Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model

Chen, Jiabin; Hu, Ruohua; Chen, Leyan; Liao, Zihao; Che, Linlin; Li, Tongwen

doi:10.1016/j.isprsjprs.2024.01.009

ISPRS Journal of Photogrammetry and Remote Sensing

2024

DOI: 10.1016/j.isprsjprs.2024.01.009

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Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model

Jiabin Chen,

Ruohua Hu,

Leyan Chen

et al.

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Cited by 3 publications

References 51 publications

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Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

He,

Wang,

Wang

2024

Advances in Space Research

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Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

He,

Wang,

Wang

2024

Advances in Space Research

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Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model

Wu,

Yang,

Jiao

et al. 2024

Science of The Total Environment

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Enhancing Space-Based Tracking of Fossil Fuel CO₂ Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI Measurements

Li,

Zheng,

Liu

et al. 2024

Environ. Sci. Technol.

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Top-down estimates of fossil fuel CO 2 (FFCO 2 ) emissions are crucial for tracking emissions and evaluating mitigation strategies. However, their practical application is hindered by limited data coverage and overreliance on NOx-to-CO 2 emission ratios from emission inventories. We developed the Machine Learning-Driven Mapping Satellite-based XCO 2en (ML-MSXE) model using the columnaveraged dry-air mole fraction of CO 2 enhancement (XCO 2en ) derived from OCO-2 and OCO-3 measurements to reconstruct the XCO 2en distribution for monitoring FFCO 2 emissions. Compared to the previous Machine Learning-Driven Deriving XCO 2en from Mapped XCO 2 (ML-DXEMX) model, ML-MSXE enhances the utilization of TROPOMI NO 2 measurements, increasing their relative contribution from 4.3 to 21.7%, thereby improving XCO 2en reconstruction accuracy and enhancing the ability to track emissions. Despite the COVID-19 lockdown, XCO 2en levels in China rose from 1.33 ± 1.06 in 2019 to 1.39 ± 1.01 ppm in 2021. In February 2020, while the national average rate of XCO 2en decline (16.3%) aligned with the reduction in FFCO 2 emissions estimated by inventories, XCO 2en further revealed varying rates of decline between cities. Furthermore, the spatial distribution of XCO 2en identified hotspots where FFCO 2 emissions might be underestimated by inventories. This study presents a space-based approach for monitoring FFCO 2 emissions, offering valuable insights for assessing carbon neutrality progress and informing policy.

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Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model

Cited by 3 publications

References 51 publications

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model

Enhancing Space-Based Tracking of Fossil Fuel CO₂ Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI Measurements

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Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model

Cited by 3 publications

References 51 publications

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model

Enhancing Space-Based Tracking of Fossil Fuel CO2 Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI Measurements

Contact Info

Product

Resources

About

Enhancing Space-Based Tracking of Fossil Fuel CO₂ Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI Measurements