支持农业用地自愿碳市场计划的数字土壤绘图
James R Kellner1,2,3, Christian Clanton1, Kirk M Demuth1
1Perennial Climate Inc., Boulder, Colorado, United States of America.
PloS one
|September 2, 2025
概括
准确地测量土壤有机碳对农业碳市场至关重要. 一个新的数字土壤绘图框架使用机器学习和遥感来提供可扩展的,准确的美国农田SOC预测.
科学领域:
- 农业科学
- 环境科学
- 数据科学
背景情况:
- 农业的自愿碳市场 (VCM) 计划需要准确,可扩展的土壤有机碳 (SOC) 测量.
- 目前存在的障碍阻碍了SOC量化技术的广泛采用.
- 现有的公共SOC数据产品在准确反映现场测量方面存在局限性.
研究的目的:
- 开发和验证数字土壤绘图 (DSM) 框架,以准确,大规模预测农田的SOC.
- 通过提高 SOC 测量效率和准确性来克服 VCM 计划的采用障碍.
- 将开发的DSM框架与现有的公共SOC数据产品的性能进行比较.
主要方法:
- 开发了一个基于机器学习的DSM框架,使用空间共变量 (气候,地形,土壤特性,遥感).
- 在47个相邻的美国州使用了5,230个SOC测量,用于模型培训和验证.
- 使用交叉验证 (R2 = 0.811,RMSE = 0.041) 评估模型性能,并将预测与独立的现场测量和现有 SOC 数据产品进行比较.
主要成果:
- DSM框架准确地预测了土壤顶部30厘米的SOC含量,预测值和测量值之间有很强的一致性 (R2 = 0.811).
- 现有的公共SOC数据产品在复制独立现场测量时表现不佳.
- 据悉,Sentinel-2的遥感数据,温度和水文特征是SOC含量的关键预测因素.
结论:
- 数据驱动的算法和特征工程可以产生准确的,现场水平的农田SOC估计,克服VCM规模障碍.
- 在地理上具有代表性的培训和验证数据对于可靠的SOC量化至关重要.
- 开发的DSM框架为改善农业VCM中的SOC监测提供了可扩展的解决方案.
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