Wong Christopher Y S
Department of Plant Sciences, University of California, Davis, Davis, CA, USA.
AoB Plants. 2023 Jul 6;15(4):plad039. doi: 10.1093/aobpla/plad039. eCollection 2023 Jul.
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
基于光学的遥感技术在对一系列应用（包括植被监测与评估）中的植被特征和功能进行表型分析方面具有巨大潜力。基于光学方法的一个关键优势在于其与影响光谱信号的植被生理学、生物化学及结构之间存在潜在的机制联系。通过利用由植物对环境的生理响应所驱动的光谱变化，遥感产品可用于估算植被特征和功能。然而，这些产品通常是基于协方差的代理指标，在某些情况下可能导致误解和解耦。本观点将讨论：（i）植被的光学特性；（ii）植被指数、太阳诱导荧光及机器学习方法的应用；（iii）协方差如何能实现对植物特征和功能的良好经验近似。随着遥感数据的可用性和应用不断增加，必须考虑理解和认识植物光学的潜在机制基础。这样做将有助于在表型分析应用中恰当地应用基于光学的遥感技术并考虑其局限性。
