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<p>Accurately estimating aboveground biomass (AGB) is crucial for understanding terrestrial carbon cycling and informing climate policy. China’s diverse topography and rich vegetation types make it a significant contributor to the global carbon stock. However, existing AGB products often lack sufficient spatial resolution, data consistency, and accessibility to fully capture biomass patterns across the country’s varied ecosystems. Here, we present a 30 m-resolution, nationwide dataset of AGB density (AGBD) for China in 2020, which integrates multiple vegetation types. This product was developed using openly accessible, multi-source remote sensing data including LiDAR, optical, and radar imagery and enables consistent mapping of forests, grasslands, shrublands, croplands and wetlands. Systematic validation using field observations, national statistical yearbooks, and spatial distribution patterns, alongside comparisons with existing AGB products, demonstrates the model’s high accuracy with an average R<sup>2</sup> of 0.85, RMSE of 31.26 Mg/ha and rRMSE of 50.04%, estimating a total carbon stock of 20.20 Pg across China’s vegetation areas. This dataset provides an updated, comprehensive baseline for biomass assessment, supporting carbon accounting and biodiversity monitoring in China.</p>

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A 30 m aboveground biomass dataset for multiple vegetation types in China (2020)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月12日

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  • Xiaoya Wang,
  • Bo Zhong,
  • Bo Chang,
  • Yulin Zhang,
  • Shuaifeng Li,
  • Bailin Du,
  • Yang Qiao,
  • He Cai,
  • Junjun Wu,
  • Aixia Yang,
  • Yi Jian,
  • Jianrong Su,
  • Genxu Wang,
  • Qinhuo Liu

摘要

Accurately estimating aboveground biomass (AGB) is crucial for understanding terrestrial carbon cycling and informing climate policy. China’s diverse topography and rich vegetation types make it a significant contributor to the global carbon stock. However, existing AGB products often lack sufficient spatial resolution, data consistency, and accessibility to fully capture biomass patterns across the country’s varied ecosystems. Here, we present a 30 m-resolution, nationwide dataset of AGB density (AGBD) for China in 2020, which integrates multiple vegetation types. This product was developed using openly accessible, multi-source remote sensing data including LiDAR, optical, and radar imagery and enables consistent mapping of forests, grasslands, shrublands, croplands and wetlands. Systematic validation using field observations, national statistical yearbooks, and spatial distribution patterns, alongside comparisons with existing AGB products, demonstrates the model’s high accuracy with an average R2 of 0.85, RMSE of 31.26 Mg/ha and rRMSE of 50.04%, estimating a total carbon stock of 20.20 Pg across China’s vegetation areas. This dataset provides an updated, comprehensive baseline for biomass assessment, supporting carbon accounting and biodiversity monitoring in China.