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Chlorophyll estimation in field crops: an assessment of handheld leaf meters and spectral reflectance measurements

Published online by Cambridge University Press:  18 July 2014

R. CASA*
Affiliation:
Department of Agriculture Forestry Nature and Energy (DAFNE), Università degli Studi della Tuscia (DPV), Via San Camillo de Lellis, 01100 Viterbo, Italy
F. CASTALDI
Affiliation:
Department of Agriculture Forestry Nature and Energy (DAFNE), Università degli Studi della Tuscia (DPV), Via San Camillo de Lellis, 01100 Viterbo, Italy
S. PASCUCCI
Affiliation:
Consiglio Nazionale delle Ricerche – Institute of Methodologies for Environmental Analysis (C.N.R. – IMAA), Via del Fosso del Cavaliere 100, 00133 Roma, Italy
S. PIGNATTI
Affiliation:
Consiglio Nazionale delle Ricerche – Institute of Methodologies for Environmental Analysis (C.N.R. – IMAA), Via del Fosso del Cavaliere 100, 00133 Roma, Italy
*
*To whom all correspondence should be addressed. Email: rcasa@unitus.it

Summary

The widespread adoption by agronomists and researchers of handheld leaf chlorophyll meters stimulates enquiries on instrumental calibration issues, given the necessity, for some applications, of inferring actual chlorophyll concentrations from the readings provided. This is especially required for recently developed and more innovative devices such as the Dualex (Force-A, France), which unlike the more common SPAD-502 (Minolta, Japan) has not undergone extensive (published) calibration tests. Additionally, devices for spectral reflectance measurements are also becoming increasingly available. In the present paper, the calibration of SPAD on maize (Zea mays L.) and of Dualex on winter wheat (Triticum aestivum L.), durum wheat (Triticum durum Desf.), horse bean (Vicia faba L.) and maize, was compared to spectral reflectance indices and full spectral information (400–2500 nm) acquired by a spectroradiometer (ASD FieldSpec) equipped with a contact probe and leaf clip. Full spectral data were exploited using partial least squares regression (PLSR). The measurements were performed in the field at Maccarese (Central Italy) in 2012, gathering a specific experimental dataset. The calibration models obtained on experimental data for SPAD (on maize) and Dualex (on four crops) showed intermediate or high estimation accuracy with root-mean-square error (RMSE) values ranging between 7 and 11 μg/cm2 depending on the species. These results were slightly better than those achieved using spectral reflectance indices, which were inferior though to those provided by PLSR using full spectral resolution. A synthetic database, generated by the physically based PROSPECT model, simulating hemispherical leaf reflectance and transmittance, was used to compare the performances of the reflectance indices and the chlorophyll meters for a wider range of leaf properties. The results confirmed the substantial equivalence of reflectance-based and transmittance-based (i.e. simulated SPAD and Dualex) indices and the advantage of exploiting the full spectral information, e.g. through PLSR, if available.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2014 

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References

Alchanatis, V., Schmilovitch, Z. & Meron, M. (2005). In-field assessment of single leaf nitrogen status by spectral reflectance measurements. Precision Agriculture 6, 2539.CrossRefGoogle Scholar
Arregui, L. M., Lasa, B., Lafarga, A., Irañeta, I., Baroja, E. & Quemada, M. (2006). Evaluation of chlorophyll meters as tools for N fertilization in winter wheat under humid Mediterranean conditions. European Journal of Agronomy 24, 140148.CrossRefGoogle Scholar
Bannari, A., Khurshidt, K. S., Staenz, K. & Schwarz, J. W. (2007). A comparison of hyperspectral chlorophyll indices for wheat crop chlorophyll content estimation using laboratory reflectance measurements. IEEE Transactions on Geoscience and Remote Sensing 45, 30633074.CrossRefGoogle Scholar
Baret, F. & Fourty, T. (1997). Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements. Agronomie 17, 455464.CrossRefGoogle Scholar
Bousquet, L., Lachérade, S., Jacquemoud, S. & Moya, I. (2005). Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sensing of Environment 98, 201211.CrossRefGoogle Scholar
Carretero, R., Bancal, M. O. & Miralles, D. J. (2011). Effect of leaf rust (Puccinia triticina) on photosynthesis and related processes of leaves in wheat crops grown at two contrasting sites and with different nitrogen levels. European Journal of Agronomy 35, 237246.CrossRefGoogle Scholar
Cartelat, A., Cerovic, Z. G., Goulas, Y., Meyer, S., Lelarge, C., Prioul, J.-L., Barbottin, A., Jeuffroy, M.-H., Gate, P., Agati, G. & Moya, I. (2005). Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crops Research 91, 3549.CrossRefGoogle Scholar
Castelli, F., Contillo, R. & Miceli, F. (1996). Non-destructive determination of leaf chlorophyll content in four crop species. Journal of Agronomy and Crop Science 177, 275283.CrossRefGoogle Scholar
Cerovic, Z. G., Masdoumier, G., Ghozlen, N. B. & Latouche, G. (2012). A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiologia Plantarum 146, 251260.CrossRefGoogle ScholarPubMed
Chang, C. W., Laird, D. A., Mausbach, M. J. & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Science Society of America Journal 65, 480490.CrossRefGoogle Scholar
Ciganda, V., Gitelson, A. & Schepers, J. (2009). Non-destructive determination of maize leaf and canopy chlorophyll content. Journal of Plant Physiology 166, 157167.CrossRefGoogle ScholarPubMed
Comar, A., Baret, F., Viénot, F., Yan, L. & De Solan, B. (2012). Wheat leaf bidirectional reflectance measurements: description and quantification of the volume, specular and hot-spot scattering features. Remote Sensing of Environment 121, 2635.CrossRefGoogle Scholar
Coste, S., Baraloto, C., Leroy, C., Marcon, É., Renaud, A., Richardson, A. D., Roggy, J.-C., Schimann, H., Uddling, J. & Hérault, B. (2010). Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: a calibration test with thirteen tree species of tropical rainforest in French Guiana. Annals of Forest Science 67, 607607.CrossRefGoogle Scholar
Ercoli, L., Mariotti, M., Masoni, A. & Massantini, F. (1993). Relationship between nitrogen and chlorophyll content and spectral properties in maize leaves. European Journal of Agronomy 2, 113117.CrossRefGoogle Scholar
Feng, J., McGlone, A. V., Currie, M., Clark, C. J. & Jordan, B. R. (2011). Assessment of yellow-fleshed kiwifruit (Actinidia chinensis ‘Hort16A’) quality in pre- and post-harvest conditions using a portable near-infrared spectrometer. HortScience 46, 5763.CrossRefGoogle Scholar
Féret, J.-B., François, C., Asner, G. P., Gitelson, A. A., Martin, R. E., Bidel, L. P. R., Ustin, S. L., le Maire, G. & Jacquemoud, S. (2008). PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment 112, 30303043.CrossRefGoogle Scholar
Féret, J.-B., François, C., Gitelson, A., Asner, G. P., Barry, K. M., Panigada, C., Richardson, A. D. & Jacquemoud, S. (2011). Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment 115, 27422750.CrossRefGoogle Scholar
Fourty, T., Baret, F., Jacquemoud, S., Schmuck, G. & Verdebout, J. (1996). Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems. Remote Sensing of Environment 56, 104117.CrossRefGoogle Scholar
Gitelson, A. & Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology 143, 286292.CrossRefGoogle Scholar
Gitelson, A. A., Keydan, G. P. & Merzlyak, M. N. (2006). Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters 33, L11402. doi: 10.1029/2006GL026457.CrossRefGoogle Scholar
Gomez, C., Viscarra Rossel, R. A. & McBratney, A. B. (2008). Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: an Australian case study. Geoderma 146, 403411.CrossRefGoogle Scholar
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J. & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81, 416426.CrossRefGoogle Scholar
Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, A. & Schmuck, G. (1994). Leaf Optical Properties EXperiment 93 (LOPEX93). Report EUR 16095 EN. Luxembourg: European Commission.Google Scholar
Iman, R. L. & Conover, W. J. (1982). A distribution-free approach to inducing rank correlation among input variables. Communications in Statistics – Simulation and Computation 11, 311334.CrossRefGoogle Scholar
Imanishi, J., Nakayama, A., Suzuki, Y., Imanishi, A., Ueda, N., Morimoto, Y. & Yoneda, M. (2010). Nondestructive determination of leaf chlorophyll content in two flowering cherries using reflectance and absorptance spectra. Landscape and Ecological Engineering 6, 219234.CrossRefGoogle Scholar
Jacquemoud, S. & Baret, F. (1990). PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment 34, 7591.CrossRefGoogle Scholar
Jacquemoud, S., Ustin, S. L., Verdebout, J., Schmuck, G., Andreoli, G. & Hosgood, B. (1996). Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment 56, 194202.CrossRefGoogle Scholar
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., François, C. & Ustin, S. L. (2009). PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment 113 (Suppl. 1), S56S66.CrossRefGoogle Scholar
Lagarias, J. C., Reeds, J. A., Wright, M. H. & Wright, P. E. (1999). Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM Journal on Optimization 9, 112147.CrossRefGoogle Scholar
Lancashire, P. D., Bleiholder, H., van den Boom, T., Langeluddeke, P., Stauss, R., Weber, E. & Witzenberger, A. (1991). A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology 119, 561601.CrossRefGoogle Scholar
le Maire, G., François, C. & Dufrêne, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 89, 128.CrossRefGoogle Scholar
Lichtenthaler, H. K. (1987). Chlorophylls and carotenoids: pigments of photosynthetic biomembranes. Methods in Enzymology 148, 350382.CrossRefGoogle Scholar
Maccioni, A., Agati, G. & Mazzinghi, P. (2001). New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology B: Biology 61, 5261.CrossRefGoogle ScholarPubMed
Markwell, J., Osterman, J. C. & Mitchell, J. L. (1995). Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research 46, 467472.CrossRefGoogle ScholarPubMed
McKay, M. D., Beckman, R. J. & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239245.Google Scholar
Mevik, B. H. & Wehrens, R. (2007). The pls package: principal component and partial least squares regression in R. Journal of Statistical Software 18, 124.CrossRefGoogle Scholar
Mevik, B. H., Wehrens, R. & Liland, K. H. (2013). pls: Partial Least Squares and Principal Component Regression. R package version 2.4–3. Oslo: B. H. Mevik. http://CRAN.R-project.org/package=plsGoogle Scholar
Minasny, B. & McBratney, A. B. (2002). Uncertainty analysis for pedotransfer functions. European Journal of Soil Science 53, 417429.CrossRefGoogle Scholar
Minasny, B. & McBratney, A. B. (2008). Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometrics and Intelligent Laboratory Systems 94, 7279.CrossRefGoogle Scholar
Minocha, R., Martinez, G., Lyons, B. & Long, S. (2009). Development of a standardized methodology for quantifying total chlorophyll and carotenoids from foliage of hardwood and conifer tree species. Canadian Journal of Forest Research 39, 849861.CrossRefGoogle Scholar
Monje, O. A. & Bugbee, B. (1992). Inherent limitations of nondestructive chlorophyll meters: a comparison of two types of meters. HortScience 27, 6971.CrossRefGoogle ScholarPubMed
Nauš, J., Prokopová, J., Rebíček, J. & Spundová, M. (2010). SPAD chlorophyll meter reading can be pronouncedly affected by chloroplast movement. Photosynthesis Research 105, 265271.CrossRefGoogle ScholarPubMed
Pedrós, R., Goulas, Y., Jacquemoud, S., Louis, J. & Moya, I. (2010). FluorMODleaf: a new leaf fluorescence emission model based on the PROSPECT model. Remote Sensing of Environment 114, 155167.CrossRefGoogle Scholar
R Development Core Team (2011). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Richardson, A. D., Duigan, S. P. & Berlyn, G. P. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist 153, 185194.CrossRefGoogle Scholar
Ritchie, R. J. (2006). Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynthesis Research 89, 2741.CrossRefGoogle ScholarPubMed
Sánchez, M.-T., De La Haba, M. J., Benítez-López, M., Fernández-Novales, J., Garrido-Varo, A. & Pérez-Marín, D. (2012). Non-destructive characterization and quality control of intact strawberries based on NIR spectral data. Journal of Food Engineering 110, 102108.CrossRefGoogle Scholar
Shaver, T. M., Khosla, R. & Westfall, D. G. (2011). Evaluation of two crop canopy sensors for nitrogen variability determination in irrigated maize. Precision Agriculture 12, 892904.CrossRefGoogle Scholar
Terashima, I. & Saeki, T. (1983). Light environment within a leaf I. Optical properties of paradermal sections of camellia leaves with special reference to differences in the optical properties of palisade and spongy tissues. Plant and Cell Physiology 24, 14931501.CrossRefGoogle Scholar
Tremblay, N., Wang, Z. & Bélec, C. (2009). Performance of dualex in spring wheat for crop nitrogen status assessment, yield prediction and estimation of soil nitrate content. Journal of Plant Nutrition 33, 5770.CrossRefGoogle Scholar
Uddling, J., Gelang-Alfredsson, J., Piikki, K. & Pleijel, H. (2007). Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynthesis Research 91, 3746.CrossRefGoogle ScholarPubMed
Wallach, D. (2006). Evaluating crop models. In Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications (Eds Wallach, D., Makowski, D. & Jones, J. W.), pp. 1154. The Netherlands: Amsterdam, Elsevier.Google Scholar
Wellburn, A. R. (1994). The spectral determination of chlorophylls a and b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. Journal of Plant Physiology 144, 307313.CrossRefGoogle Scholar
Wood, C. W., Reeves, D. W., Duffield, R. R. & Edmisten, K. L. (1992). Field chlorophyll measurements for evaluation of corn nitrogen status. Journal of Plant Nutrition 15, 487500.CrossRefGoogle Scholar
Wright, S. W., Jeffrey, S. W. & Mantoura, F. R. C. (1997). Evaluation of methods and solvents for pigment extraction. In Phytoplankton Pigments in Oceanography: Guidelines to Modern Methods (Eds Jeffrey, S. W., Mantoura, R. F. C. & Wright, S. W.), pp. 261282. UNESCO Publication no. 10. Paris: UNESCO.Google Scholar
Wu, C., Niu, Z., Tang, Q. & Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agricultural and Forest Meteorology 148, 12301241.CrossRefGoogle Scholar
Yu, H., Wu, H. S. & Wang, Z. J. (2010). Evaluation of SPAD and Dualex for in-season corn nitrogen status estimation. Acta Agronomica Sinica 36, 840847.Google Scholar