262 citations as recorded by crossref.

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9. Improved Methods for Retrieval of Chlorophyll Fluorescence from Satellite Observation in the Far-Red Band Using Singular Value Decomposition Algorithm K. Zhu et al. https://doi.org/10.3390/rs16183441
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18. A cloud detection scheme for the Chinese Carbon Dioxide Observation Satellite (TANSAT) X. Wang et al. https://doi.org/10.1007/s00376-016-6033-y
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21. Monitoring greenhouse gases (GHGs) in China: status and perspective Y. Sun et al. https://doi.org/10.5194/amt-15-4819-2022
22. Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries Y. Hwang et al. https://doi.org/10.3390/su13126700
23. An 11-year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm T. Taylor et al. https://doi.org/10.5194/essd-14-325-2022
24. Intercomparison of Carbon Dioxide Products Retrieved from GOSAT Short-Wavelength Infrared Spectra for Three Years (2010–2012) A. Deng et al. https://doi.org/10.3390/atmos7090109
25. Regional CO2 flux estimates for 2009–2010 based on GOSAT and ground-based CO2 observations S. Maksyutov et al. https://doi.org/10.5194/acp-13-9351-2013
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31. Evaluation of earth system model and atmospheric inversion using total column CO2 observations from GOSAT and OCO-2 P. Patra et al. https://doi.org/10.1186/s40645-021-00420-z
32. Spatio-temporal dynamics of human-induced carbon emissions in Southeast Asia (1992–2022) based on nighttime light C. Huang et al. https://doi.org/10.1016/j.eehl.2025.100150
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34. What controls the seasonal cycle of columnar methane observed by GOSAT over different regions in India? N. Chandra et al. https://doi.org/10.5194/acp-17-12633-2017
35. New approach to evaluate satellite-derived XCO2 over oceans by integrating ship and aircraft observations A. Müller et al. https://doi.org/10.5194/acp-21-8255-2021
36. Retrieval and Validation of XCO2 from TanSat Target Mode Observations in Beijing Z. Bao et al. https://doi.org/10.3390/rs12183063
37. Assessment of the Influence of Instrument Parameters on the Detection Accuracy of Greenhouse-Gases Absorption Spectrometer-2 (GAS-2) S. Li et al. https://doi.org/10.3390/atmos14091418
38. China’s sizeable and uncertain carbon sink: a perspective from GOSAT L. Zhang et al. https://doi.org/10.1007/s11434-014-0260-3
39. First Concurrent Observations of NO2 and CO2 From Power Plant Plumes by Airborne Remote Sensing T. Fujinawa et al. https://doi.org/10.1029/2021GL092685
40. Research Progress of Atmospheric CO2Monitoring by Satellite Remote Sensing R. Gao https://doi.org/10.1088/1742-6596/2386/1/012028
41. Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. https://doi.org/10.5194/bg-14-3401-2017
42. On the consistency of methane retrievals using the Total Carbon Column Observing Network (TCCON) and multiple spectroscopic databases E. Malina et al. https://doi.org/10.5194/amt-15-2377-2022
43. The Effect of GOSAT Observations on Estimates of Net CO₂ Flux in Semi-Arid Regions of the Southern Hemisphere M. Kondo et al. https://doi.org/10.2151/sola.2016-037
44. 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
45. Observation of greenhouse gas vertical profiles in the boundary layer of the Mount Qomolangma region using a multirotor UAV Y. Zhou et al. https://doi.org/10.5194/amt-18-1609-2025
46. Ground-Based Remote Sensing of Total Columnar CO2, CH4, and CO Using EM27/SUN FTIR Spectrometer at a Suburban Location (Shadnagar) in India and Validation of Sentinel-5P/TROPOMI V. Sagar et al. https://doi.org/10.1109/LGRS.2022.3171216
47. Validation of XCH4 derived from SWIR spectra of GOSAT TANSO-FTS with aircraft measurement data M. Inoue et al. https://doi.org/10.5194/amt-7-2987-2014
48. Characteristics of interannual variability in space-based XCO2 global observations Y. Guan et al. https://doi.org/10.5194/acp-23-5355-2023
49. Impact of ENSO on variability of global CO2 concentration retrievals from GOSAT and AIRS N. Ying & Q. Ye https://doi.org/10.1088/1755-1315/237/2/022009
50. Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system P. Ciais et al. https://doi.org/10.5194/bg-11-3547-2014
51. An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements S. Houweling et al. https://doi.org/10.1002/2014JD022962
52. A development of cloud top height retrieval using thermal infrared spectra observed with GOSAT and comparison with CALIPSO data Y. Someya et al. https://doi.org/10.5194/amt-9-1981-2016
53. CH4 retrieval from hyperspectral satellite measurements in short-wave infrared: sensitivity study and preliminary test with GOSAT data J. Deng et al. https://doi.org/10.1007/s11434-014-0245-2
54. Greenhouse gases Observing SATellite 2 (GOSAT-2): mission overview R. Imasu et al. https://doi.org/10.1186/s40645-023-00562-2
55. Spatial and temporal variations of land surface temperature and its coupling with atmospheric CO2 concentrations in China from 2009 to 2022 H. Song et al. https://doi.org/10.1007/s12145-025-01970-2
56. Multispectral information from TANSO-FTS instrument – Part 1: Application to greenhouse gases (CO2 and CH4) in clear sky conditions H. Herbin et al. https://doi.org/10.5194/amt-6-3301-2013
57. Retrieved XCO2 Accuracy Improvement by Reducing Aerosol-Induced Bias for China’s Future High-Precision Greenhouse Gases Monitoring Satellite Mission J. Ke et al. https://doi.org/10.3390/atmos13091384
58. Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method H. Watanabe et al. https://doi.org/10.1080/01431161.2015.1011792
59. Global Evaluation and Intercomparison of XCO2 Retrievals from GOSAT, OCO-2, and TANSAT with TCCON J. Fang et al. https://doi.org/10.3390/rs15205073
60. 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
61. Study of mid-latitude retrieval XCO2 greenhouse gas: Validation of satellite-based shortwave infrared spectroscopy with ground-based TCCON observations S. Karbasi et al. https://doi.org/10.1016/j.scitotenv.2022.155513
62. Identification of Bias in Satellite Measurements Using its Geospatial Properties J. Tadic & S. Biraud https://doi.org/10.1109/LGRS.2020.3015174
63. Comparison of model-derived carbon dioxide datasets with the Orbiting Carbon Observatory 3 (OCO-3) observations F. Mustafa & M. Xu https://doi.org/10.1016/j.atmosres.2025.108057
64. High-resolution GEOS-Chem model for Indian monsoon region: Seasonal cycle and budget of tropospheric CO2 S. Allahudheen et al. https://doi.org/10.1016/j.atmosenv.2023.119913
65. Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method Z. He et al. https://doi.org/10.3390/rs12030576
66. A differential optical absorption spectroscopy method for $${X_{CO_2}}$$ retrieval from ground-based Fourier transform spectrometers measurements of the direct solar beam Y. Huo et al. https://doi.org/10.1007/s00376-015-4213-9
67. Improvement of satellite-based estimation of gross primary production through optimization of meteorological parameters and high resolution land cover information at regional scale over East Asia H. Park et al. https://doi.org/10.1016/j.agrformet.2019.02.040
68. The global methane budget 2000–2012 M. Saunois et al. https://doi.org/10.5194/essd-8-697-2016
69. Multi-observational estimation of regional and sectoral emission contributions to the persistent high growth rate of atmospheric CH4 for 2020–2022 Y. Niwa et al. https://doi.org/10.5194/acp-25-6757-2025
70. A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO2 inversion Y. Niwa et al. https://doi.org/10.5194/gmd-10-2201-2017
71. Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories F. Wang et al. https://doi.org/10.3390/rs11212489
72. Observations of atmospheric CO2 and CO based on in-situ and ground-based remote sensing measurements at Hefei site, China C. Shan et al. https://doi.org/10.1016/j.scitotenv.2022.158188
73. Comparison of GOSAT SWIR and Aircraft Measurements of XCH₄ over West Siberia A. Ono et al. https://doi.org/10.2151/sola.2015-036
74. A comparison of satellite observations with the XCO 2 surface obtained by fusing TCCON measurements and GEOS-Chem model outputs L. Zhang et al. https://doi.org/10.1016/j.scitotenv.2017.06.018
75. Regional CO emission estimated from ground-based remote sensing at Hefei site, China C. Shan et al. https://doi.org/10.1016/j.atmosres.2019.02.005
76. Using portable low-resolution spectrometers to evaluate Total Carbon Column Observing Network (TCCON) biases in North America N. Mostafavi Pak et al. https://doi.org/10.5194/amt-16-1239-2023
77. Retrieval of greenhouse gases from GOSAT and GOSAT-2 using the FOCAL algorithm S. Noël et al. https://doi.org/10.5194/amt-15-3401-2022
78. A physics-based AI algorithm integrating radiative transfer and machine learning for joint retrieval of aerosol layer height and optical thickness from hyperspectral satellite observations over the ocean R. Yao et al. https://doi.org/10.1016/j.rse.2026.115379
79. A simple and quick sensitivity analysis method for methane isotopologues detection with GOSAT-TANSO-FTS E. Malina et al. https://doi.org/10.14324/111.444/ucloe.000013
80. The Importance of Scale in the Definition of Uncertainties: How do we Best Communicate this to Data Users? C. Bulgin et al. https://doi.org/10.1007/s10712-025-09908-5
81. The Global Methane Budget 2000–2017 M. Saunois et al. https://doi.org/10.5194/essd-12-1561-2020
82. 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
83. Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. https://doi.org/10.1088/1748-9326/abd352
84. Ability of the 4-D-Var analysis of the GOSAT BESD XCO2 retrievals to characterize atmospheric CO2 at large and synoptic scales S. Massart et al. https://doi.org/10.5194/acp-16-1653-2016
85. Quantitative analysis of spatiotemporal coverage and uncertainty decomposition in OCO-2/3 XCO2 across China L. Zhang et al. https://doi.org/10.1016/j.atmosenv.2024.120636
86. A new algorithm to generate a priori trace gas profiles for the GGG2020 retrieval algorithm J. Laughner et al. https://doi.org/10.5194/amt-16-1121-2023
87. Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution N. Bie et al. https://doi.org/10.5194/amt-11-1251-2018
88. Fourier transform spectrometer measurements of column CO2 at Sodankylä, Finland R. Kivi & P. Heikkinen https://doi.org/10.5194/gi-5-271-2016
89. Daytime diurnal representation of global OCO-2 and GOSAT XCO2 products M. Zhang et al. https://doi.org/10.1016/j.atmosenv.2026.122103
90. Study on the Ground-Based FTS Measurements at Beijing, China and the Colocation Sensitivity of Satellite Data S. Yang et al. https://doi.org/10.3390/atmos12121586
91. Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization M. Buchwitz et al. https://doi.org/10.5194/amt-6-3477-2013
92. Retrieval of Global Carbon Dioxide From TanSat Satellite and Comprehensive Validation With TCCON Measurements and Satellite Observations X. Hong et al. https://doi.org/10.1109/TGRS.2021.3066623
93. Comparisons of CH4 ground-based FTIR measurements near Saint Petersburg with GOSAT observations N. Gavrilov et al. https://doi.org/10.5194/amt-7-1003-2014
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100. Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
101. Satellite-derived methane hotspot emission estimates using a fast data-driven method M. Buchwitz et al. https://doi.org/10.5194/acp-17-5751-2017
102. Monitoring Greenhouse Gases from Space H. Boesch et al. https://doi.org/10.3390/rs13142700
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105. Global height-resolved methane retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp R. Siddans et al. https://doi.org/10.5194/amt-10-4135-2017
106. Variation of XCO2 anomaly patterns in the Middle East from OCO-2 satellite data F. Golkar & S. Mousavi https://doi.org/10.1080/17538947.2022.2096936
107. Atmospheric Greenhouse Gas Distributions: Satellite-Based Measurements A. Uspensky https://doi.org/10.1134/S0001433823140141
108. Evaluating the performance of carbon dioxide and methane observations from carbon-monitoring satellite products over China X. Hong et al. https://doi.org/10.1016/j.scitotenv.2024.176896
109. Algorithm update of the GOSAT/TANSO-FTS thermal infrared CO2 product (version 1) and validation of the UTLS CO2 data using CONTRAIL measurements N. Saitoh et al. https://doi.org/10.5194/amt-9-2119-2016
110. Enhanced Methane Emissions during Amazonian Drought by Biomass Burning M. Saito et al. https://doi.org/10.1371/journal.pone.0166039
111. Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations R. Janardanan et al. https://doi.org/10.1088/1748-9326/ad2436
112. A high-precision retrieval method for methane vertical profiles based on dual-band spectral data from the GOSAT satellite L. Li et al. https://doi.org/10.1016/j.atmosenv.2023.120183
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114. Satellite-based inversion of global methane fluxes: capabilities and implications of GOSAT-2 measurements M. Saito et al. https://doi.org/10.5194/essd-18-6565-2026
115. Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0 A. Tsuruta et al. https://doi.org/10.5194/gmd-10-1261-2017
116. Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set M. Buchwitz et al. https://doi.org/10.1016/j.rse.2016.12.027
117. Detection of fossil-fuel CO2 plummet in China due to COVID-19 by observation at Hateruma Y. Tohjima et al. https://doi.org/10.1038/s41598-020-75763-6
118. Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements F. Xie et al. https://doi.org/10.5194/amt-17-3949-2024
119. Occurrence and Discrepancy of Surface and Column Mole Fractions of CO2 and CH4 at a Desert Site in Dunhuang, Western China C. Wei et al. https://doi.org/10.3390/atmos13040571
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121. Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data M. Inoue et al. https://doi.org/10.5194/amt-9-3491-2016
122. Spatiotemporal Investigation of Near-Surface CO2 and Its Affecting Factors Over Asia F. Mustafa et al. https://doi.org/10.1109/TGRS.2022.3178125
123. Validation of GOSAT SWIR XCO₂ and XCH₄ Retrieved by PPDF-S Method and Comparison with Full Physics Method C. Iwasaki et al. https://doi.org/10.2151/sola.2017-031
124. Spatial distributions of XCO2 seasonal cycle amplitude and phase over northern high-latitude regions N. Jacobs et al. https://doi.org/10.5194/acp-21-16661-2021
125. Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals using TCCON data from near-ocean sites M. Zhou et al. https://doi.org/10.5194/amt-9-1415-2016
126. Ship- and aircraft-based XCH4 over oceans as a new tool for satellite validation A. Müller et al. https://doi.org/10.5194/amt-17-1297-2024
127. Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia M. Khaliq et al. https://doi.org/10.1016/j.scitotenv.2024.171311
128. Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling R. Janardanan et al. https://doi.org/10.3390/rs9090941
129. Investigating the performance of a greenhouse gas observatory in Hefei, China W. Wang et al. https://doi.org/10.5194/amt-10-2627-2017
130. A Theoretical Analysis for Improving Aerosol-Induced CO2 Retrieval Uncertainties Over Land Based on TanSat Nadir Observations Under Clear Sky Conditions X. Chen et al. https://doi.org/10.3390/rs11091061
131. XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm S. Noël et al. https://doi.org/10.5194/amt-14-3837-2021
132. Benefits of satellite XCO2 and newly proposed atmospheric CO2 observation network over India in constraining regional CO2 fluxes S. Halder et al. https://doi.org/10.1016/j.scitotenv.2021.151508
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135. First Retrieval of Formic Acid from GOSAT-2 Thermal–Infrared Observations over Land F. Xie et al. https://doi.org/10.3390/rs18162750
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137. Estimation of CH4emission based on an advanced 4D-LETKF assimilation system J. Bisht et al. https://doi.org/10.5194/gmd-16-1823-2023
138. A high-accuracy method for simulating the XCO2 global distribution using GOSAT retrieval data M. Zhao et al. https://doi.org/10.1007/s11430-016-0069-7
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151. Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011) R. Miao et al. https://doi.org/10.3390/rs5073431
152. Using ocean-glint scattered sunlight as a diagnostic tool for satellite remote sensing of greenhouse gases A. Butz et al. https://doi.org/10.5194/amt-6-2509-2013
153. Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. https://doi.org/10.1038/s41598-022-19462-4
154. A Review of City-Scale Methane Flux Inversion Based on Top-Down Methods X. Li et al. https://doi.org/10.3390/rs17183152
155. Satellite and ground-based measurements of XCO2 in a remote semiarid region of Australia V. Velazco et al. https://doi.org/10.5194/essd-11-935-2019
156. A long-term global XCO2 dataset: Ensemble of satellite products C. Jin et al. https://doi.org/10.1016/j.atmosres.2022.106385
157. Space- and ground-based CO2 measurements: A review T. Yue et al. https://doi.org/10.1007/s11430-015-0239-7
158. A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup M. Reuter et al. https://doi.org/10.3390/rs9111159
159. Observations of XCO2 and XCH4 with ground-based high-resolution FTS at Saga, Japan, and comparisons with GOSAT products H. Ohyama et al. https://doi.org/10.5194/amt-8-5263-2015
160. Improving Forest Carbon Sink Accounting Using Integrated Satellite-Ground Observations, Machine Learning, and Ecological Process Modeling X. Hong et al. https://doi.org/10.1021/acs.est.6c03931
161. CO2 concentration and its spatiotemporal variation in the troposphere using multi-sensor satellite data, carbon tracker, and aircraft observations S. Lee et al. https://doi.org/10.1080/15481603.2017.1317120
162. Atmospheric CO2 retrieval from satellite spectral measurements by a two-step machine learning approach Z. Zhao et al. https://doi.org/10.1016/j.jqsrt.2021.108006
163. EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations А. Bril et al. https://doi.org/10.1016/j.jqsrt.2016.12.005
164. Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
165. Development of satellite-based surface methane flux model for major agro-ecosystems using energy balance diagnostics S. Thakur et al. https://doi.org/10.1007/s10333-020-00808-5
166. First Global Carbon Dioxide Maps Produced from TanSat Measurements D. Yang et al. https://doi.org/10.1007/s00376-018-7312-6
167. Quantification and evaluation of atmospheric emissions from crop residue burning constrained by satellite observations in China during 2016–2020 X. Hong et al. https://doi.org/10.1016/j.scitotenv.2022.161237
168. Using Multisource Data and Time Series Features to Construct a Global Terrestrial CO₂ Coverage by Deep Learning W. Tian et al. https://doi.org/10.1109/TGRS.2024.3462589
169. Vast ecosystem disturbance in a warming climate may jeopardize our climate goal of reducing CO2: a case study for megafires in the Australian ‘black summer’ X. Hong et al. https://doi.org/10.1016/j.scitotenv.2023.161387
170. Monitoring carbon dioxide from space: Retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China D. Yang et al. https://doi.org/10.1007/s00376-017-6221-4
171. 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
172. Retrieval of vertical profiles and tropospheric CO2 columns based on high-resolution FTIR over Hefei, China C. Shan et al. https://doi.org/10.1364/OE.411383
173. A Review of Anthropogenic Ground-Level Carbon Emissions Based on Satellite Data K. Hu et al. https://doi.org/10.1109/JSTARS.2024.3355549
174. Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO2 T. Thum et al. https://doi.org/10.5194/bg-17-5721-2020
175. Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model J. Chen et al. https://doi.org/10.1016/j.isprsjprs.2024.01.009
176. Validation of XCO2 and XCH4 retrieved from a portable Fourier transform spectrometer with those from in situ profiles from aircraft-borne instruments H. Ohyama et al. https://doi.org/10.5194/amt-13-5149-2020
177. 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
178. Two-Year Comparison of Airborne Measurements of CO2 and CH4 With GOSAT at Railroad Valley, Nevada T. Tanaka et al. https://doi.org/10.1109/TGRS.2016.2539973
179. Validation of XCO2 derived from SWIR spectra of GOSAT TANSO-FTS with aircraft measurement data M. Inoue et al. https://doi.org/10.5194/acp-13-9771-2013
180. Development of the empirical orthogonal functions-based algorithm for the retrievals of atmospheric CO2 total column amount from space-borne observations of reflected sunlight A. Bril & S. Maksyutov https://doi.org/10.1117/1.JRS.12.046012
181. 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
182. A Remote Sensing Technique for CO2 Column Density G. Hai et al. https://doi.org/10.1109/TGRS.2025.3579265
183. Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003–2016 M. Buchwitz et al. https://doi.org/10.5194/acp-18-17355-2018
184. XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker A. Liang et al. https://doi.org/10.3390/atmos14091335
185. Bias assessment of lower and middle tropospheric CO2 concentrations of GOSAT/TANSO-FTS TIR version 1 product N. Saitoh et al. https://doi.org/10.5194/amt-10-3877-2017
186. Seasonal variability of tropospheric CO2 over India based on model simulation, satellite retrieval and in-situ observation M. Krishnapriya et al. https://doi.org/10.1007/s12040-020-01478-x
187. The Atmospheric Imaging Mission for Northern Regions: AIM-North R. Nassar et al. https://doi.org/10.1080/07038992.2019.1643707
188. Recent methane surges reveal heightened emissions from tropical inundated areas X. Lin et al. https://doi.org/10.1038/s41467-024-55266-y
189. CO2 retrieval model and analysis in short-wave infrared spectrum Y. Li et al. https://doi.org/10.1016/j.ijleo.2016.01.144
190. Inter-annual variability of summertime CO 2 exchange in Northern Eurasia inferred from GOSAT XCO 2 M. Ishizawa et al. https://doi.org/10.1088/1748-9326/11/10/105001
191. Emissions of methane from coal fields, thermal power plants, and wetlands and their implications for atmospheric methane across the south Asian region M. Dangeti et al. https://doi.org/10.5194/acp-24-12843-2024
192. MCF-XCO2: A cross-mission consistency and fusion framework for integrating multi-satellite XCO2 observations Y. Yu et al. https://doi.org/10.1016/j.atmosres.2026.108747
193. Aerosol Property Retrieval Algorithm over Northeast Asia from TANSO-CAI Measurements Onboard GOSAT S. Lee et al. https://doi.org/10.3390/rs9070687
194. 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
195. High-Resolution Geos-Chem Model for Indian Monsoon Region: Seasonal Cycle and Budget of Tropospheric Co2 S. Allahudheen et al. https://doi.org/10.2139/ssrn.4161842
196. Study of the footprints of short-term variation in XCO2 observed by TCCON sites using NIES and FLEXPART atmospheric transport models D. Belikov et al. https://doi.org/10.5194/acp-17-143-2017
197. Meteorological Control of Subtropical South American Methane Emissions Estimated from GOSAT Observations H. Takagi et al. https://doi.org/10.2151/sola.2021-037
198. Improved calibration procedures for the EM27/SUN spectrometers of the COllaborative Carbon Column Observing Network (COCCON) C. Alberti et al. https://doi.org/10.5194/amt-15-2433-2022
199. Super-resolution reconstruction of GOSAT CO 2 products using bicubic interpolation R. Xiang et al. https://doi.org/10.1080/10106049.2022.2096699
200. Enhancing scientific transparency in national CO2 emissions reports via satellite-based a posteriori estimates M. Watanabe et al. https://doi.org/10.1038/s41598-023-42664-3
201. Evaluating Radiometric Responsivity Degradations of TANSO-FTS/GOSAT Using Principal Component Analysis Y. Someya & Y. Yoshida https://doi.org/10.1175/JTECH-D-19-0224.1
202. Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature T. von Clarmann et al. https://doi.org/10.5194/amt-13-4393-2020
203. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space A. Kuze et al. https://doi.org/10.5194/amt-9-2445-2016
204. Large XCH4 anomaly in summer 2013 over northeast Asia observed by GOSAT M. Ishizawa et al. https://doi.org/10.5194/acp-16-9149-2016
205. Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI S. Massart et al. https://doi.org/10.5194/acp-14-6139-2014
206. Preface D. Lü & Y. Liu https://doi.org/10.1007/s11434-014-0313-7
207. Independent Bias Correction Method for Satellite Observation Data Introduced to CO₂ Flux Inversion T. Maki et al. https://doi.org/10.2151/sola.2023-021
208. Simulating CO2 profiles using NIES TM and comparison with HIAPER Pole-to-Pole Observations C. Song et al. https://doi.org/10.1007/s11707-022-0997-y
209. Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. https://doi.org/10.1016/j.rse.2022.112966
210. 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
211. GFIT3: a full physics retrieval algorithm for remote sensing of greenhouse gases in the presence of aerosols Z. Zeng et al. https://doi.org/10.5194/amt-14-6483-2021
212. 基于深度学习的稀疏卫星二氧化碳观测空间间隙填充方法 西. Xijiade Aihemaiti & 任. Ren Tao https://doi.org/10.3788/AOS251851
213. Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals E. Kivimäki et al. https://doi.org/10.3390/rs11070882
214. Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing Y. Jung et al. https://doi.org/10.3390/rs8040322
215. Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations R. Janardanan et al. https://doi.org/10.3390/rs12030375
216. Use of Differential Optical Spectroscopy for Satellite Diagnostics of Methane Content in a Scattering Atmosphere A. Bril et al. https://doi.org/10.1007/s10812-023-01635-2
217. Analysis of the Current Situation of CO2 Satellite Observation Y. Li et al. https://doi.org/10.3390/rs17213635
218. Detection of Methane Emission from a Local Source Using GOSAT Target Observations A. Kuze et al. https://doi.org/10.3390/rs12020267
219. Geostatistical Analysis of CH4 Columns over Monsoon Asia Using Five Years of GOSAT Observations M. Liu et al. https://doi.org/10.3390/rs8050361
220. The greenhouse gas observation mission with Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW): objectives, conceptual framework and scientific contributions H. Tanimoto et al. https://doi.org/10.1186/s40645-025-00684-9
221. Dynamic estimation of a priori CO2 vertical profiles from satellite SWIR spectra for improved XCO2 retrieval S. Xu et al. https://doi.org/10.1016/j.atmosenv.2026.122177
222. Comparing GOSAT observations of localized CO2 enhancements by large emitters with inventory‐based estimates R. Janardanan et al. https://doi.org/10.1002/2016GL067843
223. Spatio-temporal variations of atmospheric methane and its response to climate on the Tibetan Plateau from 2010 to 2022 Y. Wei et al. https://doi.org/10.1016/j.atmosenv.2023.120088
224. Fast CO2 Retrieval Using a Semi-Physical Statistical Model for the High-Resolution Spectrometer on the Fengyun-3D Satellite Y. Bi et al. https://doi.org/10.1007/s13351-022-1149-8
225. TanSat Mission Achievements: from Scientific Driving to Preliminary Observations Y. LIU et al. https://doi.org/10.11728/cjss2018.05.627
226. Satellite-inferred European carbon sink larger than expected M. Reuter et al. https://doi.org/10.5194/acp-14-13739-2014
227. Optimization of the Photon Path Length Probability Density Function-Simultaneous (PPDF-S) Method and Evaluation of CO2 Retrieval Performance Under Dense Aerosol Conditions C. Iwasaki et al. https://doi.org/10.3390/s19051262
228. Interannual Variability of Atmospheric CH4 and Its Driver Over South Korea Captured by Integrated Data in 2019 S. Kenea et al. https://doi.org/10.3390/rs13122266
229. Potential improvements in global carbon flux estimates from a network of laser heterodyne radiometer measurements of column carbon dioxide P. Palmer et al. https://doi.org/10.5194/amt-12-2579-2019
230. Quality Evaluation of the Column-Averaged Dry Air Mole Fractions of Carbon Dioxide and Methane Observed by GOSAT and GOSAT-2 Y. Yoshida et al. https://doi.org/10.2151/sola.2023-023
231. 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
232. A comprehensive evaluation of the spatiotemporal variation of CO2 and its driving forces over China X. Jin et al. https://doi.org/10.3389/fenvs.2023.1129639
233. Column-averaged CO2 concentrations in the subarctic from GOSAT retrievals and NIES transport model simulations D. Belikov et al. https://doi.org/10.1016/j.polar.2014.02.002
234. On the zero-level offset in the GOSAT TANSO-FTS O2 A band and the quality of solar-induced chlorophyll fluorescence (SIF): comparison of SIF between GOSAT and OCO-2 H. Oshio et al. https://doi.org/10.5194/amt-12-6721-2019
235. Preliminary Assessment of Methane Concentration Variation Observed by GOSAT in China X. Qin et al. https://doi.org/10.1155/2015/125059
236. The TanSat mission: preliminary global observations Y. Liu et al. https://doi.org/10.1016/j.scib.2018.08.004
237. Conversion of surface CH4 concentrations from GOSAT satellite observations using XGBoost algorithm Y. Wan et al. https://doi.org/10.1016/j.atmosenv.2023.119694
238. A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2 M. Reuter et al. https://doi.org/10.3390/rs9111102
239. Impact of equatorial and continental airflow on primary greenhouse gases in the northern South China Sea C. Ou-Yang et al. https://doi.org/10.1088/1748-9326/10/6/065005
240. Analysis of Long-Range Transport of Carbon Dioxide and Its High Concentration Events over East Asian Region Using GOSAT Data and GEOS-Chem Modeling S. Kim et al. https://doi.org/10.1155/2015/680264
241. Validation and analysis of MOPITT CO observations of the Amazon Basin M. Deeter et al. https://doi.org/10.5194/amt-9-3999-2016
242. GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al. https://doi.org/10.5194/gmd-19-1683-2026
243. Analysis of spatiotemporal patterns of atmospheric CO2 concentration in the Yellow River Basin over the past decade based on time-series remote sensing data Y. Lv et al. https://doi.org/10.1007/s11356-023-30553-1
244. Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison H. Ohyama et al. https://doi.org/10.3390/rs9010064
245. Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network E. Dupuy et al. https://doi.org/10.3390/rs8050414
246. Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm Y. Someya et al. https://doi.org/10.5194/amt-16-1477-2023
247. Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan Y. Tohjima et al. https://doi.org/10.1186/s40645-023-00542-6
248. GOSAT Mapping of Global Greenhouse Gas in 2020 and 2021 L. Zhang et al. https://doi.org/10.3390/atmos13111814
249. Large Increase in Atmospheric Methane over West Siberian Wetlands during Summer Detected from Space Y. Someya et al. https://doi.org/10.2151/sola.2020-027
250. Using a portable FTIR spectrometer to evaluate the consistency of Total Carbon Column Observing Network (TCCON) measurements on a global scale: the Collaborative Carbon Column Observing Network (COCCON) travel standard B. Herkommer et al. https://doi.org/10.5194/amt-17-3467-2024
251. Retrieval of solar-induced chlorophyll fluorescence (SIF) from satellite measurements: comparison of SIF between TanSat and OCO-2 L. Yao et al. https://doi.org/10.5194/amt-15-2125-2022
252. Characterisation of methane variability and trends from near-infrared solar spectra over Hefei, China Y. Tian et al. https://doi.org/10.1016/j.atmosenv.2017.11.001
253. Global 20-year XCO2 mapping through synergy of multi-satellite observations H. Yang et al. https://doi.org/10.1016/j.gsf.2026.102333
254. Information content analysis: the potential for methane isotopologue retrieval from GOSAT-2 E. Malina et al. https://doi.org/10.5194/amt-11-1159-2018
255. Emissions from the Oil and Gas Sectors, Coal Mining and Ruminant Farming Drive Methane Growth over the Past Three Decades N. CHANDRA et al. https://doi.org/10.2151/jmsj.2021-015
256. XCO2 retrieval algorithm in short-wave infrared spectrum and validation with TCCON data Y. Li et al. https://doi.org/10.1016/j.ijleo.2016.04.074
257. Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions X. He et al. https://doi.org/10.3390/rs17244026
258. Does GOSAT capture the true seasonal cycle of carbon dioxide? H. Lindqvist et al. https://doi.org/10.5194/acp-15-13023-2015
259. Comparison of XCH4 Derived from g-b FTS and GOSAT and Evaluation Using Aircraft In-Situ Observations over TCCON Site S. Kenea et al. https://doi.org/10.1007/s13143-019-00105-0
260. Influence of differences in current GOSATXCO2retrievals on surface flux estimation H. Takagi et al. https://doi.org/10.1002/2013GL059174
261. Design of a high-coverage ground-based CO2 monitoring layout using a novel information theory-based optimization model P. Bakhtiari et al. https://doi.org/10.1007/s10661-021-08933-2
262. 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