63 citations as recorded by crossref.

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2. Evaluation of the methane full-physics retrieval applied to TROPOMI ocean sun glint measurements A. Lorente et al. https://doi.org/10.5194/amt-15-6585-2022
3. Evaluation of Sentinel-5P TROPOMI Methane Observations at Northern High Latitudes H. Lindqvist et al. https://doi.org/10.3390/rs16162979
4. How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates M. Kiel et al. https://doi.org/10.5194/amt-12-2241-2019
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6. Satellite-Based Assessment of Spatially Heterogeneous XCO2 and Marine pCO2 Trends (2015–2020) S. Zhang et al. https://doi.org/10.3390/rs18040630
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17. Surface reflectance biases in XCH4 retrievals from the 2.3 µm band are enhanced in the presence of aerosols P. Somkuti et al. https://doi.org/10.5194/amt-18-4647-2025
18. Substantial Discrepancies Across Global Satellite XCO2 Products: A Systematic Evaluation J. Yang et al. https://doi.org/10.3390/rs18020371
19. Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements S. Wang et al. https://doi.org/10.3390/rs12142204
20. Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations R. Sussmann & M. Rettinger https://doi.org/10.3390/rs12152387
21. Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions X. He et al. https://doi.org/10.3390/rs17244026
22. Quality controls, bias, and seasonality of CO2 columns in the boreal forest with Orbiting Carbon Observatory-2, Total Carbon Column Observing Network, and EM27/SUN measurements N. Jacobs et al. https://doi.org/10.5194/amt-13-5033-2020
23. Potential improvement of XCO 2 retrieval of the OCO-2 by having aerosol information from the A-train satellites J. Hong et al. https://doi.org/10.1080/15481603.2023.2209968
24. Seasonal and Diurnal Variations in Greenhouse Gas Methane (CH4) in a Rural Area of Rome (Italy) A. Ianniello et al. https://doi.org/10.3390/atmos17020159
25. Spectral sizing of a coarse-spectral-resolution satellite sensor for XCO2 J. Wilzewski et al. https://doi.org/10.5194/amt-13-731-2020
26. Modification of Fraser’s Method for the Atmospheric CO2 Mass Estimation by Using Satellite Data M. Pellegrini et al. https://doi.org/10.3390/atmos13060866
27. A Random Forest-Based CO2 Profile Emulator for Real-Time Prior Profile Generation in TanSat XCO2 Retrieval S. Wu et al. https://doi.org/10.3390/rs17162764
28. Global daily seamless XCO2 Mapping (2016–2020): Spatio-temporal trends and variations during wildfire events J. Li et al. https://doi.org/10.1016/j.jag.2026.105092
29. Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai Y. Wu et al. https://doi.org/10.3390/atmos15030246
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31. Improved estimation of carbon dioxide and methane using machine learning with satellite observations over the Arabian Peninsula M. Alcibahy et al. https://doi.org/10.1038/s41598-024-84593-9
32. XCO2 over East Asia: Assessing consistency, growth, and seasonality from multiple satellites and models Y. Yang et al. https://doi.org/10.1016/j.atmosres.2026.108974
33. First TanSat CO2 retrieval over land and ocean using both nadir and glint spectroscopy X. Hong et al. https://doi.org/10.1016/j.rse.2024.114053
34. Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications M. Reuter et al. https://doi.org/10.5194/amt-13-789-2020
35. The Adaptable 4A Inversion (5AI): description and first XCO2 retrievals from Orbiting Carbon Observatory-2 (OCO-2) observations M. Dogniaux et al. https://doi.org/10.5194/amt-14-4689-2021
36. An intensification of atmospheric CO2 concentrations due to the surface temperature extremes in India S. Gupta et al. https://doi.org/10.1007/s00703-021-00834-w
37. Effect of spectral resolution and signal-to-noise ratios on retrieval of XCO2 and XCH4 using an ultra-hyperspectral sensor R. Chipade et al. https://doi.org/10.1016/j.jqsrt.2026.110019
38. Solar-induced chlorophyll fluorescence from the Geostationary Carbon Cycle Observatory (GeoCarb): An extensive simulation study P. Somkuti et al. https://doi.org/10.1016/j.rse.2021.112565
39. Simultaneous Retrieval of Trace Gases, Aerosols, and Cirrus Using RemoTAP—The Global Orbit Ensemble Study for the CO2M Mission S. Lu et al. https://doi.org/10.3389/frsen.2022.914378
40. Towards routine shipborne measurements of columnar CO2, CH4, CO, and NO2: a case study for tracking regional-scale emission patterns V. Enders et al. https://doi.org/10.5194/amt-19-2633-2026
41. Global Daily Atmospheric Methane Column Concentration Retrieval From TROPOMI Satellite Spectral Measurements by Two-Stage Machine Learning Algorithm Y. Bao et al. https://doi.org/10.1109/JSTARS.2026.3688284
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43. The Significance of Fast Radiative Transfer for Hyperspectral SWIR XCO2 Retrievals P. Somkuti et al. https://doi.org/10.3390/atmos11111219
44. Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements Z. Zeng et al. https://doi.org/10.1016/j.rse.2019.111494
45. 大气甲烷卫星传感器和遥感算法研究综述 何. He Zhuo et al. https://doi.org/10.3788/AOS230429
46. XCO2 observations using satellite measurements with moderate spectral resolution: investigation using GOSAT and OCO-2 measurements L. Wu et al. https://doi.org/10.5194/amt-13-713-2020
47. 基于国产碳卫星的在轨光谱性能评估及大气二氧化碳的高精度反演（特邀） 洪. Hong Xinhua et al. https://doi.org/10.3788/AOS241153
48. XCO2 estimates from the OCO-2 measurements using a neural network approach L. David et al. https://doi.org/10.5194/amt-14-117-2021
49. Seamless mapping of long-term (2010–2020) 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 Y. Wang et al. https://doi.org/10.5194/essd-15-3597-2023
50. An Efficient Algorithm for Retrieving CO2 in the Atmosphere From Hyperspectral Measurements of Satellites: Application of NLS-4DVar Data Assimilation Method Z. Jin et al. https://doi.org/10.3389/feart.2021.688542
51. Estimation and analysis of CO2 column concentrations (XCO2) in the Yangtze River Delta of China based on multi-source data and machine learning C. Chen et al. https://doi.org/10.1016/j.apr.2025.102528
52. Spatial distribution of XCO2 using OCO-2 data in growing seasons Z. Siabi et al. https://doi.org/10.1016/j.jenvman.2019.05.049
53. Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements A. Lorente et al. https://doi.org/10.5194/amt-14-665-2021
54. Spectral Calibration of the Spectrometer on Board the Colombian FACSAT-2 Satellite Mission L. Cárdenas Espinosa et al. https://doi.org/10.3390/rs16193674
55. Anthropogenic CO2 monitoring satellite mission: the need for multi-angle polarimetric observations S. Rusli et al. https://doi.org/10.5194/amt-14-1167-2021
56. Long-term column-averaged greenhouse gas observations using a COCCON spectrometer at the high-surface-albedo site in Gobabeb, Namibia M. Frey et al. https://doi.org/10.5194/amt-14-5887-2021
57. WRF-Chem simulations of CO2 over Belgium and surrounding countries assessed by ground-based measurements J. Wang et al. https://doi.org/10.5194/acp-26-3541-2026
58. Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity L. Zhao et al. https://doi.org/10.3390/rs14051106
59. The Space Carbon Observatory (SCARBO) concept: assessment of XCO2 and XCH4 retrieval performance M. Dogniaux et al. https://doi.org/10.5194/amt-15-4835-2022
60. A Concept of 2U Spaceborne Multichannel Heterodyne Spectroradiometer for Greenhouse Gases Remote Sensing S. Zenevich et al. https://doi.org/10.3390/rs13122235
61. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm C. O'Dell et al. https://doi.org/10.5194/amt-11-6539-2018
62. Toward High Precision XCO2Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements D. Yang et al. https://doi.org/10.1029/2020JD032794
63. Non-Lambertian snow surface reflection models for simulated top-of-the-atmosphere radiances in the NIR and SWIR wavelengths A. Mikkonen et al. https://doi.org/10.1016/j.jqsrt.2023.108892