Mind the leaf anatomy while taking ground truth with portable chlorophyll meters
Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
Grantová podpora
LTAUSA18154
The ministry of Education, Youth and Sports of Czech Republic
QL24010275
The Ministry of Agriculture of the Czech Republic
80NSSC18K0337
NASA, LCLUC Program NNH17ZDA001N-LCLUC
PubMed
39805920
PubMed Central
PMC11730753
DOI
10.1038/s41598-024-84052-5
PII: 10.1038/s41598-024-84052-5
Knihovny.cz E-zdroje
- Klíčová slova
- Chlorophyll, Leaf pigments, Leaf structure, Leaf with hypodermis, Remote sensing, Vegetation index,
- MeSH
- chlorofyl * analýza MeSH
- listy rostlin * anatomie a histologie chemie metabolismus MeSH
- technologie dálkového snímání metody MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- chlorofyl * MeSH
A wide range of portable chlorophyll meters are increasingly being used to measure leaf chlorophyll content as an indicator of plant performance, providing reference data for remote sensing studies. We tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference. Optical Chl assessments included measurements taken by four chlorophyll meters: three transmittance-based (SPAD-502, Dualex-4 Scientific, and MultispeQ 2.0), one fluorescence-based (CCM-300), and vegetation indices calculated from the 400-2500 nm leaf reflectance acquired using an ASD FieldSpec and a contact plant probe. Three leaf types with different anatomy were included: dorsiventral laminar leaves, grass leaves, and needles. On laminar leaves, all instruments performed well for chlorophyll content estimation (R2 > 0.80, nRMSE < 15%), regardless of the variation in their specific internal structure (mesomorphic, scleromorphic, or scleromorphic with hypodermis), similarly to the performance of four reflectance indices (R2 > 0.90, nRMSE < 16%). For grasses, the model to predict chlorophyll content across multiple species had low performance with CCM-300 (R2 = 0.45, nRMSE = 11%) and failed for SPAD. For Norway spruce needles, the relation of CCM-300 values to chlorophyll content was also weak (R2 = 0.45, nRMSE = 11%). To improve the accuracy of data used for remote sensing algorithm development, we recommend calibration of chlorophyll meter measurements with biochemical assessments, especially for species with anatomy other than laminar dicot leaves. The take-home message is that portable chlorophyll meters perform well for laminar leaves and grasses with wider leaves, however, their accuracy is limited for conifer needles and narrow grass leaves. Species-specific calibrations are necessary to account for anatomical variations, and adjustments in sampling protocols may be required to improve measurement reliability.
Zobrazit více v PubMed
Richardson, A. D., Duigan, S. P. & Berlyn, G. P. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol.153, 185–194 (2002).
Houborg, R., Anderson, M. & Daughtry, C. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sens. Environ.113, 259–274 (2009).
Zhang, Y., He, N., Li, M., Yan, P. & Yu, G. Community chlorophyll quantity determines the spatial variation of grassland productivity. Sci. Total Environ.801, 149567 (2021). PubMed
Brewster, C., Fenner, N. & Hayes, F. Chronic ozone exposure affects nitrogen remobilization in wheat at key growth stages. Sci. Total Environ.908, 168288 (2024). PubMed
Gräf, M. et al. Application of leaf analysis in addition to growth assessment to evaluate the suitability of greywater for irrigation of Tilia cordata and Acer pseudoplatanus. Sci. Total Environ.836, 155745 (2022). PubMed
Hong, H. et al. Warming delays but grazing advances leaf senescence of five plant species in an alpine meadow. Sci. Total Environ.858, 159858 (2023). PubMed
Chi, D., Van Meerbeek, K., Yu, K., Degerickx, J. & Somers, B. Foliar optical traits capture physiological and phenological leaf plasticity in Tilia×euchlora in the urban environment. Sci. Total Environ.805, 150219 (2022). PubMed
Lhotáková, Z. et al. Detection of multiple stresses in scots pine growing at post-mining sites using visible to near-infrared spectroscopy. Environ. Sci. Process. Impacts15, 2004–2015 (2013). PubMed
Kandpal, K. C. & Kumar, A. Migrating from invasive to noninvasive techniques for enhanced leaf chlorophyll content estimations efficiency. Critic. Rev. Anal. Chem.10.1080/10408347.2023.2188425 (2023). PubMed
Beamish, A. et al. Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook. Remote Sens. Environ.246, 111872 (2020).
Burnett, A. C. et al. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. J. Exp. Botany72, 6175–6189 (2021). PubMed
Angel, Y. & McCabe, M. F. Machine learning strategies for the retrieval of leaf-chlorophyll dynamics: Model choice, sequential versus retraining learning, and hyperspectral predictors. Front. Plant Sci.13, 722442 (2022). PubMed PMC
Gates, D. M., Keegan, H. J., Schleter, J. C. & Weidner, V. R. Spectral properties of plants. Appl. Opt.4, 11–20 (1965).
Lichtenthaler, H. K. [34] Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. in (ed. Enzymology, B.-M. in) vol. 148 350–382 (Academic Press, 1987).
Lichtenthaler, H. K., Buschmann, C., Rinderle, U. & Schmuck, G. Application of chlorophyll fluorescence in ecophysiology. Radiat. Environ. Biophys.25, 297–308 (1986). PubMed
Gitelson, A. A. & Solovchenko, A. Non-invasive quantification of foliar pigments: Possibilities and limitations of reflectance- and absorbance-based approaches. J. Photochem. Photobiol. B Biol.178, 537–544 (2018). PubMed
Jacquemoud, S. & Ustin, S. Leaf Optical Properties (Cambridge University Press, 2019). 10.1017/9781108686457.
Mershon, J., Becker, M. & Bickford, C. Linkage between trichome morphology and leaf optical properties in New Zealand alpine Pachycladon (Brassicaceae). New Zealand J. Botany53, 175–182 (2015).
Neuwirthová, E., Lhotáková, Z., Lukeš, P. & Albrechtová, J. Leaf surface reflectance does not affect biophysical traits modelling from VIS-NIR spectra in plants with sparsely distributed trichomes. Remote Sens.13, 4144 (2021).
Gitelson, A. A., Buschmann, C. & Lichtenthaler, H. K. Leaf chlorophyll fluorescence corrected for re-absorption by means of absorption and reflectance measurements. J. Plant Physiol.152, 283–296 (1998).
Gitelson, A. A., Chivkunova, O. B. & Merzlyak, M. N. Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. Am. J. Botany96, 1861–1868 (2009). PubMed
Junker, L. V. & Ensminger, I. Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves. Tree Physiol.36, 694–711 (2016). PubMed
Hoch, W. A., Singsaas, E. L. & McCown, B. H. Resorption protection. Anthocyanins facilitate nutrient recovery in autumn by shielding leaves from potentially damaging light levels. Plant Physiol.133, 1296–1305 (2003). PubMed PMC
Tattini, M. et al. Epidermal coumaroyl anthocyanins protect sweet basil against excess light stress: Multiple consequences of light attenuation. Physiol. Plantarum152, 585–598 (2014). PubMed
Gould, K. S., Jay-Allemand, C., Logan, B. A., Baissac, Y. & Bidel, L. P. R. When are foliar anthocyanins useful to plants? Re-evaluation of the photoprotection hypothesis using Arabidopsis thaliana mutants that differ in anthocyanin accumulation. Environ. Exp. Botany154, 11–22 (2018).
Jordheim, M. et al. High concentrations of aromatic acylated anthocyanins found in cauline hairs in Plectranthus ciliatus. Phytochemistry128, 27–34 (2016). PubMed
Merzlyak, M. N., Chivkunova, O. B., Solovchenko, A. E. & Naqvi, K. R. Light absorption by anthocyanins in juvenile, stressed, and senescing leaves. J. Exp. Botany59, 3903–3911 (2008). PubMed PMC
Donnelly, A., Yu, R., Rehberg, C., Meyer, G. & Young, E. B. Leaf chlorophyll estimates of temperate deciduous shrubs during autumn senescence using a SPAD-502 meter and calibration with extracted chlorophyll. Ann. For. Sci.77, 30 (2020).
Kuhlgert, S. et al. MultispeQ Beta: A tool for large-scale plant phenotyping connected to the open PhotosynQ network. R. Soc. Open Sci.3, 160592 (2016). PubMed PMC
Parry, C., Blonquist, J. M. & Bugbee, B. In situ measurement of leaf chlorophyll concentration: Analysis of the optical/absolute relationship: The optical/absolute chlorophyll relationship. Plant Cell Environ.37, 2508–2520 (2014). PubMed
Brown, L. A., Williams, O. & Dash, J. Calibration and characterisation of four chlorophyll meters and transmittance spectroscopy for non-destructive estimation of forest leaf chlorophyll concentration. Agric. For. Meteorol.323, 109059 (2022).
Cerovic, Z. G., Masdoumier, G., Ghozlen, N. B. & Latouche, G. A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiol. Plantarum146, 251–260 (2012). PubMed PMC
Goulas, Y., Cerovic, Z. G., Cartelat, A. & Moya, I. Dualex: A new instrument for field measurements of epidermal ultraviolet absorbance by chlorophyll fluorescence. Appl. Opt.43, 4488 (2004). PubMed
Buschmann, C. Variability and application of the chlorophyll fluorescence emission ratio red/far-red of leaves. Photosynth. Res.92, 261–271 (2007). PubMed
Gitelson, A. A., Buschmann, C. & Lichtenthaler, H. K. The chlorophyll fluorescence ratio F735/F700 as an accurate measure of the chlorophyll content in plants. Remote Sens. Environ.69, 296–302 (1999).
Verrelst, J. et al. Quantifying vegetation biophysical variables from imaging spectroscopy data: A review on retrieval methods. Surv. Geophys.40, 589–629 (2019). PubMed PMC
Féret, J.-B. 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.231, 110959 (2019).
Serrano, L. Effects of leaf structure on reflectance estimates of chlorophyll content. Int. J. Remote Sens.29, 5265–5274 (2008).
Slaton, M. R., Hunt, E. R. & Smith, W. K. Estimating near-infrared leaf reflectance from leaf structural characteristics. Am. J. Botany88, 278–284 (2001). PubMed
Ustin, S. & Jacquemoud, S. How the Optical Properties of Leaves Modify the Absorption and Scattering of Energy and Enhance Leaf Functionality 349–384 (Springer, 2020). 10.1007/978-3-030-33157-3_14.
Baránková, B., Lazár, D. & Nauš, J. Analysis of the effect of chloroplast arrangement on optical properties of green tobacco leaves. Remote Sens. Environ.174, 181–196 (2016).
Fukushima, K. & Hasebe, M. Adaxial-abaxial polarity: The developmental basis of leaf shape diversity: Development and evolution of leaf types. Genesis52, 1–18 (2014). PubMed
Conklin, P. A., Strable, J., Li, S. & Scanlon, M. J. On the mechanisms of development in monocot and eudicot leaves. New Phytol.221, 706–724 (2019). PubMed
Marenco, R. A., Antezana-Vera, S. A. & Nascimento, H. C. S. Relationship between specific leaf area, leaf thickness, leaf water content and SPAD-502 readings in six Amazonian tree species. Photosynthesis47, 184–190 (2009).
Coste, S. et al. Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: A calibration test with thirteen tree species of tropical rainforest in French Guiana. Ann. For. Sci.67, 607–607 (2010).
Evans, J., Caemmerer, S., Setchell, B. & Hudson, G. The relationship between CO2 transfer conductance and leaf anatomy in transgenic tobacco with a reduced content of rubisco. Funct. Plant Biol.21, 475 (1994).
Aasamaa, K., Niinemets, Ü. & Sõber, A. Leaf hydraulic conductance in relation to anatomical and functional traits during Populus tremula leaf ontogeny. Tree Physiol.25, 1409–1418 (2005). PubMed
Jifon, J. L., Syvertsen, J. P. & Whaley, E. Growth environment and leaf anatomy affect nondestructive estimates of chlorophyll and nitrogen in Citrus sp. Leaves. J. Am. Soc. Hortic. Sci.130, 152–158 (2005).
Nauš, J., Prokopová, J., Řebíček, J. & Špundová, M. SPAD chlorophyll meter reading can be pronouncedly affected by chloroplast movement. Photosynth. Res.105, 265–271 (2010). PubMed
McClendon, J. H. & Fukshansky, L. On the interpretation of absorption spectra of leaves–II. The non-absorbed ray of the sieve effect and the mean optical pathlength in the remainder of the leaf. Photochem. Photobiol.51, 211–216 (1990).
Davis, P. A., Caylor, S., Whippo, C. W. & Hangarter, R. P. Changes in leaf optical properties associated with light-dependent chloroplast movements: Chloroplast movement and leaf optics. Plant Cell Environ.34, 2047–2059 (2011). PubMed
McClendon, J. H. & Fukshansky, L. On the interpretation of absorption spectra of leaves–I. Introduction and the correction of leaf spectra for surface reflection. Photochem. Photobiol.51, 203–210 (1990).
Uddling, J., Gelang-Alfredsson, J., Piikki, K. & Pleijel, H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth. Res.91, 37–46 (2007). PubMed
Dong, T. et al. Assessment of portable chlorophyll meters for measuring crop leaf chlorophyll concentration. Remote Sens.11, 2706 (2019).
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.246, 111862 (2020).
Bercu, R. Anatomical aspects of Ficus lyrata Warb. (Moraceae) leaf. Annals of West University of Timişoara, ser. Biology (2015).
Bercu, R. Some general anatomical aspects of Ficus benjamina L. cv. Danielle (Moraceae) leaf. (Annals of the University of Craiova - Agriculture, Montanology, Cadastre Series 43–49 (2016).
Falcioni, R. et al. High resolution leaf spectral signature as a tool for foliar pigment estimation displaying potential for species differentiation. J. Plant Physiol.249, 153161 (2020). PubMed
Zar, J. H. Biostatistical Analysis (Prentice-Hall/Pearson, 2010).
Dong, R. et al. Estimating plant nitrogen concentration of maize using a leaf fluorescence sensor across growth stages. Remote Sens.12, 1139 (2020).
Ghosh, M., Swain, D. K., Jha, M. K., Tewari, V. K. & Bohra, A. Optimizing chlorophyll meter (SPAD) reading to allow efficient nitrogen use in rice and wheat under rice-wheat cropping system in eastern India. Plant Prod. Sci.23, 270–285 (2020).
Padilla, F. M. et al. Influence of time of day on measurement with chlorophyll meters and canopy reflectance sensors of different crop N status. Precis. Agric.20, 1087–1106 (2019).
Arellano, P., Tansey, K., Balzter, H. & Boyd, D. S. Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest. Environ. Earth Sci.76, 217 (2017).
Colzi, I. et al. Impact of microplastics on growth, photosynthesis and essential elements in Cucurbita pepo L. J. Hazard. Mater.423, 127238 (2022). PubMed
Zhen, J. et al. Mapping leaf chlorophyll content of mangrove forests with Sentinel-2 images of four periods. Int. J. Appl. Earth Observ. Geoinf.102, 102387 (2021).
Cary, K. L. & Pittermann, J. Small trees, big problems: Comparative leaf function under extreme edaphic stress. Am. J. Botany105, 50–59 (2018). PubMed
Huemmrich, K. F. et al. Leaf-level chlorophyll fluorescence and reflectance spectra of high latitude plants. Environ. Res. Commun.4, 035001 (2022).
Darvishzadeh, R. et al. Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model. Int. J. Appl. Earth Observ. Geoinf.79, 58–70 (2019).
Fiorentini, M. et al. Nitrogen and chlorophyll status determination in durum wheat as influenced by fertilization and soil management: Preliminary results. PLoS ONE14, e0225126 (2019). PubMed PMC
Červená, L. et al. Determination of chlorophyll content in selected grass communities of Krkonoše Mts. tundra based on laboratory spectroscopy and aerial hyperspectral data.. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.XLIII-B3-2022, 381–388 (2022).
Ludwig, A. D., Doktor, D., Goss, R., Sasso, S. & Feilhauer, H. The leaf is always greener on the other side of the lab: Optical in-situ indicators for leaf chlorophyll content need improvement for semi-natural grassland areas. Ecol. Indic.143, 109424 (2022).
McClendon, J. H. The micro-optics of leaves. I. Patterns of reflection from the epidermis. Am. J. Botany71, 1391–1397 (1984).
Liu, S. Comparison of two noninvasive methods for measuring the pigment content in foliose macrolichens. Photosynth. Res.141, 245–257 (2019). PubMed
Bachofen, C., D’Odorico, P. & Buchmann, N. Light and VPD gradients drive foliar nitrogen partitioning and photosynthesis in the canopy of European beech and silver fir. Oecologia192, 323–339 (2020). PubMed
Hoeppner, J. M. et al. Mapping canopy chlorophyll content in a temperate forest using airborne hyperspectral data. Remote Sens.12, 3573 (2020).
Lai, Y. et al. Bidirectional reflectance factor measurement of conifer needles with microscopic spectroscopy imaging. Agric. For. Meteorol.330, 109311 (2023).
Ren, J. et al. Tree growth response to soil nutrients and neighborhood crowding varies between mycorrhizal types in an old-growth temperate forest. Oecologia197, 523–535 (2021). PubMed
Olascoaga, B., Mac Arthur, A., Atherton, J. & Porcar-Castell, A. A comparison of methods to estimate photosynthetic light absorption in leaves with contrasting morphology. Tree Physiol36, 368–379 (2016). PubMed PMC
Malenovský, Z. et al. Applicability of the PROSPECT model for Norway spruce needles. Int. J. Remote Sens.27, 5315–5340 (2006).
Mesarch, M. A., Walter-Shea, E. A., Asner, G. P., Middleton, E. M. & Chan, S. S. A revised measurement methodology for conifer needles spectral optical properties: Evaluating the influence of gaps between elements. Remote Sens. Environ.177, 192 (1999).
Yáñez-Rausell, L., Malenovsky, Z., Clevers, J. G. P. W. & Schaepman, M. E. Minimizing measurement uncertainties of coniferous needle-leaf optical properties. Part II: Experimental setup and error analysis. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.7, 406–420 (2014).
Čepl, J. et al. Heritable variation in needle spectral reflectance of Scots pine (Pinus sylvestris L.) peaks in red edge. Remote Sens. Environ.219, 89–98 (2018).
Hejtmánek, J. et al. Revealing the complex relationship among hyperspectral reflectance, photosynthetic pigments, and growth in Norway spruce ecotypes. Front. Plant Sci.13, 721064 (2022). PubMed PMC
Einzmann, K. et al. Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria Germany. Remote Sens. Environ.266, 112676 (2021).
Zhang, Y., Wang, A., Li, J. & Wu, J. Water content estimation of conifer needles using leaf-level hyperspectral data. Front. Plant Sci.15, 1428212 (2024). PubMed PMC
Neuwirthová, E. et al. Leaf age matters in remote sensing: Taking ground truth for spectroscopic studies in hemiboreal deciduous trees with continuous leaf formation. Remote Sens.13, 1353 (2021).
Borsuk, A. M. & Brodersen, C. R. The spatial distribution of chlorophyll in leaves. Plant Physiol.180, 1406–1417 (2019). PubMed PMC
Jacquemoud, S. & Baret, F. PROSPECT: A model of leaf optical properties spectra. Remote Sens. Environ.34, 75–91 (1990).
Jiang, J., Comar, A., Weiss, M. & Baret, F. FASPECT: A model of leaf optical properties accounting for the differences between upper and lower faces. Remote Sens. Environ.253, 112205 (2021).
Shi, H., Jiang, J., Jacquemoud, S., Xiao, Z. & Ma, M. Estimating leaf mass per area with leaf radiative transfer model. Remote Sens. Environ.286, 113444 (2023).
Kallel, A. FluLCVRT: Reflectance and fluorescence of leaf and canopy modeling based on Monte Carlo vector radiative transfer simulation. J. Quantit. Spectrosc. Radiat. Transf.253, 107183 (2020).
Kallel, A. Leaf polarized BRDF simulation based on Monte Carlo 3-D vector RT modeling. J. Quantit. Spectrosc. Radiat. Transf.221, 202–224 (2018).
Théroux-Rancourt, G. et al. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning. Appl. Plant Sci.8, e11380 (2020). PubMed PMC
Borsuk, A. M., Roddy, A. B., Théroux-Rancourt, G. & Brodersen, C. R. Structural organization of the spongy mesophyll. New Phytol.234, 946–960 (2022). PubMed PMC
Casa, R., Castaldi, F., Pascucci, S. & Pignatti, S. Chlorophyll estimation in field crops: An assessment of handheld leaf meters and spectral reflectance measurements. J. Agric. Sci.153, 876–890 (2015).
Neuwirthová, E. et al. Asymmetry of leaf internal structure affects PLSR modelling of anatomical traits using VIS-NIR leaf level spectra. Eur. J. Remote Sens.57, 2292154 (2024).
Švik, M. et al. Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods. Int. J. Remote Sens.44, 3083–3105 (2023).
Hunt, L. et al. Leaf functional traits in relation to species composition in an arctic-alpine tundra grassland. Plants12, 1001 (2023). PubMed PMC
Lhotáková, Z. et al. Foliage biophysical trait prediction from laboratory spectra in norway spruce is more affected by needle age than by site soil conditions. Remote Sens.13, 391 (2021).
Markwell, J., Osterman, J. C. & Mitchell, J. L. Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynth. Res.46, 467–472 (1995). PubMed
Wellburn, A. R. The spectral determination of chlorophylls a and b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. J. Plant Physiol.144(3), 307–313 (1994).
Mancinelli, A. L., Yang, C.-P.H., Lindquist, P., Anderson, O. R. & Rabino, I. Photocontrol of anthocyanin synthesis. Plant Physiol.55(2), 251–257 (1975). PubMed PMC
R Core Team. R: The R Project for Statistical Computing,R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. https://www.r-project.org/ (2021).
Tang, Y., Horikoshi, M. & Li, W. ggfortify: Unified interface to visualize statistical results of popular R packages. R J.8, 474 (2016).