A long-term global XCO2 dataset: Ensemble of satellite products

Jin, Chunlin; Xue, Yong; Jiang, Xingxing; Zhao, Liang; Yuan, Tao; Sun, Yuxin; Wu, Shuhui; Wang, Xiangkai

doi:10.1016/j.atmosres.2022.106385

Cited by 13 publications

(2 citation statements)

References 32 publications

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“…It combines ten XCO 2 products derived from SCIAMACHY, OCO-2, GOSAT, and GOSAT-2 by calculating the median over a spatial range of 10°×10° to produce the final XCO 2 values. The ensemble method is effective in reducing biases as well as occasional outliers [41], [42].…”

Section: A Satellite Xco 2 Datamentioning

confidence: 99%

Validation of Remotely Sensed XCO₂ Products With TCCON Observations in East Asia

Ji,

Xu,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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 As an important greenhouse gas (GHG) in the atmosphere, Carbon dioxide (CO2) has a great impact on global climate change. Accurate knowledge of the spatiotemporal variations of CO2 is of great significance for understanding the carbon cycle and evaluating the effectiveness of carbon emission reduction. In recent years, several satellites with CO2 sensors have been launched and a series of atmospheric CO2 concentration products have been developed using different retrieval algorithms. This study validated nine satellite XCO2 products derived from Greenhouse gases Observing SATellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory-2 (OCO-2) and OCO-3: including ACOS-GOSAT, NIES-GOSAT, BESD-GOSAT, OCFP-GOSAT, SRFP-GOSAT, EMMA, GOSAT-2, OCO-2, and OCO-3 XCO2. The remotely sensed XCO2 products were compared with the XCO2 observations from six Total Carbon Column Observing Network (TCCON) stations in East Asia for validation. The results showed that the OCO-2 XCO2 product outperformed other products, with the highest R 2 of 0.94 and the lowest MAE of 1.24 ppm. The ACOS-GOSAT and EMMA-GOSAT XCO2 products also showed favorable accuracies, both achieving the R 2 of 0.93 and corresponding

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Section: A Satellite Xco 2 Datamentioning

confidence: 99%

Validation of Remotely Sensed XCO₂ Products With TCCON Observations in East Asia

Ji,

Xu,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In previous research, three main approaches have been developed to reconstruct high-coverage XCO 2 (carbon dioxide column-averaged dry air mole fraction) data [14], including spatial interpolation [15], multisensor fusion [16], and modeling based on machine learning [8].…”

mentioning

confidence: 99%

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Hu,

Zhang,

Feng

et al. 2024

Remote Sensing

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Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.

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Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model

Cui,

Yang,

Qiao

et al. 2024

Atmospheric Research

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A long-term global XCO2 dataset: Ensemble of satellite products

Cited by 13 publications

References 32 publications

Validation of Remotely Sensed XCO₂ Products With TCCON Observations in East Asia

Validation of Remotely Sensed XCO₂ Products With TCCON Observations in East Asia

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model

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A long-term global XCO2 dataset: Ensemble of satellite products

Cited by 13 publications

References 32 publications

Validation of Remotely Sensed XCO2 Products With TCCON Observations in East Asia

Validation of Remotely Sensed XCO2 Products With TCCON Observations in East Asia

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model

Contact Info

Product

Resources

About

Validation of Remotely Sensed XCO₂ Products With TCCON Observations in East Asia

Validation of Remotely Sensed XCO₂ Products With TCCON Observations in East Asia