ReviewAoB PLANTS2023
Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications.
Review in AoB PLANTS, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- The case for an epiphyte area index.The New phytologist · 2026Article
- Spectral network analysis illuminates coordinated plant traits across a climate gradient.The New phytologist · 2026Article
- Magnetometry for Agriculture and Animal Systems: From Classical Sensors to Quantum-Enabled Biosensing.Biosensors · 2026Review
- Leaf to Root: Harnessing leaf spectral signatures for non-destructive monitoring of soybean nodule traits.Plant phenomics (Washington, D.C.) · 2026Article
- Pigment-Structure Leaf Syndromes in Poinsettia (ACS omega · 2026Article
- Genetic constraints on in-situ reflectance spectral variation in bermudagrass populations across Hainan Island.Plant phenomics (Washington, D.C.) · 2026Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Hyperspectral reflectance integrates key traits for predicting leaf metabolism.The New phytologist · 2025Article
- Proximal remote sensing: an essential tool for bridging the gap between high-resolution ecosystem monitoring and global ecology.The New phytologist · 2025Review
- Spectral response of gross primary production to in situ canopy light absorption coefficient of chlorophyll.Photosynthesis research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.