Chloroplast to Leaf

Loreto, Francesco; Baker, Neil R.; Ort, Donald R.

doi:10.1007/0-387-27267-4_9

Cited by 11 publications

(13 citation statements)

References 86 publications

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“…According to Loreto et al, 8 when the RWC falls below 70%, irreversible damage of the foliar photosynthetic apparatus can result. More specifically, the RWC reduction is accompanied by an accentuated drop in pigment content.…”

Section: Introductionmentioning

confidence: 99%

“…9,10 This reduces light absorption efficiency, which may lead to the plant's death. 8,11 Moderate water stress (RWC > 70%), which is observed more often in nature, 8 may also prompt structural variations, such as tissue thickness reductions, 12 that can affect foliar optical properties and, consequently, the leaves' spectral signatures.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, in these experiments, maize leaves whose water content was moderately reduced using an in vivo procedure showed a decrease in the visible reflectance even though their pigment content remained relatively constant. 3 According to Loretto et al, 8 during slowly developing water stress (e.g., when plants sense the reduction of soil water content), water deficit signals are released in the live plant roots and delivered to the leaves. The main signaling route used in this process is mediated by abscisic acid (ABA).…”

Section: Introductionmentioning

confidence: 99%

“…8 Also, Ca 2þ channels are essential for the initiation of a large number of signal transduction processes in higher plant cells. 15 For example, experiments by Sato et al 16 indicate that the influx of external Ca 2þ through the plasma membrane is required for the early signaling step of chloroplast mechanorelocation movements.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and monitoring water stress states in maize crops using spectral ratios obtained in the photosynthetic domain

Baranoski

Leeuwen

2017

J. Appl. Remote Sens

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, "Detecting and monitoring water stress states in maize crops using spectral ratios obtained in the photosynthetic domain," J. Appl. Remote Sens. 11(3), 036025 (2017), doi: 10.1117/1.JRS.11.036025. Abstract. The reliable detection and monitoring of changes in the water status of crops composed of plants like maize, a highly adaptable C4 species in large demand for both food and biofuel production, are longstanding remote sensing goals. Existing procedures employed to achieve these goals rely predominantly on the spectral signatures of plant leaves in the infrared domain where the light absorption within the foliar tissues is dominated by water. It has been suggested that such procedures could be implemented using subsurface reflectance to transmittance ratios obtained in the visible (photosynthetic) domain with the assistance of polarization devices. However, the experiments leading to this proposition were performed on detached maize leaves, which were not influenced by the whole (living) plant's adaptation mechanisms to water stress. In this work, we employ predictive simulations of light-leaf interactions in the photosynthetic domain to demonstrate that the living specimens' physiological responses to dehydration stress should be taken into account in this context. Our findings also indicate that a reflectance to transmittance ratio obtained in the photosynthetic domain at a lower angle of incidence without the use of polarization devices may represent a cost-effective alternative for the assessment of water stress states in maize crops. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting and monitoring water stress states in maize crops using spectral ratios obtained in the photosynthetic domain

Baranoski

Leeuwen

2017

J. Appl. Remote Sens

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“…However, the spectral data used in studies involving the reduction of foliar water content, particularly Hyperspectral Modeling of Material Appearance: General Framework, Challenges and Prospects in the more conspicuous moderate range (reduced water content > 70%) [77], is normally obtained under in vitro conditions. In these situations, the specimens (leaves) are detached from a living maize plant and usually air dried [26,113].…”

Section: Casementioning

confidence: 99%

Hyperspectral Modeling of Material Appearance: General Framework, Challenges and Prospects

Baranoski

2015

RITA

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Abstract:The main purpose of this tutorial is to address theoretical and practical issues involved in the development of predictive material appearance models for interdisciplinary applications within and outside the visible spectral domain. We examine the specific constraints and pitfalls found in each of the key stages of the model development framework, namely data collection, design and evaluation, and discuss alternatives to enhance the effectiveness of the entire process. Although predictive material appearance models developed by computer graphics researchers are usually aimed at realistic image synthesis applications, they also provide valuable support for a myriad of advanced investigations in related areas, such as computer vision, image processing and pattern recognition, which rely on the accurate analysis and interpretation of material appearance attributes in the hyperspectral domain. In fact, their scope of contributions goes beyond the realm of traditional computer science applications. For example, predictive light transport simulations, which are essential for the development of these models, are also regularly being used by physical and life science researchers to understand and predict material appearance changes prompted by mechanisms which cannot be fully studied using standard "wet" experimental procedures. For completeness, this tutorial also provides an overview of such synergistic research efforts and in silico investigations, which are illustrated by case studies involving the use of hyperspectral material appearance models.

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