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

He, Junchen; Wang, Wei; Wang, Nan

doi:10.1016/j.asr.2024.07.007

Advances in Space Research

2024

DOI: 10.1016/j.asr.2024.07.007

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

Junchen He,

Wei Wang,

Nan Wang

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Cited by 3 publications

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Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method

Wang,

Chen

2024

Advances in Space Research

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Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method

Wang,

Chen

2024

Advances in Space Research

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Insights into global visibility patterns: Spatiotemporal distributions revealed by satellite remote sensing

He,

Wang,

et al. 2024

Journal of Cleaner Production

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Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model

Jin,

He,

Wang

2024

Remote Sensing

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Atmospheric methane is one of the major greenhouse gases with a drastic impact on climate change. This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration.

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

Cited by 3 publications

References 63 publications

Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method

Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method

Insights into global visibility patterns: Spatiotemporal distributions revealed by satellite remote sensing

Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model

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