272 citations as recorded by crossref.

1. Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example R. Andres et al. 10.5194/acp-16-14979-2016
2. An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO 2 Retrievals D. Roten et al. 10.1029/2020EA001343
3. EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012 G. Janssens-Maenhout et al. 10.5194/essd-11-959-2019
4. The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes A. Eldering et al. 10.1126/science.aam5745
5. The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery B. Byrne et al. 10.1029/2021AV000469
6. A cluster-based method to map urban area from DMSP/OLS nightlights Y. Zhou et al. 10.1016/j.rse.2014.03.004
7. Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO 2 exchanges: GOSAT CO 2 inversion and empirical eddy flux upscaling M. Kondo et al. 10.1002/2014JG002866
8. A synthesis of carbon dioxide emissions from fossil-fuel combustion R. Andres et al. 10.5194/bg-9-1845-2012
9. Simulation of contribution of continental anthropogenic sources to variations in the CO2 concentration during winter period on Hateruma Island A. Ganshin et al. 10.1134/S1024856013010089
10. The Vulcan Version 3.0 High‐Resolution Fossil Fuel CO 2 Emissions for the United States K. Gurney et al. 10.1029/2020JD032974
11. Imputing missing data in non-renewable empower time series from night-time lights observations L. Neri et al. 10.1016/j.ecolind.2017.08.040
12. A Comparative Analysis of Anthropogenic CO 2 Emissions at City Level Using OCO‐2 Observations: A Global Perspective P. Fu et al. 10.1029/2019EF001282
13. Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data Y. Sun et al. 10.3390/rs12182916
14. Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia Y. Fu et al. 10.1016/j.accre.2020.03.001
15. The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data A. Eldering et al. 10.5194/amt-12-2341-2019
16. A Thermodynamic Geography: Night-Time Satellite Imagery as a Proxy Measure of Emergy L. Coscieme et al. 10.1007/s13280-013-0468-5
17. Study of the footprints of short-term variation in XCO<sub>2</sub> observed by TCCON sites using NIES and FLEXPART atmospheric transport models D. Belikov et al. 10.5194/acp-17-143-2017
18. Anthropogenic Heat Release: Estimation of Global Distribution and Possible Climate Effect B. CHEN et al. 10.2151/jmsj.2014-A10
19. Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform D. Mallia et al. 10.1021/acs.est.0c04388
20. A simple method based on the thermal anomaly index to detect industrial heat sources H. Xia et al. 10.1016/j.jag.2018.08.003
21. Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2 F. Chevallier et al. 10.1002/2013GL058772
22. Far-field biogenic and anthropogenic emissions as a dominant source of variability in local urban carbon budgets: A global high-resolution model study with implications for satellite remote sensing A. Schuh et al. 10.1016/j.rse.2021.112473
23. North American CO<sub>2</sub> exchange: inter-comparison of modeled estimates with results from a fine-scale atmospheric inversion S. Gourdji et al. 10.5194/bg-9-457-2012
24. Brightness of Nighttime Lights as a Proxy for Freight Traffic: A Case Study of China J. Tian et al. 10.1109/JSTARS.2013.2258892
25. Optimization of a prognostic biosphere model for terrestrial biomass and atmospheric CO<sub>2</sub> variability M. Saito et al. 10.5194/gmd-7-1829-2014
26. Effects of urban forms on CO2 emissions in China from a multi-perspective analysis K. Shi et al. 10.1016/j.jenvman.2020.110300
27. A global carbon assimilation system using a modified ensemble Kalman filter S. Zhang et al. 10.5194/gmd-8-805-2015
28. High-resolution spatial distribution of greenhouse gas emissions in the residential sector O. Danylo et al. 10.1007/s11027-019-9846-z
29. Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China Z. He et al. 10.3390/rs12040718
30. Near-real-time global gridded daily CO2 emissions X. Dou et al. 10.1016/j.xinn.2021.100182
31. Recent research quantifying anthropogenic CO2emissions at the street scale within the urban domain K. Gurney 10.1080/17583004.2014.986849
32. Spatial distributions of <i>X</i><sub>CO<sub>2</sub></sub> seasonal cycle amplitude and phase over northern high-latitude regions N. Jacobs et al. 10.5194/acp-21-16661-2021
33. A comparison of estimates of global carbon dioxide emissions from fossil carbon sources R. Andrew 10.5194/essd-12-1437-2020
34. Quantification of Fossil Fuel CO2 Emissions on the Building/Street Scale for a Large U.S. City K. Gurney et al. 10.1021/es3011282
35. VIIRS night-time lights C. Elvidge et al. 10.1080/01431161.2017.1342050
36. Direct space‐based observations of anthropogenic CO 2 emission areas from OCO‐2 J. Hakkarainen et al. 10.1002/2016GL070885
37. A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network T. Shirai et al. 10.1080/16000889.2017.1291158
38. A new space-borne perspective of crop productivity variations over the US Corn Belt P. Somkuti et al. 10.1016/j.agrformet.2019.107826
39. Consistent regional fluxes of CH<sub>4</sub> and CO<sub>2</sub> inferred from GOSAT proxy XCH<sub>4</sub> : XCO<sub>2</sub> retrievals, 2010–2014 L. Feng et al. 10.5194/acp-17-4781-2017
40. Short-term reduction of regional enhancement of atmospheric CO2 in China during the first COVID-19 pandemic period S. Sim et al. 10.1088/1748-9326/ac507d
41. Global carbon emission spatial pattern in 2030 under INDCs: using a gridding approach based on population and urbanization L. Tao et al. 10.1108/IJCCSM-04-2021-0038
42. Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. 10.1016/j.rse.2022.112966
43. Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis G. Geng et al. 10.5194/acp-17-4131-2017
44. Removing traffic emissions from CO2 time series measured at a tall tower using mobile measurements and transport modeling A. Schmidt et al. 10.1016/j.atmosenv.2014.08.006
45. Forecasting China’s GDP at the pixel level using nighttime lights time series and population images N. Zhao et al. 10.1080/15481603.2016.1276705
46. A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon F. Santos et al. 10.1371/journal.pone.0226224
47. Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment J. Lian et al. 10.1021/acs.est.1c04973
48. Inverse Modeling of CO₂ Fluxes Using GOSAT Data and Multi-Year Ground-Based Observations T. Saeki et al. 10.2151/sola.2013-011
49. Toward Accurate, Policy-Relevant Fossil Fuel CO2 Emission Landscapes K. Gurney et al. 10.1021/acs.est.0c01175
50. Top‐Down Constraints on Anthropogenic CO 2 Emissions Within an Agricultural‐Urban Landscape C. Hu et al. 10.1029/2017JD027881
51. Towards space based verification of CO<sub>2</sub> emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation V. Velazco et al. 10.5194/amt-4-2809-2011
52. Evaluating national and subnational CO2 mitigation goals in China’s thirteenth five-year plan from satellite observations G. Pan et al. 10.1016/j.envint.2021.106771
53. Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation Y. Fu et al. 10.3390/rs13193880
54. Spatial variation in household consumption-based carbon emission inventories for 1200 Japanese cities K. Kanemoto et al. 10.1088/1748-9326/abc045
55. Diurnal, weekly, seasonal, and spatial variabilities in carbon dioxide flux in different urban landscapes in Sakai, Japan M. Ueyama & T. Ando 10.5194/acp-16-14727-2016
56. How does urbanization affect CO2 emissions of central heating systems in China? An assessment of natural gas transition policy based on nighttime light data W. Zhang et al. 10.1016/j.jclepro.2020.123188
57. Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide S. Anenberg et al. 10.1038/s41598-019-48057-9
58. Optimizing the Spatial Resolution for Urban CO2 Flux Studies Using the Shannon Entropy J. Liang et al. 10.3390/atmos8050090
59. Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing L. Yuan et al. 10.1007/s11869-021-01041-7
60. CO 2 annual and semiannual cycles from multiple satellite retrievals and models X. Jiang et al. 10.1002/2014EA000045
61. Analyzing the impact of three-dimensional building structure on CO2 emissions based on random forest regression J. Lin et al. 10.1016/j.energy.2021.121502
62. Fluxes of Atmospheric Greenhouse‐Gases in Maryland (FLAGG‐MD): Emissions of Carbon Dioxide in the Baltimore, MD‐Washington, D.C. Area D. Ahn et al. 10.1029/2019JD032004
63. Anthropogenic Methane Emission and Its Partitioning for the Yangtze River Delta Region of China C. Hu et al. 10.1029/2018JG004850
64. Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity M. Kiel et al. 10.1016/j.rse.2021.112314
65. Shedding Light on Agricultural Transitions, Dragon Fruit Cultivation, and Electrification in Southern Vietnam Using Mixed Methods L. Krauser et al. 10.1080/24694452.2021.1940825
66. On the Ability of Space-Based Passive and Active Remote Sensing Observations of CO2 to Detect Flux Perturbations to the Carbon Cycle S. Crowell et al. 10.1002/2017JD027836
67. Simultaneous shipborne measurements of CO<sub>2</sub>, CH<sub>4</sub> and CO and their application to improving greenhouse-gas flux estimates in Australia B. Bukosa et al. 10.5194/acp-19-7055-2019
68. Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO 2 measurements from 1999 to 2017 J. Yun et al. 10.1111/gcb.15061
69. Errors and uncertainties in a gridded carbon dioxide emissions inventory T. Oda et al. 10.1007/s11027-019-09877-2
70. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data J. Wang et al. 10.1038/s41586-020-2849-9
71. Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources R. Bun et al. 10.1007/s11027-018-9791-2
72. Investigating sources of variability and error in simulations of carbon dioxide in an urban region C. Martin et al. 10.1016/j.atmosenv.2018.11.013
73. Tweets or nighttime lights: Comparison for preeminence in estimating socioeconomic factors N. Zhao et al. 10.1016/j.isprsjprs.2018.08.018
74. Comparison of Global Downscaled Versus Bottom‐Up Fossil Fuel CO 2 Emissions at the Urban Scale in Four U.S. Urban Areas K. Gurney et al. 10.1029/2018JD028859
75. Improving the temporal and spatial distribution of CO2emissions from global fossil fuel emission data sets R. Nassar et al. 10.1029/2012JD018196
76. Detecting regional patterns of changing CO 2 flux in Alaska N. Parazoo et al. 10.1073/pnas.1601085113
77. CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations W. He et al. 10.5194/gmd-11-3515-2018
78. Geostationary Emission Explorer for Europe (G3E): mission concept and initial performance assessment A. Butz et al. 10.5194/amt-8-4719-2015
79. Can we evaluate a fine-grained emission model using high-resolution atmospheric transport modelling and regional fossil fuel CO2 observations? F. Vogel et al. 10.3402/tellusb.v65i0.18681
80. Variation patterns and driving factors of regional atmospheric CO2 anomalies in China Y. Fu et al. 10.1007/s11356-021-17139-5
81. Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach P. Misra et al. 10.3390/atmos10090517
82. Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective K. Shi et al. 10.1016/j.apenergy.2017.11.042
83. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
84. Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite A. Shekhar et al. 10.1088/1748-9326/ab9cfe
85. Identifying industrial heat sources using time-series of the VIIRS Nightfire product with an object-oriented approach Y. Liu et al. 10.1016/j.rse.2017.10.019
86. GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY O. Danylo et al. 10.23939/istcgcap2015.01.131
87. Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach Y. Liu et al. 10.1016/j.envpol.2017.12.070
88. Estimates of CO2 Anthropogenic Emission from the Megacity St. Petersburg Y. Timofeyev et al. 10.1134/S1028334X20090184
89. Uncertainty in gridded CO 2 emissions estimates S. Hogue et al. 10.1002/2015EF000343
90. Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos A. Gaughan et al. 10.1088/2515-7620/ab3d91
91. Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables Y. Hwang et al. 10.3390/ijerph17165976
92. Inter-annual variability of summertime CO 2 exchange in Northern Eurasia inferred from GOSAT XCO 2 M. Ishizawa et al. 10.1088/1748-9326/11/10/105001
93. All urban areas’ energy use data across 640 districts in India for the year 2011 K. Tong et al. 10.1038/s41597-021-00853-7
94. Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC–Baltimore Metropolitan Area Using an Inverse Modeling Technique I. Lopez-Coto et al. 10.1021/acs.est.9b06619
95. Generating the Nighttime Light of the Human Settlements by Identifying Periodic Components from DMSP/OLS Satellite Imagery H. Letu et al. 10.1021/acs.est.5b02471
96. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives . Zhao et al. 10.3390/rs11171971
97. Estimating regional fluxes of CO<sub>2</sub> and CH<sub>4</sub> using space-borne observations of XCH<sub>4</sub>: XCO<sub>2</sub> A. Fraser et al. 10.5194/acp-14-12883-2014
98. Column-averaged CO2 concentrations in the subarctic from GOSAT retrievals and NIES transport model simulations D. Belikov et al. 10.1016/j.polar.2014.02.002
99. Net CO<sub>2</sub> fossil fuel emissions of Tokyo estimated directly from measurements of the Tsukuba TCCON site and radiosondes A. Babenhauserheide et al. 10.5194/amt-13-2697-2020
100. On the Benefit of GOSAT Observations to the Estimation of Regional CO₂ Fluxes H. Takagi et al. 10.2151/sola.2011-041
101. Refined estimate of China's CO<sub>2</sub> emissions in spatiotemporal distributions M. Liu et al. 10.5194/acp-13-10873-2013
102. Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data V. Kinakh et al. 10.23939/mmc2021.02.304
103. Estimating CO<sub>2</sub> emissions for 108 000 European cities D. Moran et al. 10.5194/essd-14-845-2022
104. Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO2 Flux Y. Hwang et al. 10.3390/en13226009
105. Fossil fuel CO2 emissions over metropolitan areas from space: A multi-model analysis of OCO-2 data over Lahore, Pakistan R. Lei et al. 10.1016/j.rse.2021.112625
106. Influence of emission inventory resolution on the modeled spatio-temporal distribution of air pollutants in Buenos Aires, Argentina, using WRF-Chem A. López-Noreña et al. 10.1016/j.atmosenv.2021.118839
107. Isentropic transport and the seasonal cycle amplitude of CO2 E. Barnes et al. 10.1002/2016JD025109
108. NiO hollow microspheres as efficient bifunctional electrocatalysts for Overall Water-Splitting A. Mondal et al. 10.1016/j.ijhydene.2018.06.139
109. Comparing GOSAT observations of localized CO 2 enhancements by large emitters with inventory‐based estimates R. Janardanan et al. 10.1002/2016GL067843
110. A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States M. Hutchins et al. 10.1007/s11027-016-9709-9
111. The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements K. Davis et al. 10.1525/elementa.188
112. Spaceborne detection of XCO2 enhancement induced by Australian mega-bushfires J. Wang et al. 10.1088/1748-9326/abc846
113. The CO<sub>2</sub> integral emission by the megacity of St Petersburg as quantified from ground-based FTIR measurements combined with dispersion modelling D. Ionov et al. 10.5194/acp-21-10939-2021
114. Consistent weekly cycles of atmospheric NO2, CO, and CO2 in a North American megacity from ground-based, mountaintop, and satellite measurements H. Wang et al. 10.1016/j.atmosenv.2021.118809
115. A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations A. Ganshin et al. 10.5194/gmd-5-231-2012
116. High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China X. Chuai & J. Feng 10.1016/j.scitotenv.2019.05.138
117. Monthly trends of methane emissions in Los Angeles from 2011 to 2015 inferred by CLARS-FTS observations C. Wong et al. 10.5194/acp-16-13121-2016
118. Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations M. Fischer et al. 10.1002/2016JD025617
119. Network design for quantifying urban CO<sub>2</sub> emissions: assessing trade-offs between precision and network density A. Turner et al. 10.5194/acp-16-13465-2016
120. Contrasting Patterns of Urban Expansion in Colombia, Ecuador, Peru, and Bolivia Between 1992 and 2009 N. Álvarez-Berríos et al. 10.1007/s13280-012-0344-8
121. Sensitivity of simulated CO<sub>2</sub> concentration to sub-annual variations in fossil fuel CO<sub>2</sub> emissions X. Zhang et al. 10.5194/acp-16-1907-2016
122. Plant responses to volcanically elevated CO<sub>2</sub> in two Costa Rican forests R. Bogue et al. 10.5194/bg-16-1343-2019
123. Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7 H. Peiro et al. 10.5194/acp-22-1097-2022
124. Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NO<sub>x</sub> and CO<sub>2</sub> and their impacts J. Brioude et al. 10.5194/acp-13-3661-2013
125. Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data Z. Wang et al. 10.1016/j.rse.2021.112557
126. Humans and biodiversity: population and demographic trends in the hotspots J. Williams 10.1007/s11111-012-0175-3
127. Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems B. Weir et al. 10.5194/acp-21-9609-2021
128. Carbon flux estimation for Siberia by inverse modeling constrained by aircraft and tower CO2measurements T. Saeki et al. 10.1002/jgrd.50127
129. Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT L. Kunik et al. 10.1525/elementa.375
130. The added value of satellite observations of methane forunderstanding the contemporary methane budget P. Palmer et al. 10.1098/rsta.2021.0106
131. Vista-LA: Mapping methane-emitting infrastructure in the Los Angeles megacity V. Carranza et al. 10.5194/essd-10-653-2018
132. High-resolution atmospheric emission inventory of the argentine energy sector. Comparison with edgar global emission database S. Puliafito et al. 10.1016/j.heliyon.2017.e00489
133. CDIAC-FF: global and national CO<sub>2</sub> emissions from fossil fuel combustion and cement manufacture: 1751–2017 D. Gilfillan & G. Marland 10.5194/essd-13-1667-2021
134. Evaluating China's fossil-fuel CO<sub>2</sub> emissions from a comprehensive dataset of nine inventories P. Han et al. 10.5194/acp-20-11371-2020
135. Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies A. Hsu et al. 10.3389/fdata.2020.00029
136. Southern California megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model J. Hedelius et al. 10.5194/acp-18-16271-2018
137. The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network S. Crowell et al. 10.5194/acp-19-9797-2019
138. The Open-source Data Inventory for Anthropogenic CO<sub>2</sub>, version 2016 (ODIAC2016): a global monthly fossil fuel CO<sub>2</sub> gridded emissions data product for tracer transport simulations and surface flux inversions T. Oda et al. 10.5194/essd-10-87-2018
139. A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion J. Ray et al. 10.5194/gmd-8-1259-2015
140. Estimation of observation errors for large-scale atmospheric inversion of CO2 emissions from fossil fuel combustion Y. Wang et al. 10.1080/16000889.2017.1325723
141. A Study on Bio-Diesel and Jet Fuel Blending for the Production of Renewable Aviation Fuel R. El-Maghraby 10.4028/www.scientific.net/MSF.1008.231
142. Identifying local anthropogenic CO2 emissions with satellite retrievals: a case study in South Korea C. Shim et al. 10.1080/01431161.2018.1523585
143. Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources C. Kiemle et al. 10.3390/rs9111137
144. Assessing fossil fuel CO 2 emissions in California using atmospheric observations and models H. Graven et al. 10.1088/1748-9326/aabd43
145. An inversion method for estimating strong point carbon dioxide emissions using a differential absorption Lidar T. Shi et al. 10.1016/j.jclepro.2020.122434
146. Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data H. Xiao et al. 10.1016/j.apenergy.2018.09.200
147. DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration F. Hsu et al. 10.3390/rs70201855
148. Impact of Urban Surface Characteristics and Socio-Economic Variables on the Spatial Variation of Land Surface Temperature in Lagos City, Nigeria D. Dissanayake et al. 10.3390/su11010025
149. Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal P. Palmer et al. 10.1038/s41467-019-11097-w
150. Detection of fossil-fuel CO2 plummet in China due to COVID-19 by observation at Hateruma Y. Tohjima et al. 10.1038/s41598-020-75763-6
151. Characteristics of atmospheric CO2 fluxes and the estimation of their potential sources around Boseong Standard Weather Observatory (BSWO) C. Park et al. 10.1016/j.atmosenv.2021.118340
152. Temporal changes in the emissions of CH<sub>4</sub> and CO from China estimated from CH<sub>4</sub> / CO<sub>2</sub> and CO / CO<sub>2</sub> correlations observed at Hateruma Island Y. Tohjima et al. 10.5194/acp-14-1663-2014
153. On the impact of granularity of space-based urban CO2 emissions in urban atmospheric inversions: A case study for Indianapolis, IN T. Oda et al. 10.1525/elementa.146
154. Anthropogenic emission inventories in China: a review M. Li et al. 10.1093/nsr/nwx150
155. A global map of emission clumps for future monitoring of fossil fuel CO<sub>2</sub> emissions from space Y. Wang et al. 10.5194/essd-11-687-2019
156. Simulation of variability in atmospheric carbon dioxide using a global coupled Eulerian – Lagrangian transport model Y. Koyama et al. 10.5194/gmd-4-317-2011
157. Space-based detection of missing sulfur dioxide sources of global air pollution C. McLinden et al. 10.1038/ngeo2724
158. Gridded estimates of CO2 emissions: uncertainty as a function of grid size S. Hogue et al. 10.1007/s11027-017-9770-z
159. Atmospheric CO 2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence Y. Shiga et al. 10.1002/2017GL076630
160. An improved nightlight-based method for modeling urban CO2 emissions J. Han et al. 10.1016/j.envsoft.2018.05.008
161. Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2 M. Sheng et al. 10.3390/rs13173524
162. A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems V. Yadav et al. 10.1002/2016JD025642
163. Long-term urban carbon dioxide observations reveal spatial and temporal dynamics related to urban characteristics and growth L. Mitchell et al. 10.1073/pnas.1702393115
164. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm C. O'Dell et al. 10.5194/amt-11-6539-2018
165. Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution N. Zhao et al. 10.1016/j.jag.2020.102058
166. Drivers of column-average CO<sub>2</sub> variability at Southern Hemispheric Total Carbon Column Observing Network sites N. Deutscher et al. 10.5194/acp-14-9883-2014
167. Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China B. Yu et al. 10.1109/JSTARS.2015.2399416
168. Monthly, global emissions of carbon dioxide from fossil fuel consumption R. Andres et al. 10.1111/j.1600-0889.2011.00530.x
169. Regional CO<sub>2</sub> flux estimates for 2009–2010 based on GOSAT and ground-based CO<sub>2</sub> observations S. Maksyutov et al. 10.5194/acp-13-9351-2013
170. Estimation of global CO2 fluxes using ground-based and satellite (GOSAT) observation data with empirical orthogonal functions R. Zhuravlev et al. 10.1134/S1024856013060158
171. Did Polycentric and Compact Structure Reduce Carbon Emissions? A Spatial Panel Data Analysis of 286 Chinese Cities from 2002 to 2019 K. Zhu et al. 10.3390/land11020185
172. Potential of European <sup>14</sup>CO<sub>2</sub> observation network to estimate the fossil fuel CO<sub>2</sub> emissions via atmospheric inversions Y. Wang et al. 10.5194/acp-18-4229-2018
173. Resolution dependence of uncertainties in gridded emission inventories: a case study in Hebei, China B. Zheng et al. 10.5194/acp-17-921-2017
174. Estimates of CO2 Emissions and Uptake by the Water Surface near St. Petersburg Megalopolis G. Nerobelov & Y. Timofeyev 10.1134/S1024856021050158
175. Global anthropogenic CO<sub>2</sub> emissions and uncertainties as a prior for Earth system modelling and data assimilation M. Choulga et al. 10.5194/essd-13-5311-2021
176. Improved Constraints on Northern Extratropical CO 2 Fluxes Obtained by Combining Surface‐Based and Space‐Based Atmospheric CO 2 Measurements B. Byrne et al. 10.1029/2019JD032029
177. Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate L. Feng et al. 10.1038/s41467-022-28989-z
178. Mapping the Carbon Footprint of Nations K. Kanemoto et al. 10.1021/acs.est.6b03227
179. A multiyear, global gridded fossil fuel CO2emission data product: Evaluation and analysis of results S. Asefi-Najafabady et al. 10.1002/2013JD021296
180. Statistical characterization of urban CO2 emission signals observed by commercial airliner measurements T. Umezawa et al. 10.1038/s41598-020-64769-9
181. Separation of biospheric and fossil fuel fluxes of CO<sub>2</sub> by atmospheric inversion of CO<sub>2</sub> and <sup>14</sup>CO<sub>2</sub> measurements: Observation System Simulations S. Basu et al. 10.5194/acp-16-5665-2016
182. Mass-conserving tracer transport modelling on a reduced latitude-longitude grid with NIES-TM D. Belikov et al. 10.5194/gmd-4-207-2011
183. Diurnal variations and source apportionment of ozone at the summit of Mount Huang, a rural site in Eastern China J. Gao et al. 10.1016/j.envpol.2016.11.031
184. Development of a unit-based industrial emission inventory in the Beijing–Tianjin–Hebei region and resulting improvement in air quality modeling H. Zheng et al. 10.5194/acp-19-3447-2019
185. Improving resolution of a spatial air pollution inventory with a statistical inference approach J. Horabik & Z. Nahorski 10.1007/s10584-013-1029-4
186. Spaceborne detection of localized carbon dioxide sources F. Schwandner et al. 10.1126/science.aam5782
187. Potential improvements in global carbon flux estimates from a network of laser heterodyne radiometer measurements of column carbon dioxide P. Palmer et al. 10.5194/amt-12-2579-2019
188. A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions J. Ray et al. 10.5194/gmd-7-1901-2014
189. Sensitivity of simulated CO<sub>2</sub> concentration to regridding of global fossil fuel CO<sub>2</sub> emissions X. Zhang et al. 10.5194/gmd-7-2867-2014
190. Improving accuracy of economic estimations with VIIRS DNB image products N. Zhao et al. 10.1080/01431161.2017.1331060
191. The impact of transport model differences on CO<sub>2</sub> surface flux estimates from OCO-2 retrievals of column average CO<sub>2</sub> S. Basu et al. 10.5194/acp-18-7189-2018
192. High-resolution mapping of combustion processes and implications for CO<sub>2</sub> emissions R. Wang et al. 10.5194/acp-13-5189-2013
193. The impacts of fossil fuel emission uncertainties and accounting for 3-D chemical CO2 production on inverse natural carbon flux estimates from satellite and in situ data J. Wang et al. 10.1088/1748-9326/ab9795
194. Greenhouse gas observation network design for Africa A. Nickless et al. 10.1080/16000889.2020.1824486
195. Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS) Z. Chen et al. 10.1002/2016JG003716
196. Automated detection of atmospheric NO<sub>2</sub> plumes from satellite data: a tool to help infer anthropogenic combustion emissions D. Finch et al. 10.5194/amt-15-721-2022
197. Determination of the emission rates of CO<sub>2</sub> point sources with airborne lidar S. Wolff et al. 10.5194/amt-14-2717-2021
198. Assimilation of OCO-2 retrievals with WRF-Chem/DART: A case study for the Midwestern United States Q. Zhang et al. 10.1016/j.atmosenv.2020.118106
199. The effects of economic and political integration on power plants’ carbon emissions in the post-soviet transition nations A. Jorgenson et al. 10.1088/1748-9326/aa650b
200. Terrestrial ecosystem carbon flux estimated using GOSAT and OCO-2 XCO<sub>2</sub> retrievals H. Wang et al. 10.5194/acp-19-12067-2019
201. Distinguishing Anthropogenic CO2 Emissions From Different Energy Intensive Industrial Sources Using OCO-2 Observations: A Case Study in Northern China S. Wang et al. 10.1029/2018JD029005
202. Large Uncertainties in Urban‐Scale Carbon Emissions C. Gately & L. Hutyra 10.1002/2017JD027359
203. A Cluster of CO2 Change Characteristics with GOSAT Observations for Viewing the Spatial Pattern of CO2 Emission and Absorption D. Liu et al. 10.3390/atmos6111695
204. Comparing a global high-resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities J. Chen et al. 10.1186/s13021-020-00146-3
205. Relationships between CO₂ Flux Estimated by Inverse Analysis and Land Surface Elements in South America and Africa K. MABUCHI et al. 10.2151/jmsj.2016-021
206. Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint S. Maksyutov et al. 10.5194/acp-21-1245-2021
207. Using Space‐Based Observations and Lagrangian Modeling to Evaluate Urban Carbon Dioxide Emissions in the Middle East E. Yang et al. 10.1029/2019JD031922
208. Investigating the effect of carbon leakage on the environmental Kuznets curve using luminosity data A. Steinkraus 10.1017/S1355770X17000249
209. A spatial uncertainty metric for anthropogenic CO2emissions D. Woodard et al. 10.1080/20430779.2014.1000793
210. Mapping spatio-temporal changes of Chinese electric power consumption using night-time imagery N. Zhao et al. 10.1080/01431161.2012.684076
211. Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network J. Tao & X. Kong 10.1038/s41598-021-93456-6
212. WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
213. Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China L. Meng et al. 10.1016/j.energy.2014.04.103
214. Constraining Fossil Fuel CO 2 Emissions From Urban Area Using OCO‐2 Observations of Total Column CO 2 X. Ye et al. 10.1029/2019JD030528
215. Estimates of European uptake of CO<sub>2</sub> inferred from GOSAT X<sub>CO<sub>2</sub></sub> retrievals: sensitivity to measurement bias inside and outside Europe L. Feng et al. 10.5194/acp-16-1289-2016
216. Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation D. Belikov et al. 10.5194/gmd-9-749-2016
217. A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China C. Li et al. 10.3390/ijerph17103416
218. An atmospheric inversion over the city of Cape Town: sensitivity analyses A. Nickless et al. 10.5194/acp-19-7789-2019
219. The Effect of GOSAT Observations on Estimates of Net CO₂ Flux in Semi-Arid Regions of the Southern Hemisphere M. Kondo et al. 10.2151/sola.2016-037
220. High‐resolution atmospheric inversion of urban CO 2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX) T. Lauvaux et al. 10.1002/2015JD024473
221. High Pressure Supercritical Carbon Dioxide Separation from its Mixture with Nitrogen at Different Temperatures R. El-Maghraby et al. 10.4028/www.scientific.net/MSF.1008.1
222. A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations Z. He et al. 10.3390/rs9030251
223. Province-level fossil fuel CO2 emission estimates for China based on seven inventories P. Han et al. 10.1016/j.jclepro.2020.123377
224. Tracking city CO<sub>2</sub> emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany D. Pillai et al. 10.5194/acp-16-9591-2016
225. Large scale synthesis of Mo2C nanoparticle incorporated carbon nanosheet (Mo2C–C) for enhanced hydrogen evolution reaction A. Mondal et al. 10.1016/j.ijhydene.2019.09.051
226. Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia) G. Nerobelov et al. 10.3390/atmos12030387
227. The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements D. Yang et al. 10.1007/s00376-021-1179-7
228. Regional-Scale Estimation of Electric Power and Power Plant CO2 Emissions Using Defense Meteorological Satellite Program Operational Linescan System Nighttime Satellite Data H. Letu et al. 10.1021/ez500093s
229. Estimation of virtual water contained in international trade products using nighttime imagery N. Zhao & E. Samson 10.1016/j.jag.2012.02.002
230. High-resolution inventory of technologies, activities, and emissions of coal-fired power plants in China from 1990 to 2010 F. Liu et al. 10.5194/acp-15-13299-2015
231. The potential of CO2 satellite monitoring for climate governance: A review G. Pan et al. 10.1016/j.jenvman.2020.111423
232. High-resolution spatiotemporal patterns of China’s FFCO2 emissions under the impact of LUCC from 2000 to 2015 J. Zhao et al. 10.1088/1748-9326/ab6edc
233. Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage Y. Cui et al. 10.1016/j.apenergy.2019.03.060
234. Assessing nighttime lights for mapping the urban areas of 50 cities across the globe H. Bagan et al. 10.1177/2399808317752926
235. Examining CO 2 Model Observation Residuals Using ACT‐America Data T. Gerken et al. 10.1029/2020JD034481
236. A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) D. Wu et al. 10.5194/gmd-11-4843-2018
237. Improving Nighttime Light Imagery With Location-Based Social Media Data N. Zhao et al. 10.1109/TGRS.2018.2871788
238. Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US S. Miller & A. Michalak 10.5194/acp-17-3963-2017
239. Urban macro-level impact factors on Direct CO2 Emissions of urban residents in China J. Zhang et al. 10.1016/j.enbuild.2015.08.011
240. Mapping Global Fossil Fuel Combustion CO2 Emissions at High Resolution by Integrating Nightlight, Population Density, and Traffic Network Data J. Ou et al. 10.1109/JSTARS.2015.2476347
241. Estimating Socio-economic Indicators Through Nighttime Lights: From DMSP/OLS to Suomi NPP/VIIRS-DNB T. Nakaya 10.3169/itej.72.569
242. Addressing Measurement Error Bias in GDP with Nighttime Lights and an Application to Infant Mortality with Chinese County Data X. Chen 10.1177/0081175016654737
243. Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review Q. Huang et al. 10.3390/rs6086844
244. Regional uncertainty of GOSAT XCO<sub>2</sub> retrievals in China: quantification and attribution N. Bie et al. 10.5194/amt-11-1251-2018
245. Dark Times: nighttime satellite imagery as a detector of regional disparity and the geography of conflict L. Coscieme et al. 10.1080/15481603.2016.1260676
246. Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from U.S. megacities D. Goldberg et al. 10.1016/j.scitotenv.2019.133805
247. Nighttime Lights and Population Migration: Revisiting Classic Demographic Perspectives with an Analysis of Recent European Data X. Chen 10.3390/rs12010169
248. Cropland Carbon Uptake Delayed and Reduced by 2019 Midwest Floods Y. Yin et al. 10.1029/2019AV000140
249. Estimation of the Distribution of Global Anthropogenic Heat Flux C. Bing & S. Guang-Yu 10.1080/16742834.2012.11446974
250. Data Processing and Analysis Approach to Retrieve Carbon Dioxide Weighted-Column Mixing Ratio and 2-<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>m Reflectance With an Airborne Laser Absorption Spectrometer J. Jacob et al. 10.1109/TGRS.2018.2863711
251. A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method H. Liu et al. 10.3390/su122310058
252. Estimating regional greenhouse gas fluxes: an uncertainty analysis of planetary boundary layer techniques and bottom-up inventories X. Zhang et al. 10.5194/acp-14-10705-2014
253. A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling N. Charkovska et al. 10.1007/s11027-018-9836-6
254. How do CO2 emissions and efficiencies vary in Chinese cities? Spatial variation and driving factors in 2007 Y. Tian & W. Zhou 10.1016/j.scitotenv.2019.04.239
255. How and why did fossil fuel use change in Fukushima Prefecture before and after the Great East Japan Earthquake? R. Cong et al. 10.1016/j.egyr.2021.12.046
256. Quantification of urban atmospheric boundary layer greenhouse gas dry mole fraction enhancements in the dormant season: Results from the Indianapolis Flux Experiment (INFLUX) N. Miles et al. 10.1525/elementa.127
257. A city-level comparison of fossil-fuel and industry processes-induced CO2 emissions over the Beijing-Tianjin-Hebei region from eight emission inventories P. Han et al. 10.1186/s13021-020-00163-2
258. Impact of Prior Terrestrial Carbon Fluxes on Simulations of Atmospheric CO 2 Concentrations Y. Fu et al. 10.1029/2021JD034794
259. Poverty alleviation and local environmental degradation: An empirical analysis in Colombia D. Malerba 10.1016/j.worlddev.2019.104776
260. Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements N. Wu et al. 10.1088/1748-9326/ac175f
261. A new global gridded data set of CO2emissions from fossil fuel combustion: Methodology and evaluation P. Rayner et al. 10.1029/2009JD013439
262. Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting H. Lu & G. Liu 10.1016/j.apenergy.2014.06.036
263. Spatial Pattern of Residential Carbon Dioxide Emissions in a Rapidly Urbanizing Chinese City and Its Mismatch Effect H. Lu et al. 10.3390/su10030827
264. A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area X. Liu et al. 10.3390/rs71215863
265. Creating a Global Grid of Distributed Fossil Fuel CO2 Emissions from Nighttime Satellite Imagery T. Ghosh et al. 10.3390/en3121895
266. Urban CO2 emissions in China: Spatial boundary and performance comparison B. Cai & L. Zhang 10.1016/j.enpol.2013.10.072
267. Estimating spatiotemporal variations of city-level energy-related CO2 emissions: An improved disaggregating model based on vegetation adjusted nighttime light data X. Liu et al. 10.1016/j.jclepro.2017.12.197
268. Developing a high-resolution emission inventory tool for low-carbon city management using hybrid method – A pilot test in high-density Hong Kong M. Cai et al. 10.1016/j.enbuild.2020.110376
269. Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data J. Ou et al. 10.1371/journal.pone.0138310
270. A remote sensing technique for global monitoring of power plant CO&lt;sub&gt;2&lt;/sub&gt; emissions from space and related applications H. Bovensmann et al. 10.5194/amt-3-781-2010
271. Analysis of the Economic Ripple Effect of the United States on the World due to Future Climate Change Z. Zhang et al. 10.1029/2018EF000839
272. Disaggregation Method of Carbon Emission: A Case Study in Wuhan, China M. Luo et al. 10.3390/su11072093