使用CWT与敏感带相结合,估计树叶相对含水量
Xiangqian Qi1, Yanfang Li1, Shiqing Dou2
1School of Resource Engineering, Longyan University, Longyan 364012, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究增强了使用连续波波变换 (CWT) 和连续投影算法 (SPA) 的叶湿度监测. CWT-SPA融合模型准确地预测了相对含水量 (RWC),改善了水资源管理和作物产量.
科学领域:
- 农业遥感 农业遥感
- 植物生理学 植物生理学
- 频谱学是一种光谱学.
背景情况:
- 精确监测叶相对含水量 (RWC) 对于优化灌,提高水果质量和提高产量至关重要.
- 超频谱技术为RWC监控提供了潜力,但数据噪声和光谱别名等挑战降低了准确性.
- 叶绿素含量 (LCC) 也影响光谱数据,可能影响RWC估计.
研究的目的:
- 使用超光谱数据开发叶RWC的最佳预测模型.
- 在RWC量化中解决噪声和光谱别名问题.
- 为了评估连续波量变换 (CWT) 对RWC逆转的有效性.
主要方法:
- 利用树叶的高光谱数据 (401-2400 nm) 来进行分析.
- 应用连续波波变换 (CWT) 进行多尺度分解,以提取抗噪特征.
- 采用皮尔森相关性分析和连续预测算法 (SPA) 来选择RWC和LCC的最佳敏感频段.
- 根据选定的特征开发了一种部分最小平方回归 (PLSR) 模型.
主要成果:
- 与原始光谱相比,CWT预处理显著提高了RWC和LCC估计准确度 (最大改善:6%和3%).
- 使用CWT-SPA组合模型的反转精度高于使用原始光谱的模型.
- 最优的CWT-SPA融合模型,集成RWC Scale7和LCC Scale5-2224/2308特征,实现了R2 = 0.756和RMSE = 0.0214.8的结果.
结论:
- CWT预处理增强了树叶对RWC和LCC的光谱敏感性.
- CWT-SPA融合方法有效地减少了光谱噪声和对线性,提高了RWC预测的准确性.
- 整合RWC和LCC光谱特征的多尺度特征,为叶湿度监测提供了一个强大而稳定的模型.
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