XCO<sub>2</sub> Data Full-Coverage Mapping in China Based on Random Forest Models

XCO<sub>2</sub> Data Full-Coverage Mapping in China Based on Random Forest Models

Carbon dioxide (CO<sub>2</sub>) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO<sub>2</sub> levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes i...

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Bibliographic Details
Main Authors: Ruizhi Chen, Zhongting Wang, Chunyan Zhou, Ruijie Zhang, Huizhen Xie, Huayou Li
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Remote Sensing
Subjects:
carbon dioxide
random forest
atmospheric remote sensing
Chinese region
GF-5B
Online Access:https://www.mdpi.com/2072-4292/17/1/48
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Summary:Carbon dioxide (CO<sub>2</sub>) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO<sub>2</sub> levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes in CO<sub>2</sub> concentrations and to support the development of climate policies, this study proposes a method based on random forest models to generate a continuous monthly dataset of CO<sub>2</sub> column concentration (XCO<sub>2</sub>) across the entire Chinese region from 2004 to 2023. The study integrates XCO<sub>2</sub> satellite observations from SCIAMACHY, GOSAT, OCO-2, and GF-5B, alongside nighttime light remote sensing data, meteorological parameters, vegetation indices, and CO<sub>2</sub> profile data. Using the random forest algorithm, a complex relationship model was established between XCO<sub>2</sub> concentrations and various environmental variables. The goal of this model is to provide XCO<sub>2</sub> estimates with enhanced spatial coverage and accuracy. The XCO<sub>2</sub> concentrations predicted by the model show a high level of consistency with satellite observations, achieving a correlation coefficient (R-value) of 0.9959 and a root mean square error (RMSE) of 1.1631 ppm. This indicates that the model offers strong predictive accuracy and generalization ability. Additionally, ground-based validation further confirmed the model’s effectiveness, with a correlation coefficient (R-value) of 0.956 when compared with TCCON site observation data.
ISSN:2072-4292

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