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<p>Grasslands are critical to the global carbon cycle and support livestock production, yet long-term, high-resolution global datasets of grassland aboveground biomass (AGB) remain scarce, since existing products are often spatially coarse, temporally discontinuous, or regionally limited. Here, we present a global grassland AGB dataset spanning 2000–2022 at 0.005° spatial resolution. The dataset was generated by employing an optimized random forest model, which was trained on 11,265 spatially aggregated samples with global distribution. The multi-source predictors included Normalized Difference Vegetation Index (NDVI), climate variables, atmospheric CO<sub>2</sub> concentration, and topographical factors. At the global scale, the model achieved an R<sup>2</sup> of 0.60 for both validation and independent testing, with RMSEs of 85.97 and 93.69 g m<sup>−2</sup>, respectively. When evaluated across all samples, the model exhibited an R<sup>2</sup> of 0.93 and an RMSE of 51.85 g m<sup>−2</sup>. Comparison with other datasets showed its comparable value distributions and interannual trajectories, and closer agreement with field observations. This dataset provides a foundation for advancing research on global grassland carbon dynamics and management practices.</p>

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A long-term (2000–2022), high-resolution (0.005°) aboveground biomass dataset of global grasslands

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Pinzhen Wu,
  • Qingling Sun,
  • Daju Wang,
  • Jiahe Si,
  • Baolin Li,
  • Xuetong Zhao,
  • Siyu Zhu,
  • Yong Li,
  • Xiuzhi Chen,
  • Wenping Yuan

摘要

Grasslands are critical to the global carbon cycle and support livestock production, yet long-term, high-resolution global datasets of grassland aboveground biomass (AGB) remain scarce, since existing products are often spatially coarse, temporally discontinuous, or regionally limited. Here, we present a global grassland AGB dataset spanning 2000–2022 at 0.005° spatial resolution. The dataset was generated by employing an optimized random forest model, which was trained on 11,265 spatially aggregated samples with global distribution. The multi-source predictors included Normalized Difference Vegetation Index (NDVI), climate variables, atmospheric CO2 concentration, and topographical factors. At the global scale, the model achieved an R2 of 0.60 for both validation and independent testing, with RMSEs of 85.97 and 93.69 g m−2, respectively. When evaluated across all samples, the model exhibited an R2 of 0.93 and an RMSE of 51.85 g m−2. Comparison with other datasets showed its comparable value distributions and interannual trajectories, and closer agreement with field observations. This dataset provides a foundation for advancing research on global grassland carbon dynamics and management practices.