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<p>Accurately quantifying canopy height and above-ground carbon across diverse land-cover types is crucial for understanding carbon storage dynamics and guiding climate-mitigation strategies. Yet existing maps often overlook non-forest ecosystems. Here we present a deep learning framework based on a U-Net architecture that combines radar, optical, elevation and slope data to produce a 10 m canopy height map across China. The model is trained with laser measurements from NASA’s GEDI mission validated using unmanned aerial vehicle lidar data (<i>MAE</i> = <i>2.39 m</i>). We then estimate the above-ground biomass and carbon from these heights using a Random Forest model (<i>MAE</i> = <i>37.71 Mg ha-1</i>). By deriving carbon from canopy height, we take advantage of U-Net’s ability to capture trees in non-forest ecosystems such as croplands, grasslands and urban areas. Our nationwide 30 m carbon map reveals that trees outside forests contribute 20.8-32.9% of China’s above-ground carbon in 2019 (3.62-5.72 Pg C), underscoring their importance.</p>

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Substantial contribution of trees outside forests to above-ground carbon across China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

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  • Yang Su,
  • Tianqi Shi,
  • Xianglin Zhang,
  • Yidi Xu,
  • Kai Cheng,
  • Siyu Liu,
  • Ge Han,
  • Xin Ma,
  • Songchao Chen,
  • Xiaowei Tong,
  • Wei Li,
  • Wei Gong,
  • Qinghua Guo,
  • Martin Brandt,
  • Shilong Piao,
  • Alexandre d’Aspremont,
  • Philippe Ciais

摘要

Accurately quantifying canopy height and above-ground carbon across diverse land-cover types is crucial for understanding carbon storage dynamics and guiding climate-mitigation strategies. Yet existing maps often overlook non-forest ecosystems. Here we present a deep learning framework based on a U-Net architecture that combines radar, optical, elevation and slope data to produce a 10 m canopy height map across China. The model is trained with laser measurements from NASA’s GEDI mission validated using unmanned aerial vehicle lidar data (MAE = 2.39 m). We then estimate the above-ground biomass and carbon from these heights using a Random Forest model (MAE = 37.71 Mg ha-1). By deriving carbon from canopy height, we take advantage of U-Net’s ability to capture trees in non-forest ecosystems such as croplands, grasslands and urban areas. Our nationwide 30 m carbon map reveals that trees outside forests contribute 20.8-32.9% of China’s above-ground carbon in 2019 (3.62-5.72 Pg C), underscoring their importance.