel)"},{"key":"4135_CR30","doi-asserted-by":"publisher","first-page":"743","DOI":"10.3390\/jmse11040743","volume":"11","author":"X Yan","year":"2023","unstructured":"Yan, X. et al. Application of Synthetic DINCAE\u2013BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll\u2013a from Satellite Observations in the Arabian Sea. J Mar Sci Eng 11, 743, https:\/\/doi.org\/10.3390\/jmse11040743 (2023).","journal-title":"J Mar Sci Eng"},{"key":"4135_CR31","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1162\/003465303765299945","volume":"85","author":"CJ Murray","year":"2003","unstructured":"Murray, C. J. Cyclical Properties of Baxter-King Filtered Time Series. Review of Economics and Statistics 85, 472\u2013476, https:\/\/doi.org\/10.1162\/003465303765299945 (2003).","journal-title":"Review of Economics and Statistics"},{"key":"4135_CR32","doi-asserted-by":"publisher","first-page":"2445","DOI":"10.5194\/amt-9-2445-2016","volume":"9","author":"A Kuze","year":"2016","unstructured":"Kuze, A. et al. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space. Atmos Meas Tech 9, 2445\u20132461, https:\/\/doi.org\/10.5194\/amt-9-2445-2016 (2016).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR33","doi-asserted-by":"publisher","first-page":"3837","DOI":"10.5194\/amt-14-3837-2021","volume":"14","author":"S No\u00ebl","year":"2021","unstructured":"No\u00ebl, S. et al. XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm. Atmos Meas Tech 14, 3837\u20133869, https:\/\/doi.org\/10.5194\/amt-14-3837-2021 (2021).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR34","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.5194\/amt-14-2013-2021","volume":"14","author":"H Suto","year":"2021","unstructured":"Suto, H. et al. Thermal and near-infrared sensor for carbon observation Fourier transform spectrometer-2 (TANSO-FTS-2) on the Greenhouse gases Observing SATellite-2 (GOSAT-2) during its first year in orbit. Atmos Meas Tech 14, 2013\u20132039, https:\/\/doi.org\/10.5194\/amt-14-2013-2021 (2021).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR35","doi-asserted-by":"publisher","unstructured":"Nguyen, P., Shivadekar, S., Laya Chukkapalli, S. S. & Halem, M. Satellite Data Fusion of Multiple Observed XCO2 using Compressive Sensing and Deep Learning. in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium 2073\u20132076 https:\/\/doi.org\/10.1109\/IGARSS39084.2020.9323861 (IEEE, 2020).","DOI":"10.1109\/IGARSS39084.2020.9323861"},{"key":"4135_CR36","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. in 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28 (2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4135_CR37","doi-asserted-by":"publisher","unstructured":"Laughner, J. L. et al. The Total Carbon Column Observing Network's GGG2020 data version. Earth Syst. Sci. Data 16, 2197\u20132260. https:\/\/doi.org\/10.5194\/essd-16-2197-2024 (2024).","DOI":"10.5194\/essd-16-2197-2024"},{"key":"4135_CR38","doi-asserted-by":"publisher","unstructured":"Hamazaki, T., Kaneko, Y., Kuze, A. & Kondo, K. Fourier transform spectrometer for Greenhouse Gases Observing Satellite (GOSAT). in (eds. Komar, G. J., Wang, J. & Kimura, T.) 73 https:\/\/doi.org\/10.1117\/12.581198 (2005).","DOI":"10.1117\/12.581198"},{"key":"4135_CR39","doi-asserted-by":"publisher","first-page":"160","DOI":"10.2151\/sola.2009-041","volume":"5","author":"T Yokota","year":"2009","unstructured":"Yokota, T. et al. Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results. SOLA 5, 160\u2013163, https:\/\/doi.org\/10.2151\/sola.2009-041 (2009).","journal-title":"SOLA"},{"key":"4135_CR40","doi-asserted-by":"publisher","first-page":"5399","DOI":"10.5194\/amt-15-5399-2022","volume":"15","author":"H Suto","year":"2022","unstructured":"Suto, H. et al. Updated spectral radiance calibration on TIR bands for TANSO-FTS-2 onboard GOSAT-2. Atmos Meas Tech 15, 5399\u20135413, https:\/\/doi.org\/10.5194\/amt-15-5399-2022 (2022).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR41","doi-asserted-by":"publisher","unstructured":"Matsunaga, T. & Tanimoto, H. Greenhouse gas observation by TANSO-3 onboard GOSAT-GW. in Sensors, Systems, and Next-Generation Satellites XXVI (eds. Kimura, T., Babu, S. R. & H\u00e9li\u00e8re, A.) 16 https:\/\/doi.org\/10.1117\/12.2639221 (SPIE, 2022).","DOI":"10.1117\/12.2639221"},{"key":"4135_CR42","doi-asserted-by":"publisher","first-page":"717","DOI":"10.5194\/amt-4-717-2011","volume":"4","author":"Y Yoshida","year":"2011","unstructured":"Yoshida, Y. et al. Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite. Atmos Meas Tech 4, 717\u2013734, https:\/\/doi.org\/10.5194\/amt-4-717-2011 (2011).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR43","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1080\/01431161.2015.1011792","volume":"36","author":"H Watanabe","year":"2015","unstructured":"Watanabe, H. et al. Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method. Int J Remote Sens 36, 1509\u20131528, https:\/\/doi.org\/10.1080\/01431161.2015.1011792 (2015).","journal-title":"Int J Remote Sens"},{"key":"4135_CR44","doi-asserted-by":"publisher","first-page":"4310","DOI":"10.1002\/ece3.823","volume":"3","author":"Y Xu","year":"2013","unstructured":"Xu, Y. et al. Satellite\u2010derived estimations of spatial and seasonal variation in tropospheric carbon dioxide mass over China. Ecol Evol 3, 4310\u20134325, https:\/\/doi.org\/10.1002\/ece3.823 (2013).","journal-title":"Ecol Evol"},{"key":"4135_CR45","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.5194\/amt-12-2341-2019","volume":"12","author":"A Eldering","year":"2019","unstructured":"Eldering, A., Taylor, T. E., O\u2019Dell, C. W. & Pavlick, R. The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data. Atmos Meas Tech 12, 2341\u20132370, https:\/\/doi.org\/10.5194\/amt-12-2341-2019 (2019).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR46","doi-asserted-by":"publisher","unstructured":"Yanhui, P., Xiaoying, L., Xian, Y. & Lei, Z. Analysis of Influence to the Solar Array Output by Satellite Attitude Maneuver in Sun Synchronous Orbit. in 3372\u20133379. https:\/\/doi.org\/10.1007\/978-981-16-9492-9_331 (2022).","DOI":"10.1007\/978-981-16-9492-9_331"},{"key":"4135_CR47","doi-asserted-by":"publisher","unstructured":"Srivastava, P. et al. Establishing Launch Readiness of NASA ISS Instrument OCO-3. in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium 6101\u20136104 https:\/\/doi.org\/10.1109\/IGARSS39084.2020.9323631 (IEEE, 2020).","DOI":"10.1109\/IGARSS39084.2020.9323631"},{"key":"4135_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.112032","volume":"251","author":"TE Taylor","year":"2020","unstructured":"Taylor, T. E. et al. OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals. Remote Sens Environ 251, 112032, https:\/\/doi.org\/10.1016\/j.rse.2020.112032 (2020).","journal-title":"Remote Sens Environ"},{"key":"4135_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2021.112314","volume":"258","author":"M Kiel","year":"2021","unstructured":"Kiel, M. et al. Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity. Remote Sens Environ 258, 112314, https:\/\/doi.org\/10.1016\/j.rse.2021.112314 (2021).","journal-title":"Remote Sens Environ"},{"key":"4135_CR50","doi-asserted-by":"publisher","first-page":"5547","DOI":"10.5194\/amt-12-5547-2019","volume":"12","author":"JK Hedelius","year":"2019","unstructured":"Hedelius, J. K. et al. Evaluation of MOPITT Version 7 joint TIR\u2013NIR X&lt;sub&gt;CO&lt;\/sub&gt; retrievals with TCCON. Atmos Meas Tech 12, 5547\u20135572, https:\/\/doi.org\/10.5194\/amt-12-5547-2019 (2019).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR51","doi-asserted-by":"publisher","first-page":"2241","DOI":"10.5194\/amt-12-2241-2019","volume":"12","author":"M Kiel","year":"2019","unstructured":"Kiel, M. et al. How bias correction goes wrong: measurement of XCO2; affected by erroneous surface pressure estimates. Atmos Meas Tech 12, 2241\u20132259, https:\/\/doi.org\/10.5194\/amt-12-2241-2019 (2019).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR52","doi-asserted-by":"publisher","first-page":"5507","DOI":"10.5194\/amt-11-5507-2018","volume":"11","author":"T Borsdorff","year":"2018","unstructured":"Borsdorff, T. et al. Mapping carbon monoxide pollution from space down to city scales with daily global coverage. Atmos Meas Tech 11, 5507\u20135518, https:\/\/doi.org\/10.5194\/amt-11-5507-2018 (2018).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR53","doi-asserted-by":"publisher","first-page":"6539","DOI":"10.5194\/amt-11-6539-2018","volume":"11","author":"CW O\u2019Dell","year":"2018","unstructured":"O\u2019Dell, C. W. et al. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm. Atmos Meas Tech 11, 6539\u20136576, https:\/\/doi.org\/10.5194\/amt-11-6539-2018 (2018).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR54","doi-asserted-by":"publisher","first-page":"12317","DOI":"10.5194\/acp-11-12317-2011","volume":"11","author":"D Wunch","year":"2011","unstructured":"Wunch, D. et al. A method for evaluating bias in global measurements of CO&lt;sub&gt;2&lt;\/sub&gt; total columns from space. Atmos Chem Phys 11, 12317\u201312337, https:\/\/doi.org\/10.5194\/acp-11-12317-2011 (2011).","journal-title":"Atmos Chem Phys"},{"key":"4135_CR55","unstructured":"Total Carbon Column Observing Network (TCCON) Team. (2022). 2020 TCCON Data Release (2020)."},{"key":"4135_CR56","doi-asserted-by":"publisher","unstructured":"Morino, I., Velazco, V. A., Hori, A., Uchino, O., & Griffith, D. W. T. TCCON data from Burgos, Ilocos Norte (PH), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.burgos01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.burgos01.R0"},{"key":"4135_CR57","doi-asserted-by":"publisher","unstructured":"Wennberg, P. O. et al. TCCON data from Caltech (US), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.pasadena01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.pasadena01.R0"},{"key":"4135_CR58","doi-asserted-by":"publisher","unstructured":"Deutscher, N. M. et al. TCCON data from Darwin (AU), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.darwin01.R0 (2023).","DOI":"10.14291\/tccon.ggg2020.darwin01.R0"},{"key":"4135_CR59","doi-asserted-by":"publisher","unstructured":"Wunch, D., Mendonca, J., Colebatch, O., Allen, N. T., Blavier, J.-F., Kunz, K., Roche, S., Hedelius, J., Neufeld, G., Springett, S., Worthy, D., Kessler, R., & Strong, K. TCCON data from East Trout Lake, SK (CA), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.easttroutlake01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.easttroutlake01.R0"},{"key":"4135_CR60","doi-asserted-by":"publisher","unstructured":"Sussmann, R., & Rettinger, M. TCCON data from Garmisch (DE), Release GGG2020.R1 (Versi\u00f3n R1) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.garmisch01.R0 (2025).","DOI":"10.14291\/tccon.ggg2020.garmisch01.R0"},{"key":"4135_CR61","doi-asserted-by":"publisher","unstructured":"Weidmann, D., Brownsword, R., & Doniki, S. TCCON data from Harwell, Oxfordshire (UK), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.harwell01.R0 (2023).","DOI":"10.14291\/tccon.ggg2020.harwell01.R0"},{"key":"4135_CR62","doi-asserted-by":"publisher","unstructured":"Liu, C., Wang, W., Sun, Y., & Shan, C. TCCON data from Hefei (PRC), Release GGG2020.R1 (Versi\u00f3n R1) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.hefei01.R1 (2023).","DOI":"10.14291\/tccon.ggg2020.hefei01.R1"},{"key":"4135_CR63","doi-asserted-by":"publisher","unstructured":"Garc\u00eda, O. E., Schneider, M., Herkommer, B., Gross, J., Hase, F., Blumenstock, T., & Sep\u00falveda, E. TCCON data from Izana (ES), Release GGG2020.R1 (Versi\u00f3n R1) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.izana01.R1 (2022).","DOI":"10.14291\/tccon.ggg2020.izana01.R1"},{"key":"4135_CR64","doi-asserted-by":"publisher","unstructured":"Wennberg, P. O., Roehl, C. M., Blavier, J.-F., Wunch, D., & Allen, N. T. TCCON data from Jet Propulsion Laboratory (US), 2011, Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.jpl01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.jpl01.R0"},{"key":"4135_CR65","doi-asserted-by":"publisher","unstructured":"Wennberg, P. O. et al. TCCON data from Jet Propulsion Laboratory (US), 2007, Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.jpl02.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.jpl02.R0"},{"key":"4135_CR66","doi-asserted-by":"publisher","unstructured":"Hase, F. et al. TCCON data from Karlsruhe (DE), Release GGG2020.R2 (Versi\u00f3n R2) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.karlsruhe01.R2 (2024).","DOI":"10.14291\/tccon.ggg2020.karlsruhe01.R2"},{"key":"4135_CR67","doi-asserted-by":"publisher","unstructured":"Wennberg, P. O., Wunch, D., Roehl, C. M., Blavier, J.-F., Toon, G. C., & Allen, N. T. TCCON data from Lamont (US), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.lamont01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.lamont01.R0"},{"key":"4135_CR68","doi-asserted-by":"publisher","unstructured":"Sherlock, V. et al. TCCON data from Lauder (NZ), 120HR, Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.lauder01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.lauder01.R0"},{"key":"4135_CR69","doi-asserted-by":"publisher","unstructured":"Sherlock, V. et al. TCCON data from Lauder (NZ), 125HR, Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.lauder02.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.lauder02.R0"},{"key":"4135_CR70","doi-asserted-by":"publisher","unstructured":"Pollard, D. F., Robinson, J., & Shiona, H. TCCON data from Lauder (NZ), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.lauder03.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.lauder03.R0"},{"key":"4135_CR71","doi-asserted-by":"publisher","unstructured":"Petri, C. et al. TCCON data from Nicosia (CY), Release GGG2020.R1 (Versi\u00f3n R1) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.nicosia01.R1 (2024).","DOI":"10.14291\/tccon.ggg2020.nicosia01.R1"},{"key":"4135_CR72","doi-asserted-by":"publisher","unstructured":"Warneke, T., Petri, C., Notholt, J., & Buschmann, M. TCCON data from Orl\u00e9ans (FR), Release GGG2020.R1 (Versi\u00f3n R1) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.orleans01.R0 (2024).","DOI":"10.14291\/tccon.ggg2020.orleans01.R0"},{"key":"4135_CR73","doi-asserted-by":"publisher","unstructured":"Wennberg, P. O. et al. TCCON data from Park Falls (US), Release GGG2020.R1 (Versi\u00f3n R1) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.parkfalls01.R1 (2022).","DOI":"10.14291\/tccon.ggg2020.parkfalls01.R1"},{"key":"4135_CR74","doi-asserted-by":"publisher","unstructured":"De Mazi\u00e8re, M. et al. TCCON data from R\u00e9union Island (RE), Release GGG2020.R0 (Versi\u00f3n R0) https:\/\/doi.org\/10.14291\/tccon.ggg2020.reunion01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.reunion01.R0"},{"key":"4135_CR75","doi-asserted-by":"publisher","unstructured":"Morino, I., Ohyama, H., Hori, A., & Ikegami, H. TCCON data from Rikubetsu (JP), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.rikubetsu01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.rikubetsu01.R0"},{"key":"4135_CR76","doi-asserted-by":"publisher","unstructured":"Shiomi, K. et al. TCCON data from Saga (JP), Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. https:\/\/doi.org\/10.14291\/tccon.ggg2020.saga01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.saga01.R0"},{"key":"4135_CR77","doi-asserted-by":"publisher","unstructured":"Morino, I., Ohyama, H., Hori, A., & Ikegami, H. TCCON data from Tsukuba (JP), 125HR, Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.tsukuba02.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.tsukuba02.R0"},{"key":"4135_CR78","doi-asserted-by":"publisher","unstructured":"Zhou, M., Wang, P., Kumps, N., Hermans, C., & Nan, W. TCCON data from Xianghe, China, Release GGG2020.R0 (Versi\u00f3n R0) [Data set]. CaltechDATA. https:\/\/doi.org\/10.14291\/tccon.ggg2020.xianghe01.R0 (2022).","DOI":"10.14291\/tccon.ggg2020.xianghe01.R0"},{"key":"4135_CR79","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.5194\/acp-19-3515-2019","volume":"19","author":"A Inness","year":"2019","unstructured":"Inness, A. et al. The CAMS reanalysis of atmospheric composition. Atmos Chem Phys 19, 3515\u20133556, https:\/\/doi.org\/10.5194\/acp-19-3515-2019 (2019).","journal-title":"Atmos Chem Phys"},{"key":"4135_CR80","doi-asserted-by":"publisher","first-page":"3829","DOI":"10.5194\/acp-23-3829-2023","volume":"23","author":"A Agust\u00ed-Panareda","year":"2023","unstructured":"Agust\u00ed-Panareda, A. et al. Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020. Atmos Chem Phys 23, 3829\u20133859, https:\/\/doi.org\/10.5194\/acp-23-3829-2023 (2023).","journal-title":"Atmos Chem Phys"},{"key":"4135_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.164921","volume":"893","author":"T Li","year":"2023","unstructured":"Li, T., Wu, J. & Wang, T. Generating daily high-resolution and full-coverage XCO2 across China from 2015 to 2020 based on OCO-2 and CAMS data. Science of The Total Environment 893, 164921, https:\/\/doi.org\/10.1016\/j.scitotenv.2023.164921 (2023).","journal-title":"Science of The Total Environment"},{"key":"4135_CR82","doi-asserted-by":"publisher","first-page":"5635","DOI":"10.3390\/rs14225635","volume":"14","author":"Y Chen","year":"2022","unstructured":"Chen, Y. et al. Global-Scale Evaluation of XCO2 Products from GOSAT, OCO-2 and CarbonTracker Using Direct Comparison and Triple Collocation Method. Remote Sens (Basel) 14, 5635, https:\/\/doi.org\/10.3390\/rs14225635 (2022).","journal-title":"Remote Sens (Basel)"},{"key":"4135_CR83","doi-asserted-by":"publisher","unstructured":"Basilio, R. R., Livermore, T. R., Shen, Y. J. & Pollock, H. R. The quest for an OCO (Orbiting Carbon Observatory) re-flight. in (eds. Picard, R. H., Sch\u00e4fer, K., Comeron, A. & van Weele, M.) 78270A https:\/\/doi.org\/10.1117\/12.867042 (2010).","DOI":"10.1117\/12.867042"},{"key":"4135_CR84","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.isprsjprs.2020.06.008","volume":"166","author":"S Sunder","year":"2020","unstructured":"Sunder, S., Ramsankaran, R. & Ramakrishnan, B. Machine learning techniques for regional scale estimation of high-resolution cloud-free daily sea surface temperatures from MODIS data. ISPRS Journal of Photogrammetry and Remote Sensing 166, 228\u2013240, https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.06.008 (2020).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"4135_CR85","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.isprsjprs.2020.06.019","volume":"167","author":"T Li","year":"2020","unstructured":"Li, T., Shen, H., Yuan, Q. & Zhang, L. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. ISPRS Journal of Photogrammetry and Remote Sensing 167, 178\u2013188, https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.06.019 (2020).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"4135_CR86","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.isprsjprs.2019.11.008","volume":"160","author":"E Kamir","year":"2020","unstructured":"Kamir, E., Waldner, F. & Hochman, Z. Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing 160, 124\u2013135, https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.11.008 (2020).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"4135_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2023.116866","volume":"236","author":"C Wu","year":"2023","unstructured":"Wu, C., Ju, Y., Yang, S., Zhang, Z. & Chen, Y. Reconstructing annual XCO2 at a 1 km\u00d71 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method. Environ Res 236, 116866, https:\/\/doi.org\/10.1016\/j.envres.2023.116866 (2023).","journal-title":"Environ Res"},{"key":"4135_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2023.108057","volume":"178","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Li, T., Wu, J. & Yang, H. Global estimates of gap-free and fine-scale CO2 concentrations during 2014\u20132020 from satellite and reanalysis data. Environ Int 178, 108057, https:\/\/doi.org\/10.1016\/j.envint.2023.108057 (2023).","journal-title":"Environ Int"},{"key":"4135_CR89","doi-asserted-by":"publisher","first-page":"3597","DOI":"10.5194\/essd-15-3597-2023","volume":"15","author":"Y Wang","year":"2023","unstructured":"Wang, Y. et al. Seamless mapping of long-term (2010\u20132020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method. Earth Syst Sci Data 15, 3597\u20133622, https:\/\/doi.org\/10.5194\/essd-15-3597-2023 (2023).","journal-title":"Earth Syst Sci Data"},{"key":"4135_CR90","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.isprsjprs.2024.01.009","volume":"208","author":"J Chen","year":"2024","unstructured":"Chen, J. et al. Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model. ISPRS Journal of Photogrammetry and Remote Sensing 208, 107\u2013120, https:\/\/doi.org\/10.1016\/j.isprsjprs.2024.01.009 (2024).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"4135_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.139290","volume":"428","author":"Q He","year":"2023","unstructured":"He, Q. et al. Full-coverage mapping high-resolution atmospheric CO2 concentrations in China from 2015 to 2020: Spatiotemporal variations and coupled trends with particulate pollution. J Clean Prod 428, 139290, https:\/\/doi.org\/10.1016\/j.jclepro.2023.139290 (2023).","journal-title":"J Clean Prod"},{"key":"4135_CR92","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.isprsjprs.2019.03.011","volume":"151","author":"F Yao","year":"2019","unstructured":"Yao, F., Wu, J., Li, W. & Peng, J. A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China. ISPRS Journal of Photogrammetry and Remote Sensing 151, 263\u2013276, https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.03.011 (2019).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"4135_CR93","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.apenergy.2018.10.083","volume":"235","author":"S Wang","year":"2019","unstructured":"Wang, S., Shi, C., Fang, C. & Feng, K. Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model. Appl Energy 235, 95\u2013105, https:\/\/doi.org\/10.1016\/j.apenergy.2018.10.083 (2019).","journal-title":"Appl Energy"},{"key":"4135_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.142206","volume":"754","author":"Q Liu","year":"2021","unstructured":"Liu, Q., Wu, S., Lei, Y., Li, S. & Li, L. Exploring spatial characteristics of city-level CO2 emissions in China and their influencing factors from global and local perspectives. Science of The Total Environment 754, 142206, https:\/\/doi.org\/10.1016\/j.scitotenv.2020.142206 (2021).","journal-title":"Science of The Total Environment"},{"key":"4135_CR95","doi-asserted-by":"publisher","first-page":"142","DOI":"10.3390\/rs9020142","volume":"9","author":"H Nguyen","year":"2017","unstructured":"Nguyen, H., Cressie, N. & Braverman, A. Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets. Remote Sens (Basel) 9, 142, https:\/\/doi.org\/10.3390\/rs9020142 (2017).","journal-title":"Remote Sens (Basel)"},{"key":"4135_CR96","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1080\/14786440109462720","volume":"2","author":"K Pearson","year":"1901","unstructured":"Pearson, K. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 2, 559\u2013572, https:\/\/doi.org\/10.1080\/14786440109462720 (1901).","journal-title":"The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science"},{"key":"4135_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.164750","volume":"892","author":"Z Gao","year":"2023","unstructured":"Gao, Z., Jiang, Y., He, J. & Wu, J. Spatiotemporal variation analysis of global XCO2 concentration during 2010\u20132020 based on DINEOF-BME framework and wavelet function. Science of The Total Environment 892, 164750, https:\/\/doi.org\/10.1016\/j.scitotenv.2023.164750 (2023).","journal-title":"Science of The Total Environment"},{"key":"4135_CR98","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770\u2013778 https:\/\/doi.org\/10.1109\/CVPR.2016.90 (IEEE, 2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"4135_CR99","doi-asserted-by":"publisher","unstructured":"Liu, H., Jiang, B., Xiao, Y. & Yang, C. Coherent Semantic Attention for Image Inpainting. in 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) 4169\u20134178 https:\/\/doi.org\/10.1109\/ICCV.2019.00427 (IEEE, 2019).","DOI":"10.1109\/ICCV.2019.00427"},{"key":"4135_CR100","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Stanev, E. V. & Grayek, S. Reconstruction of the Basin\u2010Wide Sea\u2010Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks. J Geophys Res Oceans 125 https:\/\/doi.org\/10.1029\/2020JC016402 (2020).","DOI":"10.1029\/2020JC016402"},{"key":"4135_CR101","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3390\/rs10030485","volume":"10","author":"E Olmedo","year":"2018","unstructured":"Olmedo, E., Taupier-Letage, I., Turiel, A. & Alvera-Azc\u00e1rate, A. Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis. Remote Sens (Basel) 10, 485, https:\/\/doi.org\/10.3390\/rs10030485 (2018).","journal-title":"Remote Sens (Basel)"},{"key":"4135_CR102","doi-asserted-by":"publisher","first-page":"857","DOI":"10.5194\/essd-13-857-2021","volume":"13","author":"E Olmedo","year":"2021","unstructured":"Olmedo, E. et al. Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center. Earth Syst Sci Data 13, 857\u2013888, https:\/\/doi.org\/10.5194\/essd-13-857-2021 (2021).","journal-title":"Earth Syst Sci Data"},{"key":"4135_CR103","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.13895409","author":"FPA Lopez","year":"2024","unstructured":"Lopez, F. P. A. Daily Column average CO2 mapping combining OCO 3, GOSAT, and CAMS Data applying EOF and Deep learning from 2018 to 2023. Zenodo https:\/\/doi.org\/10.5281\/zenodo.13895409 (2024)."},{"key":"4135_CR104","doi-asserted-by":"publisher","unstructured":"Jing, Y., Shi, J., Zhang, P., Wang, T. & Chen, L. Comparison of atmospheric carbon dioxide concentration based on GOSAT and OCO-2 observations. in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 4071\u20134073 https:\/\/doi.org\/10.1109\/IGARSS.2016.7730059 (IEEE, 2016).","DOI":"10.1109\/IGARSS.2016.7730059"},{"key":"4135_CR105","doi-asserted-by":"publisher","first-page":"683","DOI":"10.5194\/amt-9-683-2016","volume":"9","author":"S Kulawik","year":"2016","unstructured":"Kulawik, S. et al. Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON. Atmos Meas Tech 9, 683\u2013709, https:\/\/doi.org\/10.5194\/amt-9-683-2016 (2016).","journal-title":"Atmos Meas Tech"},{"key":"4135_CR106","doi-asserted-by":"publisher","unstructured":"Rodgers, C. D. & Connor, B. J. Intercomparison of remote sounding instruments. Journal of Geophysical Research: Atmospheres 108 https:\/\/doi.org\/10.1029\/2002JD002299 (2003).","DOI":"10.1029\/2002JD002299"},{"key":"4135_CR107","doi-asserted-by":"publisher","unstructured":"(TCCON), T. C. C. O. N. Team 2020 TCCON Data Release (Version GGG2020) [Data set], https:\/\/doi.org\/10.14291\/TCCON.GGG2020 (2022).","DOI":"10.14291\/TCCON.GGG2020"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-04135-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-04135-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-04135-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T20:28:55Z","timestamp":1739910535000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-04135-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,14]]},"references-count":107,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4135"],"URL":"https:\/\/doi.org\/10.1038\/s41597-024-04135-w","relation":{"references":[{"id-type":"doi","id":"10.5281\/zenodo.13895409","asserted-by":"subject"}]},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,14]]},"assertion":[{"value":"9 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"We confirm the absence of any competing interests that could potentially influence or bias the research outcomes, methodology, or presentation. Our work was driven by a commitment to scientific integrity, and we assert that no financial, commercial, personal, or intellectual conflicts exist that could cast doubt on the objectivity of our findings. Our research approach is characterized by transparency and a dedication to advancing knowledge in the field of remote sensing and greenhouse gas monitoring, free from any external influences.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"268"}}