{
  "abstract": "Accurate monitoring of atmospheric CO 2 is crucial for understanding climate change, but satellite observations from missions like OCO-2 have significant spatial and temporal gaps. This study addresses this challenge by developing a high-resolution, full-coverage dataset for a key emission region. We generated a 1km monthly column-averaged CO 2 (XCO 2 ) dataset for China from 2015 to 2021 using a comprehensive set of predictors, including meteorological,vegetation, and anthropogenic activity-related data. We compared four machine learning models (Random Forest, AdaBoost, ANN, and CNN) and selected the best-performing AdaBoost model, validated using 10-fold cross-validation and independent ground-based TCCON data. The AdaBoost model demonstrated superior performance (CV R 2 = 0.85), and the final product showed high accuracy against TCCON measurements (Overall R 2 = 0.93, RMSE = 1.19 ppm), demonstrating competitive or improved accuracy compared with existing products where directly comparable. The reconstructed dataset reveals significant spatiotemporal heterogeneity, with high XCO 2 concentrations in China's major industrial and urban agglomerations, a robust seasonal cycle, and a consistent upward trend. Feature importance analysis confirmed that anthropogenic emissions, meteorological conditions, and biospheric activity are the primary drivers of XCO 2 distribution. This study provides a reliable, high-resolution XCO 2 dataset that is valuable for monitoring China's carbon dynamics, verifying emission inventories, and informing climate mitigation policies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Yuanyuan Yang"
    },
    {
      "name": "Qianqian Chen"
    },
    {
      "name": "Zhou Shi"
    },
    {
      "name": "Rui Lu"
    },
    {
      "name": "Xiangrui Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9d1v9pch7hhc8.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ecoinf.2026.103921",
  "fulltext_url": null,
  "id": "9d1v9pch7hhc8",
  "issns": [
    "1574-9541"
  ],
  "landing_url": "https://hal.inrae.fr/hal-05747408v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sde"
    ],
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.sa"
    ],
    [
      "hal",
      "2.sdv.sa.sds"
    ],
    [
      "openalex",
      "2306|Atmospheric and Environmental Gas Dynamics"
    ]
  ],
  "openalex": "W7168144813",
  "pdf_url": "https://hal.inrae.fr/hal-05747408/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/9d1v9pch7hhc8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:34Z",
      "native_id": "hal-05747408",
      "source": "hal",
      "url": "https://hal.inrae.fr/hal-05747408v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    },
    {
      "field": "earth",
      "origin": "hal:0.sde",
      "subfield": "environmental-science"
    }
  ],
  "title": "Full-coverage and fine-scale spatiotemporal retrieval of XCO2 in China based on OCO-2 satellite observations",
  "title_text": "Full-coverage and fine-scale spatiotemporal retrieval of XCO2 in China based on OCO-2 satellite observations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:34Z",
  "url": "https://research.draconitic.com/paper/9d1v9pch7hhc8",
  "venue": "Ecological Informatics",
  "versions": []
}