Full-coverage estimation of CO₂ concentrations in China via multisource satellite data and Deep Forest model.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-14 DOI:10.1038/s41597-024-04063-9

Kun Cai, Liuyin Guan, Shenshen Li, Shuo Zhang, Yang Liu, Yang Liu

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Abstract

Monitoring China's carbon dioxide (CO₂) concentration is essential for formulating effective carbon cycle policies to achieve carbon peaking and neutrality. Despite insufficient satellite observation coverage, this study utilizes high-resolution spatiotemporal data from the Orbiting Carbon Observatory 2 (OCO-2), supplemented with various auxiliary datasets, to estimate full-coverage, monthly, column-averaged carbon dioxide (XCO₂) values across China from 2015 to 2022 at a spatial resolution of 0.05° via the deep forest model. The 10-fold cross-validation results indicate a correlation coefficient (R) of 0.95 and a determination coefficient (R²) of 0.90. Validation against ground-based station data yielded R values of 0.93, and R² values reached 0.81. Further validation from the Greenhouse Gases Observing Satellite (GOSAT) and the Copernicus Atmosphere Monitoring Service Reanalysis dataset (CAMS) produced R² values of 0.87 and 0.80, respectively. During the study period, CO₂ concentrations in China were higher in spring and winter than in summer and autumn, indicating a clear annual increase. The estimates generated by this study could potentially support CO₂ monitoring in China.

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通过多源卫星数据和深林模型对中国二氧化碳浓度进行全覆盖估算。

监测中国的二氧化碳（CO2）浓度对于制定有效的碳循环政策以实现碳封顶和碳中和至关重要。尽管卫星观测覆盖面不足，但本研究利用轨道碳观测站 2 号（OCO-2）的高分辨率时空数据，辅以各种辅助数据集，通过深林模型估算了 2015 年至 2022 年中国全覆盖、月度、柱平均二氧化碳（XCO2）值，空间分辨率为 0.05°。10 倍交叉验证结果表明，相关系数 (R) 为 0.95，判定系数 (R²) 为 0.90。通过对地面站数据的验证，R 值为 0.93，R² 值达到 0.81。温室气体观测卫星（GOSAT）和哥白尼大气监测服务再分析数据集（CAMS）的进一步验证产生的 R² 值分别为 0.87 和 0.80。在研究期间，中国春季和冬季的二氧化碳浓度高于夏季和秋季，显示出明显的逐年上升趋势。本研究得出的估算值有可能为中国的二氧化碳监测提供支持。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.