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基于两点机器学习方法的土壤有机碳含量逆转

Chenyi Wang¹, Bingbo Gao¹, Ke Yang²

¹College of Land Science and Technology, China Agricultural University, Beijing 100193, China; Key Laboratory of Remote Sensing of Agricultural Disasters, Ministry of Agriculture and Rural Affairs, Beijing 100193, China.

The Science of the total environment

|June 7, 2024

概括

两点机器学习 (TPML) 方法使用遥感数据准确估计土壤有机碳 (SOC). 这种方法克服了传统方法的局限性,为可持续的土地管理提供了高精度的大规模SOC监测.

科学领域:

地球和环境科学地球和环境科学
遥感遥感遥感遥感
土壤科学土壤科学

背景情况:

土壤有机碳 (SOC) 对全球碳循环和可持续发展至关重要.
遥感为SOC监测提供了一种方便的方法,但面临着空间异质性和有限的地面数据的挑战.
由于环境影响,现有的方法在SOC和光谱数据之间的复杂空间关系中扎.

研究的目的:

引入和评估用于高精度SOC逆转的两点机器学习 (TPML) 方法.
解决当前SOC监测的局限性,特别是复杂的空间关系和稀缺的地面样本.
为大规模SOC估计提供可靠的方法,以不确定性评估.

主要方法:

应用了两点机器学习 (TPML) 方法来逆转海县的SOC内容.
利用来自Sentinel-1,Sentinel-2,地形和环境数据的衍生变量.
使用十倍交叉验证和t测试来评估准确性并与其他机器学习模型进行比较.

主要成果:

与随机森林,梯度增强,PLS和SVM相比,TPML显示出更高的反转精度 (平均r=0.854,MAE=0.384%,RMSE=0.558%,RPD=1.918).与随机森林,梯度增强,PLS和SVM相比,TPML显示出更高的反转精度 (平均r=0.854,MAE=0.384%,RMSE=0.558%,RPD=1.918).
通过比较实际和理论错误,TPML有效地评估了反转不确定性.
与其他模型相比,生成了10米分辨率的SOC地图,细节更流,土地使用一致性更好.

结论:

使用遥感数据,TPML方法对准确且大规模的SOC逆转非常有效.
在处理空间异质性和提供不确定性估计方面,TPML提供了显著的优势.
本研究为利用TPML进行土壤属性预测和低成本,高精度的SOC监测提供了指导.

关键词:

定量遥感模型的数量遥感模型.哨兵一号 - 哨兵一号哨兵-2 卫星 - 卫星-2 哨兵-2 卫星土壤有机碳土壤有机碳两点机器学习的机器学习.

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