土壤有机物质的光谱可预测性取决于其分数,而不是光谱融合
Zhi Zhang1,2, Meihua Yang1,3, Asim Biswas3
1Department of Environmental Engineering, Yuzhang Normal University, Nanchang 330103, China.
使用光谱学预测土壤有机物质 (SOM) 是由人类分数驱动的. 可见近红外和中红外光谱学准确地估计了SOM和Humin,但不是酸.
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 环境化学环境化学
背景情况:
- 土壤有机物 (SOM) 对土壤健康至关重要,影响碳储存和营养循环.
- 了解哪些SOM分数有助于光谱可预测性对于准确的土壤监测至关重要.
- 目前的光谱方法往往缺乏关于SOM组件的特异性.
研究的目的:
- 为了比较可见近红外 (VIS-NIR),中红外 (MIR) 和合光谱,用于预测SOM及其组成部分 (酸,酸,胺).
- 为了识别特定的SOM分数驱动光谱可预测性.
- 评估光谱聚变对提高SOM预测准确性的有效性.
主要方法:
- 分析了中国东南部亚热带农田的93个土壤样本.
- 应用部分最小平方回归 (PLSR) 用全光谱和LASSO选择的波长.
- 对SOM及其分数的Vis-NIR,MIR和融合光谱数据的预测性能进行比较.
主要成果:
- 视NIR和MIR光谱显示SOM和人体 (R2 = 0.790.90) 的中度到强度预测.
- 由于弱光谱特征,酸 (FA) 的预测很差 (R2 < 0.24).
- 光谱融合并没有改善预测,可能是由于冗余信息和规模不平衡.
结论:
- 人类分数是使用光谱学准确预测SOM的主要驱动因素.
- 对土壤碳的光谱监测需要特定组件的建模,专注于可预测的分数,如人类.
- 进一步的研究应该探索方法,以改善可预测性较低的SOM组件,如FA.FA的光谱预测.
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