开放式土壤光谱库 (OSSL):通过开放式开发和社区参与,构建可重复的土壤校准模型.
José L Safanelli1, Tomislav Hengl2, Leandro L Parente2
1Woodwell Climate Research Center, Falmouth, MA, United States of America.
PloS one
|January 13, 2025
概括
开放式土壤光谱库 (OSSL) 为土壤光谱学提供了一个负担得起的,集中的资源,提高了机器学习模型的准确性. 中红外 (MIR) 光谱通常优于可见和近红外 (VisNIR) 或近红外 (NIR) 模型.
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 土壤光谱对于环境和农业监测至关重要.
- 机器学习 (ML) 模型需要大型参考数据集 (土壤光谱库,SSL).
- 数据多样性的有限性阻碍了对新土壤样本的预测准确性.
研究的目的:
- 建立开放式土壤光谱图书馆 (OSSL) 作为一个集中式的,开放式的资源.
- 描述OSSL的数据收集,协调和分析程序.
- 评估ML模型的性能,以利用光谱数据预测土壤特性.
主要方法:
- 收集并将多个SSL协调到OSSL中.
- 进行探索性数据分析和预测建模.
- 使用十倍交叉验证和独立模型评估.
主要成果:
- 中红外 (MIR) 模型显示的精度明显高于可见和近红外 (VisNIR) 或近红外 (NIR) 模型.
- 立体式ML算法展示了对校准和可靠输出交付的最佳性能.
- 预测的准确性各不相同,在所有光谱区域,总硫,可提取的和电导率表现不佳.
结论:
- OSSL促进了负担得起的土壤数据收集,并增强了ML模型的开发.
- 与VisNIR或NIR相比,MIR光谱为预测许多土壤特性提供了更高的准确性.
- 对于对某些土壤特性进行光谱预测存在局限性,这凸显了对综合方法的需求.
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