在干旱压力下的保罗尼亚树叶中含含量的超光谱预测模型
Yamei Zhang1, Guangxin Ru1, Zhenli Zhao1
1College of Forestry, Henan Agricultural University, Zhengzhou 450046, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究开发了一种最佳模型,用于使用光谱分析预测在干旱压力下保罗维尼亚叶中的叶绿素含量. 相关性分析 (水带) 部分最小平方 (CA(W) -PLS) 模型被证明是最有效的,考虑到光谱转换,水带和叶子位置.
科学领域:
- 植物生理学 植物生理学
- 遥感 遥感 遥感 遥感
- 农业科学 农业科学
背景情况:
- 干旱压力显著影响植物健康和生理参数,包括叶绿素含量.
- 需要准确的,非破坏性的方法来评估农作物的叶绿素含量,如Paulownia幼苗.
- 光谱分析为植物生化特征的定量逆转提供了一个有希望的方法.
研究的目的:
- 在干旱压力下探索Paulownia苗木叶子中叶绿素含量的定量反转模型.
- 确定影响叶绿素含量的因素,并确定最佳预测模型.
- 评估光谱预处理,水带和叶子位置对模型准确性的影响.
主要方法:
- 在四个干旱压力级别的paulownia苗木上进行了盆栽水控制实验.
- 使用四种部分最小平方 (PLS) 建模方法分析了23种光谱变换:全频段PLS (AB-PLS),主要组件分析PLS (PCA-PLS),相关性分析PLS (CA-PLS) 和相关性分析 (水带) PLS (CA(W) -PLS).
- 进行了植被指数建模和对叶绿素和水的敏感带的分析.
主要成果:
- 通过不同的叶子位置观察到光谱反射的显著差异.
- 叶绿素敏感带接近550nm,而水敏感带接近1440和1920nm.
- CA(W) -PLS模型表现出卓越的准确性和稳定性,通过结合水带和考虑叶子位置变化,优于其他方法. 第一个衍生谱 (R') 和反向日志谱 (log(1/R)) 是特定模型的最佳预处理方法.
结论:
- 使用23种光谱变换,考虑水带和叶子位置的CA(W) -PLS模型是在干旱压力下对Paulownia苗木的叶绿素含量定量逆转的最佳方法.
- 在使用特征带进行建模时,使用水带显著提高了交叉验证的准确性.
- 叶子的位置,特别是第一对顶叶的位置,对预测准确性产生了负面影响,突出了最佳模型解决的局限性.
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