Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2021
Research article |
| 18 Nov 2021
| 18 Nov 2021
Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
Farhan Mustafa
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Lingbing Bu
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Qin Wang
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Na Yao
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Muhammad Shahzaman
School of Atmospheric Sciences (SAS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Muhammad Bilal
School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Rana Waqar Aslam
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
Rashid Iqbal
Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
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- Quantifying the drivers of surface ozone anomalies in the urban areas over the Qinghai-Tibet Plateau H. Yin et al. 10.5194/acp-22-14401-2022
- A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration Z. Ji et al. 10.3390/atmos15030323
- Comparison of Atmospheric Carbon Dioxide Concentrations Based on GOSAT, OCO-2 Observations and Ground-Based TCCON Data J. Zheng et al. 10.3390/rs15215172
- Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2 S. Zhang et al. 10.3390/rs14195024
- Health Implications of Arsenic and Qualitative Deterioration of drinking Water from Underground Water Supply Lines of Lahore, Pakistan H. Mehmood et al. 10.33411/IJIST/2022040106
- Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023 R. Fonseca & D. Francis 10.3389/fenvs.2023.1289142
- Spatiotemporal Investigation of Near-Surface CO2 and Its Affecting Factors Over Asia F. Mustafa et al. 10.1109/TGRS.2022.3178125
- Exploring CO2 anomalies in Brazilian biomes combining OCO-2 & 3 data: Linkages to wildfires patterns L. da Costa et al. 10.1016/j.asr.2024.01.016
- Airborne atmospheric carbon dioxide measurement using 1.5 µm laser double-pulse IPDA lidar over a desert area C. Fan et al. 10.1364/AO.507905
- Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model Y. Zhang et al. 10.3390/rs14163899
- Monitoring of Atmospheric Carbon Dioxide over Pakistan Using Satellite Dataset N. An et al. 10.3390/rs14225882
- Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models H. Zhang et al. 10.1016/j.jclepro.2024.141043
- Monitoring of Atmospheric Carbon Dioxide over a Desert Site Using Airborne and Ground Measurements Q. Wang et al. 10.3390/rs14205224
- Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019 M. Zhang & G. Liu 10.1016/j.scitotenv.2022.159588
- Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data R. Aslam et al. 10.3390/rs16050928
- Assessment and Monitoring of VIIRS-DNB and SQML-L light Pollution in Lahore-Pakistan H. Nisar et al. 10.33411/IJIST/2022040107
- Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model J. Chen et al. 10.1016/j.isprsjprs.2024.01.009
- The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data M. Wang et al. 10.1016/j.jenvman.2023.119054
- Spatio-Temporal Dynamics of Rangeland Transformation using machine learning algorithms and Remote Sensing data N. Wang et al. 10.1016/j.rama.2024.02.008
- Estimation of anthropogenic CO2 emissions at different scales for assessing SDG indicators: Method and application Y. Hua et al. 10.1016/j.jclepro.2023.137547
- Spaceborne tropospheric nitrogen dioxide (NO<sub>2</sub>) observations from 2005–2020 over the Yangtze River Delta (YRD), China: variabilities, implications, and drivers H. Yin et al. 10.5194/acp-22-4167-2022
- Remote Sensing Monitoring and Analysis of Spatiotemporal Changes in China’s Anthropogenic Carbon Emissions Based on XCO2 Data Y. Wang et al. 10.3390/rs15123207
- Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia F. Mustafa et al. 10.5194/amt-14-7277-2021
23 citations as recorded by crossref.
- Global-Scale Evaluation of XCO2 Products from GOSAT, OCO-2 and CarbonTracker Using Direct Comparison and Triple Collocation Method Y. Chen et al. 10.3390/rs14225635
- Quantifying the drivers of surface ozone anomalies in the urban areas over the Qinghai-Tibet Plateau H. Yin et al. 10.5194/acp-22-14401-2022
- A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration Z. Ji et al. 10.3390/atmos15030323
- Comparison of Atmospheric Carbon Dioxide Concentrations Based on GOSAT, OCO-2 Observations and Ground-Based TCCON Data J. Zheng et al. 10.3390/rs15215172
- Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2 S. Zhang et al. 10.3390/rs14195024
- Health Implications of Arsenic and Qualitative Deterioration of drinking Water from Underground Water Supply Lines of Lahore, Pakistan H. Mehmood et al. 10.33411/IJIST/2022040106
- Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023 R. Fonseca & D. Francis 10.3389/fenvs.2023.1289142
- Spatiotemporal Investigation of Near-Surface CO2 and Its Affecting Factors Over Asia F. Mustafa et al. 10.1109/TGRS.2022.3178125
- Exploring CO2 anomalies in Brazilian biomes combining OCO-2 & 3 data: Linkages to wildfires patterns L. da Costa et al. 10.1016/j.asr.2024.01.016
- Airborne atmospheric carbon dioxide measurement using 1.5 µm laser double-pulse IPDA lidar over a desert area C. Fan et al. 10.1364/AO.507905
- Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model Y. Zhang et al. 10.3390/rs14163899
- Monitoring of Atmospheric Carbon Dioxide over Pakistan Using Satellite Dataset N. An et al. 10.3390/rs14225882
- Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models H. Zhang et al. 10.1016/j.jclepro.2024.141043
- Monitoring of Atmospheric Carbon Dioxide over a Desert Site Using Airborne and Ground Measurements Q. Wang et al. 10.3390/rs14205224
- Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019 M. Zhang & G. Liu 10.1016/j.scitotenv.2022.159588
- Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data R. Aslam et al. 10.3390/rs16050928
- Assessment and Monitoring of VIIRS-DNB and SQML-L light Pollution in Lahore-Pakistan H. Nisar et al. 10.33411/IJIST/2022040107
- Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model J. Chen et al. 10.1016/j.isprsjprs.2024.01.009
- The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data M. Wang et al. 10.1016/j.jenvman.2023.119054
- Spatio-Temporal Dynamics of Rangeland Transformation using machine learning algorithms and Remote Sensing data N. Wang et al. 10.1016/j.rama.2024.02.008
- Estimation of anthropogenic CO2 emissions at different scales for assessing SDG indicators: Method and application Y. Hua et al. 10.1016/j.jclepro.2023.137547
- Spaceborne tropospheric nitrogen dioxide (NO<sub>2</sub>) observations from 2005–2020 over the Yangtze River Delta (YRD), China: variabilities, implications, and drivers H. Yin et al. 10.5194/acp-22-4167-2022
- Remote Sensing Monitoring and Analysis of Spatiotemporal Changes in China’s Anthropogenic Carbon Emissions Based on XCO2 Data Y. Wang et al. 10.3390/rs15123207
Latest update: 19 Apr 2024
Short summary
A neural-network-based approach was suggested to estimate CO2 emissions using satellite-based net primary productivity (NPP) and XCO2 retrievals. XCO2 anomalies were calculated for each year using OCO-2 retrievals. A Generalized Regression Neural Network (GRNN) model was then built; NPP, XCO2 anomalies, and ODIAC CO2 emissions from 2015 to 2018 were used as a training dataset; and, finally, CO2 emissions were predicted for 2019 based on the NPP and XCO2 anomalies calculated for the same year.
A neural-network-based approach was suggested to estimate CO2 emissions using satellite-based...
