用可见,近红外和中红外光谱学预测土壤盐度和度指标的光谱预测
Leila Lotfollahi1, Mohammad Amir Delavar1, Asim Biswas2
1Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.
Journal of environmental management
|August 30, 2023
概括
土壤盐度和度指标是用光谱学在伊朗建模的. 中红外光谱通常优于可见和近红外光谱,基于记忆的学习模型在预测土壤特性 (如可交换百分比) 上显示出高准确度.
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 环境科学 环境科学
背景情况:
- 气候变化和干旱在全球范围内增加了土壤的盐度和度,特别是在像伊朗查哈多利平原这样的地区.
- 对土壤盐度和度指标的准确建模对于受影响地区的土地管理至关重要.
- 关于使用各种光谱范围来评估这些土壤属性的研究有限.
研究的目的:
- 模拟土壤盐度和度指标,包括吸附比率 (SAR),可交换比率 (ESR),可交换百分比 (ESP),pH和电导率 (EC).
- 为了比较中红外 (中红外) 和可见到近红外 (VIS-NIR) 光谱的有效性来预测这些土壤特性.
- 评估五种不同的建模技术的性能:PLSR,BGPLSR,MBL,RF和Cubist.
主要方法:
- 利用中红外和近红外光谱来收集查哈多利平原土壤的光谱数据.
- 使用五种机器学习模型 (PLSR,BgPLSR,MBL,RF,Cubist) 来估计土壤特性 (SAR,ESR,ESP,pH,EC).
- 使用R平方 (R2) 和性能与偏差比率 (RPD) 的指标评估模型性能.
主要成果:
- 中红外光谱通常提供比近红外光谱更准确的预测,除了pH.
- 基于记忆的学习 (MBL) 模型在预测SAR (RPD=2.70),ESR (RPD=4.36) 和ESP (RPD=4.41) 方面表现出卓越的性能.
- 对于pH,使用对比NIR光谱的BgPLSR模型获得了最高的RPD (2.56),而PLSR对EC最好 (RPD=2.64).
结论:
- 基于光谱学的预测是评估盐度和度指标的土壤测量人员的宝贵工具.
- MBL模型对于预测土壤特性特别有效,尤其是在有限的数据集下.
- 建议将光谱学整合到半干旱和干旱地区的土壤测绘中,以改善土地管理.
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