What to Choose for Estimating Leaf Water Status-Spectral Reflectance or In vivo Chlorophyll Fluorescence?

. 2024 ; 6 () : 0243. [epub] 20240829

Status PubMed-not-MEDLINE Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid39211292

In the context of global climate change and the increasing need to study plant response to drought, there is a demand for easily, rapidly, and remotely measurable parameters that sensitively reflect leaf water status. Parameters with this potential include those derived from leaf spectral reflectance (R) and chlorophyll fluorescence. As each of these methods probes completely different leaf characteristics, their sensitivity to water loss may differ in different plant species and/or under different circumstances, making it difficult to choose the most appropriate method for estimating water status in a given situation. Here, we present a simple comparative analysis to facilitate this choice for leaf-level measurements. Using desiccation of tobacco (Nicotiana tabacum L. cv. Samsun) and barley (Hordeum vulgare L. cv. Bojos) leaves as a model case, we measured parameters of spectral R and chlorophyll fluorescence and then evaluated and compared their applicability by means of introduced coefficients (coefficient of reliability, sensitivity, and inaccuracy). This comparison showed that, in our case, chlorophyll fluorescence was more reliable and universal than spectral R. Nevertheless, it is most appropriate to use both methods simultaneously, as the specific ranking of their parameters according to the coefficient of reliability may indicate a specific scenario of changes in desiccating leaves.

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Marchetti CF, Ugena L, Humplík JF, Polák M, Ćavar Zeljković S, Podlešáková K, Fürst T, De Diego N, Spíchal L. A novel image-based screening method to study water-deficit response and recovery of barley populations using canopy dynamics phenotyping and simple metabolite profiling. Front Plant Sci. 2019;10:445018. PubMed PMC

Cao J, An Q, Zhang X, Xu S, Si T, Niyogi D. Is satellite Sun-induced chlorophyll fluorescence more indicative than vegetation indices under drought condition? Sci Total Environ. 2021;792:148396. PubMed

Quemada C, Pérez-Escudero JM, Gonzalo R, Ederra I, Santesteban LG, Torres N, Iriarte JC. Remote sensing for plant water content monitoring: A review. Remote Sens. 2021;13(11):2088.

Zendonadi dos Santos N, Piepho HP, Condorelli GE, Licieri Groli E, Newcomb M, Ward R, Tuberosa R, Maccaferri M, Fiorani F, Rascher U, et al. . High-throughput field phenotyping reveals genetic variation in photosynthetic traits in durum wheat under drought. Plant Cell Environ. 2021;44(9):2858–2878. PubMed

Junttila S, Hölttä T, Saarinen N, Kankare V, Yrttimaa T, Hyyppä J, Vastaranta M. Close-range hyperspectral spectroscopy reveals leaf water content dynamics. Remote Sens Environ. 2022;277:113071.

Carter GA. Primary and secondary effects of water content on the spectral reflectance of leaves. Am J Bot. 1991;78(7):916–924.

Vergara-Díaz O, Chairi F, Vicente R, Fernandez-Gallego JA, Nieto-Taladriz MT, Aparicio N, Kefauver SC, Araus JL. Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes. J Exp Bot. 2018;69(12):3081–3094. PubMed PMC

Knipling EB. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ. 1970;1(3):155–159.

Caturegli L, Matteoli S, Gaetani M, Grossi N, Magni S, Minelli A, Corsini G, Remorini D, Volterrani M. Effects of water stress on spectral reflectance of bermudagrass. Sci Rep. 2020;10(1):1–12. PubMed PMC

Woolley JT. Reflectance and transmittance of light by leaves. Plant Physiol. 1971;47(5):656–662. PubMed PMC

Peñuelas J, Pinol J, Ogaya R, Filella I. Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int J Remote Sens. 1997;18(13):2869–2875.

Peñuelas J, Munné-Bosch S, Llusià J, Filella I. Leaf reflectance and photo- and antioxidant protection in field-grown summer-stressed Phillyrea angustifolia. Optical signals of oxidative stress? New Phytol. 2004;162(1):115–124.

Seelig HD, Hoehn A, Stodieck LS, Klaus DM, Adams WW, Emery WJ. The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared. Int J Remote Sens. 2008;29(13):3701–3713.

Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Grégoire JM. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens Environ. 2001;77(1):22–33.

Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sens Environ. 2002;82(2–3):188–197.

Rapaport T, Hochberg U, Rachmilevitch S, Karnieli A. The effect of differential growth rates across plants on spectral predictions of physiological parameters. PLOS ONE. 2014;9(2): Article e88930. PubMed PMC

Elsayed S, Mistele B, Schmidhalter U. Can changes in leaf water potential be assessed spectrally? Funct Plant Biol. 2011;38(6):523. PubMed

Falcioni R, Moriwaki T, Bonato CM, Souza LA, Nanni MR, Antunes WC. Distinct growth light and gibberellin regimes alter leaf anatomy and reveal their influence on leaf optical properties. Environ Exp Bot. 2017;140:86–95.

Lukeš P, Neuwirthová E, Lhotáková Z, Janoutová R, Albrechtová J. Upscaling seasonal phenological course of leaf dorsiventral reflectance in radiative transfer model. Remote Sens Environ. 2020;246:111862.

Wong CYS, Gilbert ME, Pierce MA, Parker TA, Palkovic A, Gepts P, Magney TS, Buckley TN. Hyperspectral remote sensing for phenotyping the physiological drought response of common and tepary bean. Plant Phenomics. 2023;5:1–11. PubMed PMC

Kovar M, Brestic M, Sytar O, Barek V, Hauptvogel P, Zivcak M. Evaluation of hyperspectral reflectance parameters to assess the leaf water content in soybean. Water. 2019;11(3):443.

Lee JE, Frankenberg C, Van Der Tol C, Berry JA, Guanter L, Boyce CK, Fisher JB, Morrow E, Worden JR, Asefi S, et al. . Forest productivity and water stress in Amazonia: Observations from GOSAT chlorophyll fluorescence. Proc R Soc B. 2013;280(1761):0171. PubMed PMC

Matoušková M, Bartošková H, Nauš J, Novotný R. Reaction of photosynthetic apparatus to dark desiccation sensitively detected by the induction of chlorophyll fluorescence quenching. J Plant Physiol. 1999;155(3):399–406.

Skotnica J, Matoušková M, Nauš J, Lazár D, Dvořák L. Thermoluminescence and fluorescence study of changes in photosystem II photochemistry in desiccating barley leaves. Photosynth Res. 2000;65(1):29–40. PubMed

Brestic M, Zivcak M. PSII fluorescence techniques for measurement of drought and high temperature stress signal in crop plants: Protocols and applications. In: Rout G, Das A. editors. Molecular stress physiology of plants. India: Springer; 2013. p. 87–131.

Stirbet A, Lazár D, Kromdijk J, Govindjee. Chlorophyll a fluorescence induction: Can just a one-second measurement be used to quantify abiotic stress responses? Photosynthetica. 2018;56(1):86–104.

Lazarević B, Šatović Z, Nimac A, Vidak M, Gunjača J, Politeo O, Carović-Stanko K. Application of phenotyping methods in detection of drought and salinity stress in basil (Ocimum basilicum L.). Front Plant Sci. 2021;12:629441. PubMed PMC

Hu C, Elias E, Nawrocki WJ, Croce R. Drought affects both photosystems in Arabidopsis thaliana. New Phytol. 2023;240(2):663–675. PubMed

Chen J, Guo Y, Tan J. Dynamic analysis of chlorophyll a fluorescence in response to time-variant excitations during strong actinic illumination and application in probing plant water loss. Plant Phenomics. 2024;6(1):0151. PubMed PMC

Lin J, Zhou L, Wu J. Exploring physiological and nonphysiological responses of sun-induced chlorophyll fluorescence to different levels of water stress in winter wheat. IEEE J Sel Top Appl Earth Observ Remote Sens. 2024;17:5107–5120.

Zhang Y, Cai M, Xiao X, Yang X, Migliavacca M, Basara J, Zhou S, Deng Y. Immediate and lagged vegetation responses to dry spells revealed by continuous solar-induced chlorophyll fluorescence observations in a tall-grass prairie. Remote Sens Environ. 2024;305:114080.

Yang P, Tol C, Verhoef W, Damm A, Schickling A, Kraska T, Muller O, Rascher U. Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence. Remote Sens Environ. 2019;231:110996.

Zeng Y, Hao D, Huete A, Dechant B, Berry J, Chen JM, Joiner J, Frankenberg C, Bond-Lamberty B, Ryu Y, et al. . Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat Rev Earth Environ. 2022;3(7):477–493.

Thomas JR, Namken LN, Oerther GF, Brown RG. Estimating leaf water content by reflectance measurements. Agron J. 1971;63(6):845–847.

Eitel JUH, Gessler PE, Smith AMS, Robberecht R. Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For Ecol Manag. 2006;229(1–3):170–182.

Barták M, Hájek J, Morkusová J, Skácelová K, Košuthová A. Dehydration-induced changes in spectral reflectance indices and chlorophyll fluorescence of Antarctic lichens with different thallus color, and intrathalline photobiont. Act Physiol Plant. 2018;40(10):3.

Orekhova A, Barták M, Hájek J, Morkusová J. Species-specific responses of spectral reflectance and the photosynthetic characteristics in two selected Antarctic mosses to thallus desiccation. Acta Physiol Plant. 2022;44(1):6.

Nauš J, Prokopová J, Řebíček J, Špundová M. SPAD chlorophyll meter reading can be pronouncedly affected by chloroplast movement. Photosynth Res. 2010;105(3):265–271. PubMed

Trueba S, Pan R, Scoffoni C, John GP, Davis SD, Sack L. Thresholds for leaf damage due to dehydration: Declines of hydraulic function, stomatal conductance and cellular integrity precede those for photochemistry. New Phytol. 2019;223(1):134–149. PubMed

Van de Hulst HC. Light scattering by small particles. New York: Dover Publications Inc.; 1981.

Jacquemoud S, Ustin S. Leaf optical properties. Cambridge: Cambridge University Press; 2019.

Zygielbaum AI, Gitelson AA, Arkebauer TJ, Rundquist DC. Non-destructive detection of water stress and estimation of relative water content in maize. Geophys Res Lett. 2009;36(12):38906.

Féret JB, Maire G, Jay S, Berveiller D, Bendoula R, Hmimina G, Cheraiet A, Oliveira JC, Ponzoni FJ, Solanki T, et al. . Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning. Remote Sens Environ. 2019;231:110959.

Canny MJ, Huang CX. Leaf water content and palisade cell size. New Phytol. 2006;170(1):75–85. PubMed

Nauš J, Šmecko S, Špundová M. Chloroplast avoidance movement as a sensitive indicator of relative water content during leaf desiccation in the dark. Photosynth Res. 2016;129(2):217–225. PubMed

Janečková H, Husičková A, Ferretti U, Prčina M, Pilařová E, Plačková L, Pospíšil P, Doležal K, Špundová M. The interplay between cytokinins and light during senescence in detached Arabidopsis leaves. Plant Cell Environ. 2018;41(8):1870–1885. PubMed

Kučerová Z, Rác M, Mikulík J, Plíhal O, Pospíšil P, Bryksová M, Sedlářová M, Doležal K, Špundová M. The anti-senescence activity of cytokinin arabinosides in wheat and Arabidopsis is negatively correlated with ethylene production. Int J Mol Sci. 2020;21:8109. PubMed PMC

Terashima I, Fujita T, Inoue T, Chow WS, Oguchi R. Green light drives leaf photosynthesis more efficiently than red light in strong white light: Revisiting the enigmatic question of why leaves are green. Plant Cell Physiol. 2009;50:684–697. PubMed

Gausman HW, Allen WA, Escobar DE. Refractive index of plant cell walls. Appl Opt. 1974;13(1):109. PubMed

Baránková B, Lazár D, Nauš J. Analysis of the effect of chloroplast arrangement on optical properties of green tobacco leaves. Remote Sens Environ. 2016;174:181–196.

Momayyezi M, Borsuk AM, Brodersen CR, Gilbert ME, Théroux-Rancourt G, Kluepfel DA, McElrone AJ. Desiccation of the leaf mesophyll and its implications for CO2 diffusion and light processing. Plant Cell Environ. 2022;45(5):1362–1381. PubMed PMC

Zwieniecki MA, Brodribb TJ, Holbrook NM. Hydraulic design of leaves: Insights from rehydration kinetics. Plant Cell Environ. 2007;30(8):910–921. PubMed

Scoffoni C, Albuquerque C, Buckley TN, Sack L. The dynamic multi-functionality of leaf water transport outside the xylem. New Phytol. 2023;239(6):2099–2107. PubMed

Canny M, Wong SC, Huang C, Miller C. Differential shrinkage of mesophyll cells in transpiring cotton leaves: Implications for static and dynamic pools of water, and for water transport pathways. Funct Plant Biol. 2012;39(2):91. PubMed

Barboričová M, Filaček A, Mynáriková Vysoká D, Gašparovič K, Živčák M, Brestič M. Sensitivity of fast chlorophyll fluorescence parameters to combined heat and drought stress in wheat genotypes. Plant Soil Environ. 2022;68(7):309–316.

Keller B, Vass I, Matsubara S, Paul K, Jedmowski C, Pieruschka R, Nedbal L, Rascher U, Muller O. Maximum fluorescence and electron transport kinetics determined by light-induced fluorescence transients (LIFT) for photosynthesis phenotyping. Photosynth Res. 2019;140:221–233. PubMed PMC

Mohammed GH, Colombo R, Middleton EM, Rascher U, Tol C, Nedbal L, Goulas Y, Pérez-Priego O, Damm A, Meroni M, et al. . Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sens Environ. 2019;231:111177. PubMed PMC

Wen J, Köhler P, Duveiller G, Parazoo NC, Magney TS, Hooker G, Yu L, Chang CY, Sun Y. A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF). Remote Sens Environ. 2020;239:111644.

Ma H, Cui T, Cao L. Monitoring of drought stress in Chinese forests based on satellite solar-induced chlorophyll fluorescence and multi-source remote sensing indices. Remote Sens. 2023;15(4):879.

Liu Q, Zhang F, Zhao X. The superiority of solar-induced chlorophyll fluorescence sensitivity over other vegetation indices to drought. J Arid Environ. 2022;204:104787.

Berger K, Machwitz M, Kycko M, Kefauver SC, Van Wittenberghe S, Gerhards M, Verrelst J, Atzberger C, Tol C, Damm A, et al. . Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote Sens Environ. 2022;280: Article 113198. PubMed PMC

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