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土壤数据的最新性:土壤数据跨时间协调的基础

Tegbaru B Gobezie¹, Stacey D Scott², Prasad Daggupati³

¹School of Environmental Sciences, University of Guelph, 50 Stone Road East, N1G 2W1, Guelph, ON, Canada.

Journal of environmental management

|June 15, 2024

概括

协调传统的土壤数据对于可持续管理至关重要. 这项研究开发了一个使用机器学习整合土壤数据的框架,表明地形共变量是数据质量的关键预测因素.

科学领域:

土壤科学土壤科学
地理空间分析是什么
数据协调与统一

背景情况:

可靠的土壤信息对于可持续的土壤资源管理至关重要.
协调不同时间收集的土壤数据 (遗留数据) 是一个未被充分探索的挑战.
将时间维度整合到时空土壤研究中是必要的.

研究的目的:

为协调不同时期的土壤数据制定一个全面的框架.
通过使用土壤数据近期分析,评估历史和近期土壤数据的整合.
引入和应用"数据年龄"属性,用于时间土壤数据的协调.

主要方法:

应用了三个机器学习模型:决策树 (DT),随机森林 (RF) 和梯度提升 (GBM).
利用了6339个地点和28149个深度协调土壤层的数据集.
引入了一个"数据年龄"属性,计算调查年与现在之间的时间差异.

主要成果:

机器学习模型在预测土壤数据近期性方面表现强.
随机森林 (RF) 获得了最高的精度 (R-平方: 0.99,RMSE: 1.41,对应度: 0.97).
在预测土壤数据的最新性方面,地形衍生的环境共变量比土地利用和土地覆盖 (LULC) 变化更有影响力.

结论:

地形衍生的协变量,特别是海拔因素,有效地解释了较旧的土壤数据的质量.
土壤数据近期概念可以从历史数据中提升对土壤当前属性的预测.
这种方法可以改进实时估计 (例如碳预算),对全球地球系统模型至关重要.

关键词:

土壤年龄数据土壤年龄数据数字土壤绘制地图遥感是一种远程传感.可持续性可持续性是一个问题.时间协调与时间协调.

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