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DOI | 10.3390/s19051118 |
An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China | |
Yang, Shaoyuan1,2; Lei, Liping1; Zeng, Zhaocheng3; He, Zhonghua1,2; Zhong, Hui1,2 | |
发表日期 | 2019 |
EISSN | 1424-8220 |
卷号 | 19期号:5 |
英文摘要 | 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 (dXCO(2)) 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 dXCO(2) 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. |
WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
来源期刊 | SENSORS |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/94214 |
作者单位 | 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.CALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA |
推荐引用方式 GB/T 7714 | Yang, Shaoyuan,Lei, Liping,Zeng, Zhaocheng,et al. An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China[J],2019,19(5). |
APA | Yang, Shaoyuan,Lei, Liping,Zeng, Zhaocheng,He, Zhonghua,&Zhong, Hui.(2019).An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China.SENSORS,19(5). |
MLA | Yang, Shaoyuan,et al."An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China".SENSORS 19.5(2019). |
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