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Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables

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Abstract

This paper assesses the capability of hyperspectral remote sensing to estimate chlorophyll a (Chl a), chlorophyll b (Chl b), and chlorophyll a + b (Chl a + b) concentration at cherry leaf scale using a variety of spectral variables during the growth season. A field experiment was conducted in cherry orchard. The leaf reflectance spectra of cherry plants were acquired within 350–1050 nm wavelengths. A variety of spectral variables were mathematically computed based on the leaf spectra and transformation of reflectance spectra. The relationships between all of spectral variables and chlorophyll concentration were discussed. Estimating Chl a, Chl b, and Chl a + b concentration by stepwise linear regression method and curve estimation method were carried out. Results demonstrated that the best spectral variable for prediction of chlorophyll concentration was the new spectral variable (the first derivative of log (1/R741) and D751/D511), with the root mean square error prediction (RMSEP) of 4.802 mg L−1 for Chl a concentration, 1.659 mg L−1 for Chl b concentration, and 6.419 mg L−1 for Chl a + b concentration. It should be noted that spectral variables such as D715/D705, EBFR, D705/D722, and BND showed a good performance with the RMSEP of 5.768, 7.838, 12.146, and 14.437 mg L−1 for Chl a concentration, 1.795, 1.985, 1.765, and 3.164 mg L−1 for Chl b concentration, and 7.935, 11.49, 17.99, and 21.79 mg L−1 for Chl a + b concentration respectively. Further investigation is needed to evaluate the effectiveness of such techniques on other orchard varieties or at the canopy level.

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Abbreviations

Chl:

Chlorophyll

Chl a b, and a + b :

Chlorophyll a, b, and a + b.

References

  • Avtar, R., Takeuchi, W., & Sawada, H. (2013). Monitoring of biophysical parameters of cashew plants in Cambodia using ALOS/PALSAR data. Environmental Monitoring and Assessment, 185, 2023–2037.

    Article  Google Scholar 

  • Bian, M., Skidmore, A. K., Schlerf, M., Wang, T., Liu, Y., Zeng, R., & Fei, T. (2013). Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy level. ISPRS Journal of Photogrammetry and Remote Sensing, 78, 148–156.

    Article  Google Scholar 

  • Blackburn, G. A. (1998a). Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273–285.

    Article  Google Scholar 

  • Blackburn, G. A. (1998b). Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of Remote Sensing, 19(4), 657–675.

    Article  Google Scholar 

  • Blackburn, G. A. (2007). Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation. International Journal of Remote Sensing, 28(12), 2831–2855.

    Article  Google Scholar 

  • Botha, E. J., Leblon, B., Zebarth, B. J., & Watmough, J. (2010). Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion. International Journal of Remote Sensing, 31(7), 1679–1697.

    Article  Google Scholar 

  • Carter, G. A. (1994). Ratios of leaf reflectance in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 697–703.

    Article  Google Scholar 

  • Cheng, T., Rivard, B., Sanchez-Azofeifa, A. G., Feret, J.-B., Jacquemond, S., & Ustin, S. L. (2013). Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. Journal of Plant Physiology, 169, 1134–1142.

    Article  Google Scholar 

  • Cho, M. A., Skidmore, A. K., & Atzberger, C. (2008). Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) simulated data. International Journal of Remote Sensing, 29(8), 2241–2255.

    Article  Google Scholar 

  • Croft, H., Chen, J. M., Zhang, Y., & Simic, A. (2013). Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data. Remote Sensing of Environment, 133, 128–140.

    Article  Google Scholar 

  • Curran, P. J. (1989). Remote Sensing of Foliar Chemistry. Remote Sensing of Environment, 30, 271–278.

    Article  Google Scholar 

  • Curran, P. J., Dungan, J. L., & Peterson, D. L. (2001). Estimating the foliar biochemical concentration of leaves with the reflectance spectrometry Testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76, 349–359.

    Article  Google Scholar 

  • Datt, B. (1998). Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a + b and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66(2), 111–121.

    Article  Google Scholar 

  • Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20(14), 2741–2759.

    Article  Google Scholar 

  • Delegido, J., Fernandez, G., Gandia, S., & Moreno, J. (2008). Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data. International Journal of Remote Sensing, 29(24), 7107–7127.

    Article  Google Scholar 

  • Delegido, J., Verrelst, J., Meza, C. M., Rivera, J. P., Alonso, L., & Moreno, J. (2013). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy, 46, 42–52.

    Article  Google Scholar 

  • Demarez, V., Gastellu-etchegorry, J. P., Mougin, E., Marty, G., Proisy, C., Dufrene, E., & Le Dantec, V. (1999). Seasonal variation of leaf chlorophyll content of a temperate forest inversion of the PROSPECT model. International Journal of Remote Sensing, 20(5), 879–894.

    Article  Google Scholar 

  • Filella, L., & Penuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15(7), 1459–1470.

    Article  Google Scholar 

  • Gitelson, A. A., Merzilyak, M. N., & Lichtenthaler, H. K. (1996). Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 148, 501–508.

    Article  Google Scholar 

  • Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282.

    Article  Google Scholar 

  • Jacquemoud, S., & Baret, F. (1990). PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75–91.

    Article  Google Scholar 

  • Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M., & Jaggard, K. (1995). Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS data. Remote Sensing of Environment, 52, 163–172.

    Article  Google Scholar 

  • Jie-wen, Z., Kai-liang, W., Qin, O., & Quan-sheng, C. (2011). Measurement of chlorophyll content and distribution in tea plant’s leaf using hyperspectral imaging technique. Spectroscopy and Spectral Analysis, 31(2), 512–515.

    Google Scholar 

  • Kempeneer, P., Zarco-tejada, P. J., North, P. R. J., Debacker, S., Delalieux, S., Sepulcre-canto, G., Morales, F., Vanaardt, J. A. N., Sagardoy, R., Coppin, P., & Scheunders, P. (2008). Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery. International Journal of Remote Sensing, 29(s17–18), 5093–5111.

    Article  Google Scholar 

  • Kochubey, S. M., & Kazantsev, T. A. (2007). Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology, 164(12), 1648–1655.

    Article  Google Scholar 

  • Kokaly, R. F. (2001). Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 75, 153–161.

    Article  Google Scholar 

  • le Maire, G., Francois, C., & Dufrene, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89, 1–28.

    Article  Google Scholar 

  • Li, J., Jiang, J., Chen, Y., Wang, Y., Su, W., & Huang, W. (2007). Using hyperspectral indices to estimate foliar chlorophyll a concentrations of winter wheat under yellow rust stress. New Zealand Journal of Agricultural Research, 50, 1031–1036.

    Article  Google 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, 52–61.

    Article  Google Scholar 

  • Malenovsky, Z., Albrechtova, J., Lhotakova, Z., Zurita-milla, R., Clevers, J. G. P. W., Schaepman, M. E., & Cudlin, P. (2006). Applicability of the PROSPECT model for Norway spruce needles. International Journal of Remote Sensing, 27(24), 5315–5340.

    Article  Google Scholar 

  • Mc Murtey, J. E., III, Chappelle, E. W., Kim, M. S., Meisinger, J. J., & Corp, L. A. (1994). Distinguish nitrogen fertilization levels in field corns (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment, 47(1), 36–44.

    Article  Google Scholar 

  • Nichol, C. J., & Grace, J. (2010). Determination of leaf pigment content in Calluna vulgaris shoots from spectral reflectance. International Journal of Remote Sensing, 31(20), 5409–5422.

    Article  Google Scholar 

  • Panigada, C., Rossini, M., Busetto, L., Meroni, M., Fava, F., & Colombo, R. (2010). Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest. International Journal of Remote Sensing, 31(12), 3307–3332.

    Article  Google Scholar 

  • Penuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water- limited sunflower leaves. Remote Sensing of Environment, 48(2), 135–146.

    Article  Google Scholar 

  • Penuelas, J., Filella, I., & Baret, F. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221–230.

    Google Scholar 

  • Rotbart, N., Schmilovitch, Z., Cohen, Y., Alchanatis, V., Erel, R., Ignat, T., Shenderey, C., Dag, A., & Yermiyahu, U. (2013). Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance. Biosystems Engineering, 114, 426–434.

    Article  Google Scholar 

  • Schlemmer, M. R., Francis, D. D., Shanahan, J. F., & Schepers, J. S. (2005). Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agronomy Journal, 97(1), 106–112.

    Article  Google Scholar 

  • Simic, A., Chen, J. M., & Noland, T. L. (2011). Retrieval of forest chlorophyll content using canopy structure parameters derived from multi-angle data: the measurement concept of combining nadir hypespectral and off-nadir multispectral data. International Journal of Remote Sensing, 32(20), 5621–5644.

    Article  Google Scholar 

  • Sims, D. A., & Gamon, J. A. (2002). Relationship between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337–354.

    Article  Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices for determining agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158–182.

    Article  Google Scholar 

  • Thorp, K. R., Wang, G., West, A. L., Moran, M. S., Bronson, K. F., White, J. W., & Mon, J. (2012). Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models. Remote Sensing of Environment, 124, 224–233.

    Article  Google Scholar 

  • Villalobos, F. J., Orgaz, F., & Mateos, L. (1995). Non-destructive measurement of leaf area in olive (Olea europaea L.) trees using a gap inversion method. Agricultural and Forest Meteorology, 73, 29–42.

    Article  Google Scholar 

  • Wu, C., Han, X., Niu, Z., & Dong, J. (2010). An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation. International Journal of Remote Sensing, 31(4), 1079–1086.

    Article  Google Scholar 

  • Xue, L., & Yang, L. (2009). Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 97–106.

    Article  Google Scholar 

  • Yoder, B. J., & Pettigrew-Crosby, R. E. (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sensing of Environment, 53, 199–211.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., & Sampson, P. H. (2001). Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1491–1507.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., & Sampson, P. H. (2002). Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on Hyperspectral imagery. Journal of Environmental Quality, 31, 1433–1441.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjon, A., & Aguera, J. (2004). Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment, 90, 463–476.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Guillen-climent, M. L., Hernandez-Clemente, R., Catalina, A., Gonzalez, M. R., & Martin, P. (2013). Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Metereology, 171–172, 281–294.

    Article  Google Scholar 

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Acknowledgments

This research was financially supported by a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and was also subsidized by major research projects of the National Natural Science Fund (Project 91025022). The authors wish to thank Dr. Rui Zhang at Huaihai Institute of Technology for his invaluable assistance during the fieldwork in China.

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  1. Xia Lu

    Present address: , 540 Harley Dr. Apt.3, Columbus, OH, USA

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  1. School of Geodesy & Geomatics Engineering, Huaihai Institute of Technology, Lianyungang, Jiangsu province, 222005, China

    Xia Lu & Hongchun Peng

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Correspondence to Xia Lu.

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Lu, X., Peng, H. Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables. J Indian Soc Remote Sens 43, 109–120 (2015). https://doi.org/10.1007/s12524-014-0397-1

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