Using remotely sensed spectral reflectance to indicate leaf photosynthetic efficiency derived from active fluorescence measurements

Yi, Peng; Zeng, Aoli; Zhu, Ting’e; Fang, Shenghui; Gong, Yi; Tao, Yanqi; Zhou, Yayun; Liu, Kan

doi:10.1117/1.jrs.11.026034

Cited by 20 publications

(19 citation statements)

References 69 publications

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“…The single ratio (SR) and the curvature index (CUR) were highly correlated with F V /F M (Jia et al, 2016). Peng et al (2017) concluded that normalized difference vegetation index (NDVI) correlated with F V /F M only in non-stressed plants. In contrast, other authors reported no significant correlation between NDVI and Chl-F parameters (Jia et al, 2016).…”

Section: Biotic Stress Detection In High-throughput Platforms and At mentioning

confidence: 99%

Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging

2019

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Photosynthesis is a pivotal process in plant physiology, and its regulation plays an important role in plant defense against biotic stress. Interactions with pathogens and pests often cause alterations in the metabolism of sugars and sink/source relationships. These changes can be part of the plant defense mechanisms to limit nutrient availability to the pathogens. In other cases, these alterations can be the result of pests manipulating the plant metabolism for their own benefit. The effects of biotic stress on plant physiology are typically heterogeneous, both spatially and temporarily. Chlorophyll fluorescence imaging is a powerful tool to mine the activity of photosynthesis at cellular, leaf, and whole-plant scale, allowing the phenotyping of plants. This review will recapitulate the responses of the photosynthetic machinery to biotic stress factors, from pathogens (viruses, bacteria, and fungi) to pests (herbivory) analyzed by chlorophyll fluorescence imaging both at the lab and field scale. Moreover, chlorophyll fluorescence imagers and alternative techniques to indirectly evaluate photosynthetic traits used at field scale are also revised.

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Section: Biotic Stress Detection In High-throughput Platforms and At mentioning

confidence: 99%

Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging

2019

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“…At the next stage of the work, correlation analysis was performed comparing particular VIs derived from satellite images and those ChlF values measured on the ground in order to find if a significant relationship existed between these two parameters. Assessments of Fv/Fm from VIs derived from remote sensing data have been reported by several studies (Tan et al 2012;Peng et al 2017;Kycko et al 2019a;Kycko et al 2019b;Wei et al 2019;Zagajewski et al, 2017;Zagajewski et al 2018). For instance, some researchers compared the results of VIs to assess the Fv/Fm of maize, and reached the conclusion that out of the various kinds of VI that had a close relationship with Fv/Fm, SIPI performed best (Tan et al, 2012).…”

Section: Resultsmentioning

confidence: 94%

“…Kycko et al (2019a) and Zagajewski et al (2018) used in-situ hyperspectral remote sensing as an important technique to fulfill real-time monitoring of plant condition based on its superior performance in acquiring vegetation canopy information rapidly and non-destructively. In studies conducted by Peng et. al.…”

Section: Resultsmentioning

confidence: 99%

Remote sensing techniques to assess chlorophyll fluorescence in support of crop monitoring in Poland

Gurdak

Bartold

2021

Miscellanea Geographica

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The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=-0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.

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“…The chlorophyll contents and LAI values did not show much difference between the stressed and unstressed plants during the early phase of water stress. Zygielbaum et al () also observed that the water stress during the first 4 days in the maize crop did not change chlorophyll contents, which they attributed to photoprotective capacity of a plant such as chloroplast avoidance mechanism (Peng et al, ; Yamada et al, ; Zygielbaum et al, ). In contrast, the differences in all the physiological parameters (LAI, RWC, and EL) between stressed and unstressed plants continued to increase with the increase in the intensity of stress (as SWP decreased from about −57.4 kPa on 71 DAS to −130.7 KPa on 84 DAS in T 5 ) similar to the widening gaps between stressed and unstressed canopy reflectance values (Figures a–d).…”

Section: Resultsmentioning

confidence: 98%

Canopy Spectral Reflectance as a Predictor of Soil Water Potential in Rice

Panigrahi

Das

2018

Water Resources Research

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Soil water potential (SWP) is a key parameter for characterizing water stress. Typically, a tensiometer is used to measure SWP. However, the measurement range for commercially available tensiometers is limited to −90 kPa and a tensiometer can only provide estimate of SWP at a single location. In this study, a new approach was developed for estimating SWP from spectral reflectance data of a standing rice crop over the visible to shortwave‐infrared region (wavelength: 350–2,500 nm). Five water stress treatments corresponding to targeted SWP of −30, −50, −70, −120, and −140 kPa were examined by withholding irrigation during the vegetative growth stage of three rice varieties. Tensiometers and mechanistic water flow model were used for monitoring SWP. Spectral models for SWP were developed using partial‐least‐squares regression (PLSR), support vector regression (SVR), and coupled PLSR and feature selection (PLSRFS) approaches. Results showed that the SVR approach was the best model for estimating SWP from spectral reflectance data with the coefficient of determination values of 0.71 and 0.55 for the calibration and validation data sets, respectively. Observed root‐mean‐squared residuals for the predicted SWPs were in the range of −7 to −19 kPa. A new spectral water stress index was also developed using the reflectance values at 745 and 2,002 nm, which showed strong correlation with relative water contents and electrolyte leakage. This new approach is rapid and noninvasive and may be used for estimating SWP over large areas.

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Using remotely sensed spectral reflectance to indicate leaf photosynthetic efficiency derived from active fluorescence measurements

Cited by 20 publications

References 69 publications

Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging

Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging

Remote sensing techniques to assess chlorophyll fluorescence in support of crop monitoring in Poland

Canopy Spectral Reflectance as a Predictor of Soil Water Potential in Rice

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