使用叶子水平的超光谱数据估计针树针的含水量
Yuan Zhang1, Anzhi Wang1, Jiaxin Li1,2
1CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China.
Frontiers in plant science
|September 23, 2024
概括
针树针以不同的速度失去水,近红外和短波红外区域的水分发生显著变化. 超光谱数据和部分最小平方回归有效地估计了不同形态的针树的针头含水量.
科学领域:
- 植物生理学 植物生理学
- 遥感是一种远程传感.
- 林业林业 林业 林业 林业
背景情况:
- 植物的水含量对于生长和压力监测至关重要.
- 准确的估计有助于理解植被健康.
- 针叶树表现出不同的针形态,影响水的动态.
研究的目的:
- 为三个针叶树种构建水损失曲线.
- 评估常见的水指数,用于树针的水含量估计.
- 探索超光谱数据,以估计不同针形态的针叶树的叶子水含量.
主要方法:
- 对于三种针叶树种类,生成了水损失曲线.
- 评估了12个水指数的适用性.
- 部分最小平方回归 (PLSR) 建模使用超谱数据.
主要成果:
- 与中国树和韩国松树相比,奥尔干树的水损失率显著更高.
- 反射变化在近红外 (NIR) 和短波红外 (SWIR) 区域最为明显.
- 确定SWIR带对针树针水含量最敏感.
- 水指数对单一物种有效,但不适用于所有物种.
- PLSR模型准确地估计了所有测试的针叶树形态中的水含量.
结论:
- 超光谱数据与PLSR相结合,是一种可靠的方法来估计针树的水含量.
- 这种方法有望监测不同类型的针叶树的植被水状况.
- 了解特定物种的水损失动态对于有效的植被管理至关重要.
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