An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(5), 1118; https://doi.org/10.3390/s19051118
Received: 2 January 2019
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Revised: 13 February 2019
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Accepted: 28 February 2019
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Published: 5 March 2019
(This article belongs to the Section Remote Sensors)
Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.
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MDPI and ACS Style
Yang, S.; Lei, L.; Zeng, Z.; He, Z.; Zhong, H. An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China. Sensors 2019, 19, 1118. https://doi.org/10.3390/s19051118
AMA Style
Yang S, Lei L, Zeng Z, He Z, Zhong H. An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China. Sensors. 2019; 19(5):1118. https://doi.org/10.3390/s19051118
Chicago/Turabian StyleYang, Shaoyuan, Liping Lei, Zhaocheng Zeng, Zhonghua He, and Hui Zhong. 2019. "An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China" Sensors 19, no. 5: 1118. https://doi.org/10.3390/s19051118
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