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耕地土壤碳封存潜力及其控制因素在中国

Shuai Wang¹, Li Xu², Kabindra Adhikari³

¹Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; College of Land and Environment, Shenyang Agricultural University, Shenyang, Liaoning Province 110866, China; Earth Critical Zone and Flux Research Station of Xing'an Mountains, Chinese Academy of Sciences, Daxing'anling 165200, China.

The Science of the total environment

|September 24, 2023

概括

中国中国中国中国.

科学领域:

土壤科学土壤科学
环境科学环境科学
气候变化缓解缓解气候变化缓解

背景情况:

土壤有机碳 (SOC) 对于减缓气候变化和减少排放至关重要.
在中国的耕地中缺乏关于SOC库存和封存潜力的空间显式数据.
影响和SOC水平 (SOC_S) 和土壤有机碳密度 (SOC_D) 的因素需要进一步了解.

研究的目的:

在中国大陆的耕地预测和绘制SOC_S和SOC_D.
根据SOC_S和SOC_D来计算土壤有机碳封存潜力 (SOC_P).
确定影响SOC空间分布的关键环境变量.

主要方法:

使用了991种表土 (0-20厘米) SOC测量和12种环境变量.
使用的增强回归树 (BRT),随机森林 (RF) 和支持向量机 (SVM) 模型.
随机森林模型在预测SOC_S和SOC_D方面表现出卓越的性能.

主要成果:

预计中国东北部的SOC_S和SOC_D将更高,中国中部的SOC_S和SOC_D将更低.
在中国的耕地显示出在顶部20厘米的约3.25 Pg的SOC被扣留的潜力.
中国东北部的SOC_P最高,其次是中国北部;中国西南部的SOC_P最低.

结论:

年平均温度,粘土含量和NDVI是空间SOC_S变化的主要驱动因素.
该研究提供了对中国耕地SOC_P的关键评估和空间地图.
研究结果为决策者和研究人员在制定减缓气候变化战略方面提供了宝贵的见解.

关键词:

人类遗传因素人类遗传因素碳封存是一种碳封存方式.耕地是可以耕种的土地.环境变量环境变量机器学习机器学习空间变化的空间变化.

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