Measurements of Photochemical Reflectance Index as a Tool for Remote Monitoring of Photosynthetic Parameters of Plants
- Authors: Zolin Y.A1, Sukhova E.M1, Sukhov V.S1
-
Affiliations:
- Institute of Biology and Biomedicine, N.I. Lobachevsky State University
- Issue: Vol 69, No 3 (2024)
- Pages: 603–614
- Section: Complex systems biophysics
- URL: https://journals.rcsi.science/0006-3029/article/view/262933
- DOI: https://doi.org/10.31857/S0006302924030169
- EDN: https://elibrary.ru/OEQGTV
- ID: 262933
Cite item
Abstract
The development of remote and proximal sensing techniques for early detection of photosynthetic responses under action of stressors is an important agricultural and environmental task. The photochemical reflectance index (PRI), typically calculated on the basis of the reflected light at 531 and 570 nm, is potentially sensitive to rapid changes in photosynthesis under unfavorable conditions. Mechanisms of PRI changes are thought to include chloroplast shrinkage and aggregation of light-harvesting complexes, transitions in the xanthophyll cycle, and changes in chlorophyll and carotenoid concentrations, making PRI difficult to be applied for monitoring plant health. Light measurement, the study of light-induced changes in PRI, and the analysis of modified PRIs are the ways for improving the efficiency of the application of PRI. Other ways may also favor improvement of the efficiency (for example, the development of methods of PRI estimation based on RGB imaging). The development of PRI measurement and analysis techniques holds significant promise for monitoring photosynthetic responses of plants to stressed environments.
About the authors
Yu. A Zolin
Institute of Biology and Biomedicine, N.I. Lobachevsky State UniversityNizhny Novgorod, Russia
E. M Sukhova
Institute of Biology and Biomedicine, N.I. Lobachevsky State UniversityNizhny Novgorod, Russia
V. S Sukhov
Institute of Biology and Biomedicine, N.I. Lobachevsky State University
Email: vssuh@mail.ru
Nizhny Novgorod, Russia
References
- Pokorny J., Brom J., Cermak J., Hesslerova P., Huryna H., Nadezhdina N., and Rejškova A. Solar energy dissipation and temperature control by water and plants. Int. J. Water, 5 (4), 311–336 (2010). doi: 10.1504/IJW.2010.038726
- Ellison D., Morris C. E., Locatelli B., Sheil D., Cohen J., Murdiyarso D., Gutierrez V., Van Noordwijk M., Creed I. F., Pokorny J., Gaveau D., Spracklen D., Bargues-Tobella A., Ilstedt U., Teuling A., Gebrehiwot S. G., Sands D. C., Muys B., Verbist B., Springgay E., and Sullivan C. A. Trees, forests and water: Cool insights for a hot world. Glob. Environ. Change, 43 (51), 51–61 (2017). doi: 10.1016/j.gloenvcha.2017.01.002
- Rascher U. and Nedbal L. Dynamics of photosynthesis in fluctuating light. Curr. Opin. Plant Biol., 9 (6), 671–678 (2006). doi: 10.1016/j.pbi.2006.09.012
- Smith W. and Berry Z. Sunflecks? Tree Physiol., 33 (3), 233–237 (2013). doi: 10.1093/treephys/tpt005
- Nievola C., Carvalho C., Carvalho V., and Rodrigues E. Rapid responses of plants to temperature changes. Temperature, 4 (4), 371–405 (2017). doi: 10.1080/23328940.2017.1377812
- Kior A., Sukhov V., and Sukhova E. Application of reflectance indices for remote sensing of plants and revealing actions of stressors. Photonics, 8 (12), 582 (2021). doi: 10.3390/photonics8120582
- Zubler A. V. and Yoon J. Proximal methods for plant stress detection using optical sensors and machine learning. Biosensors, 10 (12), 193 (2020). doi: 10.3390/bios10120193
- Grace J., Nichol C., Disney M., Lewis P., Quaife T., and Bowyer P. Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? Global Change Biol., 13 (7), 1484–1497 (2007). doi: 10.1111/j.1365-2486.2007.01352.x
- Weng J. H., Wong S. L., Lai K. M., and Lin R. J. Relationships between photosystem II efficiency and photochemical reflectance index under different levels of illumination: Comparison among species grown at highand low elevations through different seasons. Trees, 26 (2), 343- 351 (2012). doi: 10.1007/s00468-011-0596-0
- Zhang C., Filella I., Liu D., Ogaya R., Llusia J., Asensio D., and Penuelas 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., 9 (11), 1189 (2017). doi: 10.3390/rs9111189
- Penuelas J., Filella I., Biel C., Serrano L., and Save R. The Reflectance at the 950-970 Nm Region as an Indicator of Plant Water Status. Int. J. Remote Sens., 14 (10), 1887–1905 (1993). doi: 10.1080/01431169308954010
- Gitelson A. and Merzlyak N. spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol., 143 (3), 286–292 (1994). doi: 10.1016/S0176-1617(11)81633-0
- Filella I., Amaro T., Araus J. L., and Penuelas J. Relationship between photosynthetic radiation-use efficiency of barley caeopies and the photochemical reflectance index (PRI). Physiol. Plant., 96, 211–216 (1996). doi: 10.1111/J.1399-3054.1996.TB00204.X
- Sukhov V., Sukhova E., Gromova E., Surova L., Nerush V., and Vodeneev V. The electrical signal-induced systemic photosynthetic response is accompanied by changes in the photochemical reflectance index in pea. Funct. Plant Biol., 46 (4), 328–338 (2019). doi: 10.1071/FP18224
- Badgley G., Field C., and Berry J. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv., 3 (3), e1602244 (2017). doi: 10.1126/sciadv.1602244
- Mahlein A., Steiner H., Dehne H., and Oerke E. C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis. Agric., 11 (4), 413–431 (2010). doi: 10.1007/s11119-010-9180-7
- Mahlein A. Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis., 100 (2), 241–254 (2016). doi: 10.1094/PDIS-03-15-0340-FE
- Gamon J., Penuelas J., and Field C. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ., 41 (1), 35–44 (1992). doi: 10.1016/0034-4257(92)90059-S
- Gamon J., Serrano L., and Surfus J. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecol., 112 (4), 492–501 (1997). doi: 10.1007/s004420050337
- Penuelas J., Filella I., and Gamon J. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol., 131 (3), 291–296 (1995). doi: 10.1111/j.1469-8137.1995.tb03064.x
- Garbulsky M., Penuelas J., Gamon J., Inoue Y., and 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., 115 (2), 281–297 (2011). doi: 10.1016/j.rse.2010.08.023
- Zhang C., Filella I., Garbulsky M., and Penuelas J. Affecting factors and recent improvements of the photochemical reflectance index (PRI) for remotely sensing foliar, canopy and ecosystemic radiation-use efficiencies. Remote Sens., 8 (9), 677 (2016). doi: 10.3390/rs8090677
- Eitel J., Long D., Gessler P., Hunt E. R., and Brown D. J. Sensitivity of ground-based remote sensing estimates of wheat chlorophyll content to variation in soil reflectance. Soil Sci. Soc. Amer. J., 73 (5), 1715–1723 (2009). doi: 10.2136/sssaj2008.0288
- Penuelas J., Pinol R., Ogaya R., and Filella I. Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int. J. Remote Sens., 18 (13), 2869–2875 (1997). doi: 10.1080/014311697217396
- Serrano L. and Gorchs G. Water availability affects the capability of reflectance indices to estimate berry yield and quality attributes in rain-fed vineyards. Agronomy, 12 (9), 2091 (2022). doi: 10.3390/agronomy12092091
- Gao B. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ., 58 (3), 257–266 (1996). doi: 10.1016/S0034-4257(96)00067-3
- Evain S., Flexas J., and 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., 91 (2), 175–185 (2004). doi: 10.1016/j.rse.2004.03.012
- Sukhov V., Sukhova E., Khlopkov A., Yudina L., Ryabkova A., Telnykh A., Sergeeva E., Vodeneev V., and Turchin I. Proximal imaging of changes in photochemical reflectance index in leaves based on using pulses of green-yellow light. Remote Sens., 13 (9), 1762 (2021). doi: 10.3390/rs13091762
- Sukhova E. and 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. 10 (5), 771 (2018). doi: 10.3390/rs10050771
- Kohzuma K., Tamaki M., and Hikosaka K. Corrected photochemical reflectance index (PRI) is an effective tool for detecting environmental stresses in agricultural crops under light conditions. J. Plant Res., 134 (4), 683–694 (2021). doi: 10.1007/s10265-021-01316-1
- Sukhova E. and 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., 11 (7), 810 (2019). doi: 10.3390/rs11070810
- Demmig-Adams B. Carotenoids and photoprotection in plants: A role for the xanthophyll zeaxanthin. Biochim. Biophys. Acta, 1020 (1), 1–24 (1990). doi: 10.1016/0005-2728(90)90088-L
- Niyogi K. K., Grossman A. R., and Bjorkman O. Arabidopsis mutants define a central role for the xanthophyll cycle in the regulation of photosynthetic energy conversion. Plant Cell., 10 (7), 1121–1134 (1998). doi: 10.1105/tpc.10.7.1121
- Maxwell K. and Johnson G. Chlorophyll fluorescence – a practical guide. J. Exp. Bot., 51 (345), 659–668 (2000). doi: 10.1093/jexbot/51.345.659
- Ruban A. Nonphotochemical chlorophyll fluorescence quenching: mechanism and effectiveness in protecting plants from photodamage. Plant Physiol., 170 (4), 1903–1916 (2016). doi: 10.1104/pp.15.01935
- Jahns P. The xanthophyll cycle in intermittent lightgrown pea plants (possible functions of chlorophyll a/b-binding proteins). Plant Physiol., 108 (1), 149–156 (1995). doi: 10.1104/pp.108.1.149
- Nilkens M., Kress E., Lambrev P., Miloslavina Y., Muller M., Holzwarth A. R., and Jahns P. Identification of a slowly inducible zeaxanthin-dependent component of non-photochemical quenching of chlorophyll fluorescence generated under steady-state conditions in Arabidopsis. Biochim. Biophys. Acta, 1797 (4), 466–475 (2010). doi: 10.1016/j.bbabio.2010.01.001
- Сухова Е. М., Юдина Л. Ю., Воденеев В. А. и Сухов В. С. Анализ связи изменений фотохимического индекса отражения (PRI) и закисления люмена хлоропластов листьев гороха и герани в условиях кратковременного освещения. Биологич. мембраны, 36 (3), 218–228 (2019). doi: 10.1134/S0233475519030083
- Jahns P, Latowski D., and Strzalka K. Mechanism and regulation of the violaxanthin cycle: the role of antenna proteins and membrane lipids. Biochim. Biophys. Acta, 1787 (1), 3–14 (2009). doi: 10.1016/j.bbabio.2008.09.013
- Li X. P., Gilmore A. M., Caffarri S., Bassi R., Golan T., Kramer D., and Niyogi K. K. Regulation of photosynthetic light harvesting involves intrathylakoid lumen pH sensing by the PsbS protein. J. Biol. Chem., 279 (22), 22866–22874 (2004). doi: 10.1074/jbc.M402461200
- Belgio E., Duffy C. D. P., and Ruban A. V. Switching light harvesting complex II into photoprotective state involves the lumen-facing apoprotein loop. Phys. Chem. Chem. Phys., 15 (29), 12253–12261 (2013). doi: 10.1039/c3cp51925b
- Kramer D. M., Cruz J. A., and Kanazawa A. Balancing the central roles of the thylakoid proton gradient. Trends Plant Sci., 8 (1), 27–32 (2003). doi: 10.1016/s1360-1385(02)00010-9
- Klughammer C., Siebke K., and Schreiber U. Continuous ECS-indicated recording of the proton-motive charge flux in leaves. Photosynth. Res., 117, 471–487 (2013). doi: 10.1007/s11120-013-9884-4
- Sukhov V., Surova L., Morozova E., Sherstneva O., and Vodeneev V. Changes in H+-ATP synthase activity, proton electrochemical gradient, and pH in pea chloroplast can be connected with variation potential. Front. Plant Sci., 7, 1092 (2016). doi: 10.3389/fpls.2016.01092
- Tikhonov A. N. pH-Dependent regulation of electron transport and ATP synthesis in chloroplasts. Photosynth. Res., 116, 511–534 (2013). doi: 10.1007/s11120013-9845-y
- Tikhonov A. N. The cytochrome b6f complex at the crossroad of photosynthetic electron transport pathways. Plant Physiol. Biochem., 81, 163–183 (2014). doi: 10.1016/j.plaphy.2013.12.011
- Murakami K. and Ibaraki Y. Time course of the photochemical reflectance index during photosynthetic induction: its relationship with the photochemical yield of photosystem II. Physiol. Plant., 165 (3), 524–536 (2019). doi: 10.1111/ppl.12745
- Yudina L., Sukhova E., Gromova E., Nerush V., Vodeneev V., and Sukhov V. A light-induced decrease in the photochemical reflectance index (PRI) can be used to estimate the energy-dependent component of nonphotochemical quenching under heat stress and soil drought in pea, wheat, and pumpkin. Photosynth. Res., 146 (1-3), 175–187 (2020). doi: 10.1007/s11120-02000718-x
- Filella I., Porcar-Castell A., Munne-Bosch S., Back J., Garbulsky M. F., and Penuelas 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., 30 (17), 4443–4455 (2009). doi: 10.1080/01431160802575661
- Porcar-Castell A., Garcia-Plazaola J. I., Nichol C. J., Kolari P., Olascoaga B., Kuusinen N., FernandezMarįn B., Pulkkinen M., Juurola E., and Nikinmaa E. Physiology of the seasonal relationship between the photochemical reflectance index and photosynthetic light use efficiency. Oecol., 170, 313–323 (2012). doi: 10.1007/s00442-012-2317-9
- Garbulsky M., Penuelas J., Ogaya R., and 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., 34 (4), 1282–1296 (2013). doi: 10.1080/01431161.2012.718457
- Wong C. and Gamon J. Three causes of variation in the photochemical reflectance index (PRI) in evergreen conifers. New Phytol., 206 (1), 187–195 (2015). doi: 10.1111/nph.13159
- Gitelson A. A., Gamon J. A., and Solovchenko A. Multiple drivers of seasonal change in PRI: Implications for photosynthesis 1. Leaf level. Remote Sens. Environ. 191, 110–116 (2017). doi: 10.1016/j.rse.2016.12.014
- Sukhova E., Zolin Y., Popova A., Yudina L., and Sukhov V. The influence of soil salt stress on modified photochemical reflectance indices in pea plants. Remote Sens., 15 (15), 3772 (2023). doi: 10.3390/rs15153772
- Sukhova E. and 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., 12 (8), 1312 (2020). doi: 10.3390/rs12081312
- Sukhova E., Yudina L., Kior A., Kior D., Popova A., Zolin Y., Gromova E., and Sukhov V. Modified photochemical reflectance indices as new tool for revealing influence of drought and heat on pea and wheat plants. Plants, 11 (10), 1308 (2022). doi: 10.3390/plants11101308
- Hmimina G., Dufrene E., and Soudani K. Relationship between photochemical reflectance index and leaf ecophysiological and biochemical parameters under two different water statuses: towards a rapid and efficient correction method using real-time measurements. Plant Cell Environ., 37 (2), 473–487 (2014). doi: 10.1111/pce.12171
- Kohzuma K. and Hikosaka K. Physiological validation of photochemical reflectance index (PRI) as a photosynthetic parameter using Arabidopsis thaliana mutants. Biochem. Biophys. Res. Commun., 498 (1), 52-57 (2018). doi: 10.1016/j.bbrc.2018.02.192
- Kovač D., Veselovska P., Klem K., Večeřova K., Ač A., Penuelas J., and 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., 10 (8), 1202 (2018). doi: 10.3390/rs10081202
- Tsujimoto K. and Hikosaka K. Estimating leaf photosynthesis of C3 plants grown under different environments from pigment index, photochemical reflectance index, and chlorophyll fluorescence. Photosynth. Res., 148, 33-46 (2021). doi: 10.1007/s11120-021-00833-3
- Yu Y., Piao J., Fan W., and Yang X. Modified photochemical reflectance index to estimate leaf maximum rate of carboxylation based on spectral analysis. Environ. Monit. Assess., 192 (12), 788 (2020). doi: 10.1007/s10661-020-08736-x
- Hikosaka K. and Noda H. M. Modeling leaf CO2 assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index. Plant Cell Environ., 42 (2), 730-739 (2019). doi: 10.1111/pce.13461
- Hikosaka K. and Tsujimoto K. Linking remote sensing parameters to CO2 assimilation rates at a leaf scale. J. Plant Res., 134 (4), 695-711 (2021). doi: 10.1007/s10265-021-01313-4
- Porcar-Castell A., Tyystjarvi E., Atherton J., van der Tol C., Flexas J., Pfundel 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., 65 (15), 40654095 (2014). doi: 10.1093/jxb/eru191
- Sukhova E., Ratnitsyna D., Gromova E., and Sukhov V. Development of two-dimensional model of photosynthesis in plant leaves and analysis of induction of spatial heterogeneity of CO2 assimilation rate under action of excess light and drought. Plants, 11 (23), 3285 (2022). doi: 10.3390/plants11233285
- Sukhova E., Ratnitsyna D., and Sukhov V. Simulated analysis of influence of changes in H+-ATPase activity and membrane CO2 conductance on parameters of photosynthetic assimilation in leaves. Plants, 11 (24), 3435 (2022). doi: 10.3390/plants11243435
- Zhang J., Su R., Fu Q., Ren W., Heide F., and Nie Y. A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging. Sci. Rep., 12, 11905 (2022). doi: 10.1038/s41598-022-16223-1
- Gupta S. D., Ibaraki Y., and Trivedi P. Applications of RGB color imaging in plants. In Plant Image Analysis: Fundamentals and Applications; Ed. by S. D. Gupta and Y. Ibaraki (Taylor & Francis eBooks, Boca Raton, 2014), pp. 41–62. doi: 10.1201/b17441-4
- Al-Tamimi N., Langan P., Bernad V., Walsh J. J., Mangina E., and Negrao S. Capturing crop adaptation to abiotic stress using image-based technologies. Open Biol., 12 (6), 210353 (2022). doi: 10.1098/rsob.210353
- Fu J., Liu J., Zhao R., Chen Z., Qiao Y., and Li D. Maize disease detection based on spectral recovery from RGB images. Front. Plant Sci., 13, 1056842 (2022). doi: 10.3389/fpls.2022.1056842
- Hamzah R., Abu Samah K. A. F., and Abdullah M. F., Investigation of RGB to HSI conversion methods for early plant disease detection using hierarchical synthesis convolutional neural networks. Int. J. Inform. Visual., 6 (1), 1–5 (2022). doi: 10.30630/joiv.6.1.852
- Gong L., Zhu C., Luo Y., and Fu X. Spectral reflectance reconstruction from Red-Green-Blue (RGB) images for chlorophyll content detection. Appl. Spectrosc., 77 (2), 200–209 (2023). doi: 10.1177/00037028221139871
- Lin Y.-T. and Finlayson G. D. A Rehabilitation of PixelBased Spectral Reconstruction from RGB Images. Sensors, 23 (8), 4155 (2023). doi: 10.3390/s23084155
- Lin Y.-T. and Finlayson G. D. An investigation on worst-case spectral reconstruction from RGB images via Radiance Mondrian World assumption. Color Res. Appl., 48 (2), 230–242 (2023). doi: 10.1002/col.22843