Estimating chlorophyll and nitrogen contents in maize leaves (Zea mays L.) with spectroscopic analysis




Reflectance, Spectroradiometry, Crops, Colombia, Plant nutrition


The relationship between reflectance and chlorophyll (Chl) and nitrogen (N) contents in maize leaves was analyzed to identify useful spectral indices for diagnosing the nutritional status of crops in terms of N. An experiment was carried out in random blocks with five treatments of nitrogen fertilizer (0, 50, 100, 150, 200 kg ha-1) and four repetitions and the foliar spectral responses were measured with a FieldSpec 4 spectroradiometer in three phenological stages. Several spectral indices and values of red edge position (REP) were calculated using various methods. Red-edge position linear interpolation (REP-L), Red-edge position linear extrapolation (REP-LE), REP-Inverted Gaussian fitting technique (REP-IG), REP-Polynomial fitting technique (REP-P) and NDVI had the best relationship with chlorophyll and nitrogen contents. The first derivative of reflectance, between 560 and 760 nm, transformed by the normal state variable (SNV) also had highly significant correlation coefficients with the N, Chl, and yield. Additionally, the corn yield showed highly significant correlations with the N and Chl contents. From the point of view of the diagnosis of the nutritional status of corn, the spectral indices and REP values were suitable for establishing the nutritional status of corn in relation to N in the phenological stages V8 and R1.


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Barker, A.V. and D.J. Pilbeam. (eds.). 2015. Handbook of plant nutrition. 2nd ed. CRC Press, Boca Raton, FL. Doi: 10.1201/b18458

Barnes, R., M. Dhanoa, and S. Lister. 1993. Letter: Correction to the description of Standard Normal Variate (SNV) and De-Trend (DT) Transformations in practical spectroscopy with applications in food and beverage analysis. J. Near Infrar. Spectros. 1(1), 185. Doi: 10.1255/jnirs.21

Campuzano Duque, L.F., S. Caicedo Guerrero, L. Narro, and A. Herbin. 2014. Corpoica H5: primer híbrido de maíz amarillo de alta calidad de proteína (QPM) para la altillanura plana colombiana. Corpoica Cienc. Tecnol. Agropecu. 15(2), 173-182. Doi: 10.21930/rcta.vol15_num2_art:357

Chen, P., D. Haboudane, N. Tremblay, J. Wang, P. Vigneault, and B. Li. 2010. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens. Environ. 114(2), 1987-1997. Doi: 10.1016/j.rse.2010.04.006

Cho, M.A. and A.K. Skidmore. 2006. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens. Environ. 101(2), 181-193. Doi: 10.1016/j.rse.2005.12.011

Colombia IGAC, Instituto Geográfico Agustín Codazzi. 2004. Estudio general de suelos y zonificación de tierras, departamento de Meta. Bogota.

Croft, H., J.M. Chen, and Y. Zhang. 2014. The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecol. Complex. 17, 119-130. Doi: 10.1016/j.ecocom.2013.11.005

Daughtry, C.S.T., C.L. Walthall, M.S. Kim, E. Brown de Colstoun, and J.E. McMurtrey III. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74(2), 229-239. Doi: 10.1016/S0034-4257(00)00113-9

Del Corso, M., R.P. Lollato, N. Macnack, J. Mullock, and B.R. Raun. 2013. Evaluation of trimble hand held crop sensor and GreenseekerTM: sensors at different heights and for various crops. In:; consulted: May, 2021.

Dwyer, L.M., D.W. Stewart, E. Gregorich, A.M. Anderson, B.L. Ma, and M. Tollenaar. 1995. Quantifying the nonlinearity in chlorophyll meter response to corn leaf nitrogen concentration. Can. J. Plant Sci. 75, 179-182. Doi: 10.4141/cjps95-030

Elmetwalli, A.H. and A.N. Tyler. 2020. Estimation of maize properties and differentiating moisture and nitrogen deficiency stress via ground – Based remotely sensed data. Agric. Water Manage. 242, 106413. Doi: 10.1016/j.agwat.2020.106413

Feng, W., X. Yao, Y. Zhu, Y.C. Tian, and W.X. Cao. 2008. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Eur. J. Agron. 28(3), 394-404. Doi: 10.1016/j.eja.2007.11.005

Gitelson, A.A., M.N. Merzlyak, and H.K. Lichtenthaler. 1996. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. J. Plant Physiol. 148(3-4), 501-508. Doi: 10.1016/S0176-1617(96)80285-9

Gitelson, A.A., A. Viña, V. Ciganda, D.C. Rundquist, and T.J. Arkebauer. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32(8), L08403. Doi: 10.1029/2005GL022688

Guyot, G. and F. Baret. 1988. Utilisation de la haute resolution spectrale pour suivre L’etat des couverts vegetaux. pp. 279-286. In: Guyenne, T.D. and J.J. Hunt (eds.). Proc. Conf. 4th Spectral Signatures of Objects in Remote Sensing. European Space Agency, Aussois, France.

Inskeep, W.P. and P.R. Bloom. 1985. Extinction coefficients of chlorophyll a and b in N,N-dimethylformamide and 80% acetone. Plant Physiol. 77(2), 483-485. Doi: 10.1104/pp.77.2.483

Kleinbaum, D.G., L.L. Kupper, and A. Nizam. 2014. Applied regression analysis and other multivariable methods. 5rd ed. Cengage Learning, Boston, MA.

Lee, Y. J., C.M. Yang, K.W. Chang, and Y. Shen. 2011. Effects of nitrogen status on leaf anatomy, chlorophyll content and canopy reflectance of paddy rice. Bot. Stud. 52(3), 295-303.

Li, F., Y. Miao, G. Feng, F. Yuan, S. Yue, X. Gao, Y. Liu, B. Liu, S.L. Ustin, and X. Chen. 2014. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crop. Res. 157, 111-123. Doi: 10.1016/j.fcr.2013.12.018

Martínez, L.J. 2017. Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agron. Colomb. 35(2), 205-215. Doi: 10.15446/agron.colomb.v35n2.62875

Martinez, L.J. and A. Ramos. 2015. Estimation of chlorophyll concentration in maize using spectral reflectance. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 40-7/W3, 65-71. Doi: 10.5194/isprsarchives-XL-7-W3-65-2015

Miller, J.R., E.W. Hare, and J. Wu. 1990. Quantitative characterization of the vegetation red edge reflectance 1. An inverted-Gaussian reflectance model. Int. J. Remote Sens. 11(10), 1755-1773. Doi: 10.1080/01431169008955128

Myneni, R.B. and D.L. Williams. 1994. On the relationship between FAPAR and NDVI. Remote Sens. Environ. 49(3), 200-211. Doi: 10.1016/0034-4257(94)90016-7

Palka, M., A.M. Manschadi, L. Koppensteiner, T. Neubauer, and G.F. Fitzgerald. 2021. Evaluating the performance of the CCCI-CNI index for estimating N status of winter wheat. Eur. J. Agron. 130, 126346. Doi: 10.1016/j.eja.2021.126346

Peñuelas, J., I. Filella, and J.A. Gamon. 1995. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol. 131, 291-296. Doi: 10.1111/j.1469-8137.1995.tb03064.x

Pu, R., P. Gong, G.S. Biging, and M.R. Larrieu. 2003. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index. IEEE Trans. Geosci. Remote Sens. 41(4), 916-921. Doi: 10.1109/TGRS.2003.813555

Ranjan, A.K. and B.R. Parida. 2020. Estimating biochemical parameters of paddy using satellite and near-proximal sensor data in Sahibganj Province, Jharkhand (India). Remote Sens. Appl.: Soc. Environ. 18, 100293. Doi: 10.1016/j.rsase.2020.100293

Schlemmer, M.R., D.D. Francis, J.F. Shanahan, and J.S. Schepers. 2005. Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agron. J. 97(1), 106-112. Doi: 10.2134/agronj2005.0106

Schlemmer, M., A. Gitelson, J. Schepers, R. Ferguson, Y. Peng, J. Shanahan, and D. Rundquist. 2013. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. Int. J. Appl. Earth Obs. Geoinf. 25, 47-54. Doi: 10.1016/j.jag.2013.04.003

Savitzky, A. and M.J. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analyt. Chem. 36(8), 1627-1639. Doi: 10.1021/ac60214a047

Serrano, L., J. Peñuelas, and S.L. Ustin. 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sens. Environ. 81(2-3), 355-364. Doi: 10.1016/S0034-4257(02)00011-1

Sims, D.A. and J.A. Gamon. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81, 337-354. Doi: 10.1016/S0034-4257(02)00010-X

Thompson, R.B., N. Tremblay, M. Fink, M. Gallardo, and F.M. Padilla. 2017. Tools and strategies for sustainable nitrogen fertilisation of vegetable crops. pp. 11-63. In: Tei, F., S. Nicola, and P. Benincasa (eds.), Advances in research on fertilization management of vegetable crops. Springer, Cham, Germany. Doi: 10.1007/978-3-319-53626-2_2

Wan, L., Z. Tang, J. Zhang, S. Chen, W. Zhou, and H. Cen. 2021. Upscaling from leaf to canopy: Improved spectral indices for leaf biochemical traits estimation by minimizing the difference between leaf adaxial and abaxial surfaces. Field Crops Res. 274, 108330. Doi: 10.1016/j.fcr.2021.108330

Wang, Z., J. Chen, J. Zhang, Y. Fan, Y. Cheng, B. Wang, X. Wu, X. Tan, T. Tan, S. Li, M.A. Raza, X. Wang, T. Yong, W. Liu, J. Liu, J. Du, Y. Wu, W. Yang, and F. Yang. 2021. Predicting grain yield and protein content using canopy reflectance in maize grown under different water and nitrogen levels. Field Crops Res. 260, 107988. Doi: 10.1016/j.fcr.2020.107988

Wen, P.-F., J. He, F. Ning, R. Wang, Y.-H. Zhang, and J. Li. 2019. Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique. Ecol. Indic. 107, 105590. Doi: 10.1016/j.ecolind.2019.105590

Wu, C., Z. Niu, Q. Tang, and W. Huang. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agric. For. Meteorol. 148(8-9), 1230-1241. Doi: 10.1016/j.agrformet.2008.03.005

Yu, K., V. Lenz-Wiedemann, X. Chen, and G. Bareth. 2014. Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects. ISPRS J. Photogramm. Remote Sens. 97, 58-77. Doi: 10.1016/j.isprsjprs.2014.08.005

Maize with 0 and 200 kg ha-¹ of nitrogen. Photo: L.J. Martínez




How to Cite

Ramos-García, C. A., Martínez-Martínez, L. J., & Bernal-Riobo, J. H. (2022). Estimating chlorophyll and nitrogen contents in maize leaves (Zea mays L.) with spectroscopic analysis. Revista Colombiana De Ciencias Hortícolas, 16(1), e13398.



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