Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Materials, Water Shortage and Heating
2.2. Description of the Developed System for Proximal PRI Imaging
2.3. Simultaneous Measurements of Fast Relaxing Component of Non-Photochemical Quenching and PRI
2.4. Measurements of Changes in PRI Induced by Short-Term Illuminations by GYL
2.5. Statistics
3. Results
3.1. Relations of PRI and ΔPRI to NPQF under Different Intensities of Actinic Light
3.2. Relations of PRI and ΔPRI to NPQF under Water Shortage
3.3. Relations of PRI and ΔPRI to NPQF after Heating
3.4. Total Relations of PRI and ΔPRI
3.5. Changes in PRI Induced by Short-Term Illuminations by GYL
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Pinter, P.J., Jr.; Hatfield, J.L.; Schepers, J.S.; Barnes, E.M.; Moran, M.S.; Daughtry, C.S.T.; Upchurch, D.R. Remote sensing for crop management. Photogram. Eng. Remote Sens. 2003, 69, 647–664. [Google Scholar] [CrossRef] [Green Version]
- Grace, J.; Nichol, C.; Disney, M.; Lewis, P.; Quaife, T.; Bowyer, P. Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? Glob. Chang. Biol. 2007, 13, 1484–1497. [Google Scholar] [CrossRef]
- Peñuelas, J.; Garbulsky, M.F.; Filella, I. Photochemical reflectance index (PRI) and remote sensing of plant CO₂ uptake. New Phytol. 2011, 191, 596–599. [Google Scholar] [CrossRef] [PubMed]
- Weng, J.H.; Wong, S.L.; Lai, K.M.; Lin, R.J. Relationships between photosystem II efficiency and photochemical reflectance index under different levels of illumination: Comparison among species grown at high- and low elevations through different seasons. Trees-Struct. Funct. 2012, 26, 343–351. [Google Scholar] [CrossRef]
- Zhang, C.; Filella, I.; Liu, D.; Ogaya, R.; Llusià, J.; Asensio, D.; Peñuelas, J. Photochemical reflectance index (PRI) for detecting responses of diurnal and seasonal photosynthetic activity to experimental drought and warming in a mediterranean shrubland. Remote Sens. 2017, 9, 1189. [Google Scholar] [CrossRef] [Green Version]
- Peñuelas, J.; Filella, I.; Biel, C.; Serrano, L.; Savé, R. The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14, 1887–1905. [Google Scholar] [CrossRef]
- Peñuelas, J.; Piñol, J.; Ogaya, R.; Filella, I. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). Int. J. Remote Sens. 1997, 18, 2869–2875. [Google Scholar] [CrossRef]
- Gitelson, A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Plant Physiol. 1994, 143, 286–292. [Google Scholar] [CrossRef]
- Blackburn, G.A. Quantifying chlorophylls and carotenoids at leaf and canopy scale: An evaluation of some hyperspectral approaches. Remote Sens. Environ. 1998, 66, 273–285. [Google Scholar] [CrossRef]
- Gamon, J.A.; Huemmrich, K.F.; Wong, C.Y.S.; Ensminger, I.; Garrity, S.; Hollinger, D.Y.; Noormets, A.; Peñuelas, J. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proc. Natl. Acad. Sci. USA 2016, 113, 13087–13092. [Google Scholar] [CrossRef] [Green Version]
- Penuelas, J.; Baret, F.; Filella, I. Semiempirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 1995, 31, 221–230. [Google Scholar]
- Filella, I.; Amaro, T.; Araus, J.L.; Peñuelas, J. Relationship between photosynthetic radiation-use efficiency of barley canopies and the photochemical reflectance index (PRI). Physiol. Plant 1996, 96, 211–216. [Google Scholar] [CrossRef]
- Filella, I.; Porcar-Castell, A.; Munné-Bosch, S.; Bäck, J.; Garbulsky, M.F.; Peñuelas, J. PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. Int. J. Remote Sens. 2009, 30, 4443–4455. [Google Scholar] [CrossRef]
- Peñuelas, J.; Marino, G.; Llusia, J.; Morfopoulos, C.; Farré-Armengol, G.; Filella, I. Photochemical reflectance index as an indirect estimator of foliar isoprenoid emissions at the ecosystem level. Nat. Commun. 2013, 4, 2604. [Google Scholar] [CrossRef] [Green Version]
- Balzarolo, M.; Peñuelas, J.; Filella, I.; Portillo-Estrada, M.; Ceulemans, R. Assessing ecosystem isoprene emissions by hyperspectral remote sensing. Remote Sens. 2018, 10, 1086. [Google Scholar] [CrossRef] [Green Version]
- Sukhov, V.; Sukhova, E.; Gromova, E.; Surova, L.; Nerush, V.; Vodeneev, V. The electrical signal-induced systemic photosynthetic response is accompanied by changes in the photochemical reflectance index in pea. Funct. Plant Biol. 2019, 46, 328–338. [Google Scholar] [CrossRef] [PubMed]
- Sukhova, E.; Yudina, L.; Akinchits, E.; Vodeneev, V.; Sukhov, V. Influence of electrical signals on pea leaf reflectance in the 400-800-nm range. Plant Signal. Behav. 2019, 14, 1610301. [Google Scholar] [CrossRef]
- Sukhova, E.; Yudina, L.; Gromova, E.; Nerush, V.; Vodeneev, V.; Sukhov, V. Burning-induced electrical signals influence broadband reflectance indices and water index in pea leaves. Plant Signal. Behav. 2020, 15, 1737786. [Google Scholar] [CrossRef]
- Mahlein, A.K.; Steiner, U.; Dehne, H.W.; Oerke, E.C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis. Agric. 2010, 11, 413–431. [Google Scholar] [CrossRef]
- Mahlein, A.-K. Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 2016, 100, 241–251. [Google Scholar] [CrossRef] [Green Version]
- Mahlein, A.K.; Kuska, M.T.; Behmann, J.; Polder, G.; Walter, A. Hyperspectral sensors and imaging technologies in phytopathology: State of the art. Annu. Rev. Phytopathol. 2018, 56, 535–558. [Google Scholar] [CrossRef] [PubMed]
- Badgley, G.; Field, C.B.; Berry, J.A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 2017, 3, e1602244. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rouse, J.W., Jr.; Haas, R.H.; Schell, J.A.; Deering, D.W.; Harlan, J.C. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation; Type III Final Rep; The National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC): Greenbelt, MD, USA, 1974.
- Eitel, J.U.H.; Long, D.S.; Gessler, P.E.; Hunt, E.R., Jr.; Brown, D.J. Sensitivity of ground-based remote sensing estimates of wheat chlorophyll content to variation in soil reflectance. Soil Sci. Soc. Am. J. 2009, 73, 1715–1723. [Google Scholar] [CrossRef] [Green Version]
- Gao, B.C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 158, 257–266. [Google Scholar] [CrossRef]
- Sytar, O.; Brücková, K.; Kovár, M.; Živčák, M.; Hemmerich, I.; Brestič, M. Nondestructive detection and biochemical quantification of buckwheat leaves using visible (VIS) and near-infrared (NIR) hyperspectral reflectance imaging. J. Centr. Eur. Agric. 2017, 18, 864–878. [Google Scholar] [CrossRef] [Green Version]
- 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, 443. [Google Scholar] [CrossRef] [Green Version]
- El-Hendawy, S.; Al-Suhaibani, N.; Dewir, Y.H.; Elsayed, S.; Alotaibi, M.; Hassan, W.; Refay, Y.; Tahir, M.U. Ability of modified spectral reflectance indices for estimating growth and photosynthetic efficiency of wheat under saline field conditions. Agronomy 2019, 9, 35. [Google Scholar] [CrossRef] [Green Version]
- El-Hendawy, S.E.; Alotaibi, M.; Al-Suhaibani, N.; Al-Gaadi, K.; Hassan, W.; Dewir, Y.H.; Emam, M.A.E.-G.; Elsayed, S.; Schmidhalter, U. Comparative performance of spectral reflectance indices and multivariate modeling for assessing agronomic parameters in advanced spring wheat lines under two contrasting irrigation regimes. Front. Plant Sci. 2019, 10, 1537. [Google Scholar] [CrossRef]
- Sun, H.; Feng, M.; Xiao, L.; Yang, W.; Wang, C.; Jia, X.; Zhao, Y.; Zhao, C.; Muhammad, S.K.; Li, D. Assessment of plant water status in winter wheat (Triticum aestivum L.) based on canopy spectral indices. PLoS ONE 2019, 14, e0216890. [Google Scholar] [CrossRef]
- Sukhova, E.; Yudina, L.; Gromova, E.; Ryabkova, A.; Kior, D.; Sukhov, V. Complex analysis of the efficiency of difference reflectance indices on the basis of 400–700 nm wavelengths for revealing the influences of water shortage and heating on plant seedlings. Remote Sens. 2021, 13, 962. [Google Scholar] [CrossRef]
- Gamon, J.A.; Peñuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35–44. [Google Scholar] [CrossRef]
- Garbulsky, M.F.; Peñuelas, J.; Gamon, J.; Inoue, Y.; Filella, I. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies. A review and meta-analysis. Remote Sens. Environ. 2011, 115, 281–297. [Google Scholar] [CrossRef]
- Zhang, C.; Filella, I.; Garbulsky, M.F.; Peñuelas, J. Affecting factors and recent improvements of the photochemical reflectance index (PRI) for remotely sensing foliar, canopy and ecosystemic radiation-use efficiencies. Remote Sens. 2016, 8, 677. [Google Scholar] [CrossRef] [Green Version]
- Sukhova, E.; Sukhov, V. Connection of the photochemical reflectance index (PRI) with the photosystem II quantum yield and nonphotochemical quenching can be dependent on variations of photosynthetic parameters among investigated plants: A meta-analysis. Remote Sens. 2018, 10, 771. [Google Scholar] [CrossRef] [Green Version]
- Sukhova, E.; Sukhov, V. Relation of photochemical reflectance indices based on different wavelengths to the parameters of light reactions in photosystems I and II in pea plants. Remote Sens. 2020, 12, 1312. [Google Scholar] [CrossRef] [Green Version]
- Peñuelas, J.; Filella, I.; Gamon, J.A. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol. 1995, 131, 291–296. [Google Scholar] [CrossRef]
- Evain, S.; Flexas, J.; Moya, I. A new instrument for passive remote sensing: 2. Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence. Remote Sens. Environ. 2004, 91, 175–185. [Google Scholar] [CrossRef]
- Murakami, K.; Ibaraki, Y. Time course of the photochemical reflectance index during photosynthetic induction: Its relationship with the photochemical yield of photosystem II. Physiol. Plant 2019, 165, 524–536. [Google Scholar] [CrossRef]
- Sukhova, E.M.; Yudina, L.M.; Vodeneev, V.A.; Sukhov, V.S. Analysis of changes in photochemical reflectance index (PRI) in relation to the acidification of the lumen of the chloroplasts of pea and geranium leaves under a short-term illumination. Biochem. Moscow. Suppl. Ser. A 2019, 13, 243–252. [Google Scholar] [CrossRef]
- Sukhova, E.; Sukhov, V. Analysis of light-induced changes in the photochemical reflectance index (PRI) in leaves of pea, wheat, and pumpkin using pulses of green-yellow measuring light. Remote Sens. 2019, 11, 810. [Google Scholar] [CrossRef] [Green Version]
- Maxwell, K.; Johnson, G.N. Chlorophyll fluorescence—A practical guide. J. Exp. Bot. 2000, 51, 659–668. [Google Scholar] [CrossRef]
- Porcar-Castell, A.; Tyystjärvi, E.; Atherton, J.; van der Tol, C.; Flexas, J.; Pfündel, E.E.; Moreno, J.; Frankenberg, C.; Berry, J.A. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: Mechanisms and challenges. J. Exp. Bot. 2014, 65, 4065–4095. [Google Scholar] [CrossRef] [PubMed]
- Ruban, A.V. Nonphotochemical chlorophyll fluorescence quenching: Mechanism and effectiveness in protecting plants from photodamage. Plant Physiol. 2016, 170, 1903–1916. [Google Scholar] [CrossRef] [Green Version]
- Yudina, L.; Sukhova, E.; Gromova, E.; Nerush, V.; Vodeneev, V.; Sukhov, V. A light-induced decrease in the photochemical reflectance index (PRI) can be used to estimate the energy-dependent component of non-photochemical quenching under heat stress and soil drought in pea, wheat, and pumpkin. Photosynth. Res. 2020, 146, 175–187. [Google Scholar] [CrossRef]
- Gamon, J.A.; Field, C.B.; Bilger, W.; Björkman, O.; Fredeen, A.L.; Peñuelas, J. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 1990, 85, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Kohzuma, K.; Hikosaka, K. Physiological validation of photochemical reflectance index (PRI) as a photosynthetic parameter using Arabidopsis thaliana mutants. Biochem. Biophys. Res. Commun. 2018, 498, 52–57. [Google Scholar] [CrossRef]
- Porcar-Castell, A.; Garcia-Plazaola, J.I.; Nichol, C.J.; Kolari, P.; Olascoaga, B.; Kuusinen, N.; Fernández-Marín, B.; Pulkkinen, M.; Juurola, E.; Nikinmaa, E. Physiology of the seasonal relationship between the photochemical reflectance index and photosynthetic light use efficiency. Oecologia 2012, 170, 313–323. [Google Scholar] [CrossRef]
- Stylinski, C.D.; Gamon, J.A.; Oechel, W.C. Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species. Oecologia 2002, 131, 366–374. [Google Scholar] [CrossRef] [PubMed]
- Garbulsky, M.F.; Peñuelas, J.; Ogaya, R.; Filella, I. Leaf and stand-level carbon uptake of a Mediterranean forest estimated using the satellite-derived reflectance indices EVI and PRI. Int. J. Remote Sens. 2013, 34, 1282–1296. [Google Scholar] [CrossRef]
- Wong, C.Y.; Gamon, J.A. Three causes of variation in the photochemical reflectance index (PRI) in evergreen conifers. New Phytol. 2015, 206, 187–195. [Google Scholar] [CrossRef]
- Ibaraki, Y.; Dutta Gupta, S. Nondestructive evaluation of the photosynthetic properties of micropropagated plantlets by imaging photochemical reflectance index under low light intensity. Vitr. Cell. Dev. Biol. Plant 2010, 46, 530–536. [Google Scholar] [CrossRef]
- Ibaraki, Y.; Matsumura, K.; Dutta Gupta, D. Low-cost photochemical reflectance index measurements of micropropagated plantlets using image analysis. Comput. Electron. Agric. 2010, 71, 170–175. [Google Scholar] [CrossRef]
- Gamon, J.A.; Surfus, J.S. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 1999, 143, 105–117. [Google Scholar] [CrossRef]
- Li, X.P.; Björkman, O.; Shih, C.; Grossman, A.R.; Rosenquist, M.; Jansson, S.; Niyogi, K.K. A pigment-binding protein essential for regulation of photosynthetic light harvesting. Nature. 2000, 403, 391–395. [Google Scholar] [CrossRef] [PubMed]
- Jajoo, A.; Mekala, N.R.; Tongra, T.; Tiwari, A.; Grieco, M.; Tikkanen, M.; Aro, E.M. Low pH-induced regulation of excitation energy between the two photosystems. FEBS Lett. 2014, 588, 970–974. [Google Scholar] [CrossRef] [Green Version]
- Demmig-Adams, B.; Adams III, W.W. The role of xanthophyll cycle carotenoids in the protection of photosynthesis. Trends Plant Sci. 1996, 1, 21–26. [Google Scholar] [CrossRef]
- Kalaji, H.M.; Schansker, G.; Ladle, R.J.; Goltsev, V.; Bosa, K.; Allakhverdiev, S.I.; Brestic, M.; Bussotti, F.; Calatayud, A.; Dąbrowski, P.; et al. Frequently asked questions about in vivo chlorophyll fluorescence: Practical issues. Photosynth. Res. 2014, 122, 121–158. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Magney, T.S.; Eusden, S.A.; Eitel, J.U.H.; Logan, B.A.; Jiang, J.; Vierling, L.A. Assessing leaf photoprotective mechanisms using terrestrial LiDAR: Towards mapping canopy photosynthetic performance in three dimensions. New Phytol. 2014, 201, 344–356. [Google Scholar] [CrossRef]
- Kováč, D.; Veselovská, P.; Klem, K.; Večeřová, K.; Ač, A.; Peñuelas, J.; Urban, O. Potential of photochemical reflectance index for indicating photochemistry and light use efficiency in leaves of European beech and Norway spruce trees. Remote Sens. 2018, 10, 1202. [Google Scholar] [CrossRef] [Green Version]
- Kováč, D.; Veselá, B.; Klem, K.; Večeřová, K.; Kmecová, Z.M.; Peñuelas, J.; Urban, O. Correction of PRI for carotenoid pigment pools improves photosynthesis estimation across different irradiance and temperature conditions. Remote Sens. Environ. 2020, 244, 111834. [Google Scholar] [CrossRef]
- Gamon, J.A.; Serrano, L.; Surfus, J.S. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia. 1997, 112, 492–501. [Google Scholar] [CrossRef]
- Deamer, D.W.; Crofts, A.R.; Packer, L. Mechanisms of light-induced structural changes in chloroplasts I. Light-scattering increments and ultrastructural changes mediated by proton transport. Biochim. Biophys. Acta. 1967, 131, 81–96. [Google Scholar] [CrossRef]
- Murakami, S.; Packer, L. Protonation and chloroplast membrane structure. J. Cell Biol. 1970, 47, 332–351. [Google Scholar] [CrossRef] [Green Version]
- Schreiber, U.; Klughammer, C. New accessory for the DUAL-PAM-100: The P515/535 module and examples of its application. PAM Appl. Notes 2008, 1, 1–10. [Google Scholar]
- Bilger, W.; Björkman, O.; Thayer, S.S. Light-induced spectral absorbance changes in relation to photosynthesis and the epoxidation state of xanthophyll cycle components in cotton leaves. Plant Physiol. 1989, 91, 542–551. [Google Scholar] [CrossRef]
- Bilger, W.; Björkman, O. Relationships among violaxanthin deepoxidation, thylakoid membrane conformation, and nonphotochemical chlorophyll fluorescence quenching in leaves of cotton (Gossypium hirsutum L.). Planta 1994, 193, 238–246. [Google Scholar] [CrossRef]
- Van Wittenberghe, S.; Alonso, L.; Malenovský, Z.; Moreno, J. In vivo photoprotection mechanisms observed from leaf spectral absorbance changes showing VIS-NIR slow-induced conformational pigment bed changes. Photosynth. Res. 2019, 142, 283–305. [Google Scholar] [CrossRef] [Green Version]
- Avenson, T.J.; Cruz, J.A.; Kramer, D.M. Modulation of energy-dependent quenching of excitons in antennae of higher plants. Proc. Natl. Acad. Sci. USA 2004, 101, 5530–5535. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Klughammer, C.; Siebke, K.; Schreiber, U. Continuous ECS-indicated recording of the proton-motive charge flux in leaves. Photosynth. Res. 2013, 117, 471–487. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Sukhov, V.; Sukhova, E.; Khlopkov, A.; Yudina, L.; Ryabkova, A.; Telnykh, A.; Sergeeva, E.; Vodeneev, V.; Turchin, I. Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light. Remote Sens. 2021, 13, 1762. https://doi.org/10.3390/rs13091762
Sukhov V, Sukhova E, Khlopkov A, Yudina L, Ryabkova A, Telnykh A, Sergeeva E, Vodeneev V, Turchin I. Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light. Remote Sensing. 2021; 13(9):1762. https://doi.org/10.3390/rs13091762
Chicago/Turabian StyleSukhov, Vladimir, Ekaterina Sukhova, Andrey Khlopkov, Lyubov Yudina, Anastasiia Ryabkova, Alexander Telnykh, Ekaterina Sergeeva, Vladimir Vodeneev, and Ilya Turchin. 2021. "Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light" Remote Sensing 13, no. 9: 1762. https://doi.org/10.3390/rs13091762
APA StyleSukhov, V., Sukhova, E., Khlopkov, A., Yudina, L., Ryabkova, A., Telnykh, A., Sergeeva, E., Vodeneev, V., & Turchin, I. (2021). Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light. Remote Sensing, 13(9), 1762. https://doi.org/10.3390/rs13091762