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

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dc.audienceInvestigador
dc.audienceProfesional
dc.creatorRamos García, Carlos Arturo
dc.creatorMartínez Martínez, Luis Joel
dc.creatorBernal Riobo, Jaime Humberto
dc.date2023-12-13T19:46:47Z
dc.date2023-12-13T19:46:47Z
dc.date2022
dc.date2022
dc.date.accessioned2026-06-27T04:35:33Z
dc.descriptionThe 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.
dc.descriptionMaíz
dc.descriptionZea mays
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.identifierhttps://revistas.uptc.edu.co/index.php/ciencias_horticolas/article/view/13398
dc.identifier2422-3719
dc.identifierhttp://hdl.handle.net/20.500.12324/38685
dc.identifierhttps://doi.org/10.19053/rcch.2022v16i1.13398
dc.identifierreponame:Biblioteca Digital Agropecuaria de Colombia
dc.identifierinstname:Corporación colombiana de investigación agropecuaria AGROSAVIA
dc.identifier.urihttp://hdl.handle.net/123456789/32392
dc.languagespa
dc.publisherUniversidad Pedagógica y Tecnológica de Colombia - UPTC
dc.relationRevista Colombiana de Ciencias Hortícolas
dc.relation16
dc.relation1
dc.relation1
dc.relation15
dc.relationBarker, A.V. and D.J. Pilbeam (eds.). 2015. Handbook of plant nutrition. 2nd ed. CRC Press, Boca Raton, FL. Doi: 10.1201/b18458
dc.relationBarnes, 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
dc.relationCampuzano 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
dc.relationChen, 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
dc.relationCho, 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
dc.relationColombia IGAC, Instituto Geográfico Agustín Codazzi. 2004. Estudio general de suelos y zonificación de tierras, departamento de Meta. Bogota
dc.relationCroft, 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
dc.relationDaughtry, 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
dc.relationDel 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: https://www.nue.okstate. edu/Pocket_Sensor/Pocket_Sensor.htm; consulted: May, 2021
dc.relationDwyer, 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
dc.relationElmetwalli, 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
dc.relationFeng, 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
dc.relationGiraldo-Betancourt, C., E.A. Velandia-Sánchez, G. Fischer, S. Gómez-Caro, and L.J. Martínez. 2020. Hyperspectral response of cape gooseberry (Physalis peruviana L.) plants inoculated with Fusarium oxysporum f. sp. physali for early detection. Rev. Colomb. Cienc. Hortíc. 14(3), 301-313. Doi: 10.17584/rcch.2020v14i3.10938
dc.relationGitelson, 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
dc.relationGitelson, 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
dc.relationGuyot, 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. 4th Conf. Spectral Signatures of Objects in Remote Sensing. European Space Agency, Aussois, France.
dc.relationInskeep, 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
dc.relationKleinbaum, D.G., L.L. Kupper, and A. Nizam. 2014. Applied regression analysis and other multivariable methods. 5th ed. Cengage Learning, Boston, MA.
dc.relationLee, 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. Botanical Studies 52(3), 295-303.
dc.relationLi, 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
dc.relationMartínez, 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
dc.relationMartínez, 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
dc.relationMiller, 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
dc.relationMyneni, 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
dc.relationPalka, 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
dc.relationPeñuelas, 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
dc.relationPu, 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
dc.relationRanjan, 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
dc.relationSchlemmer, 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
dc.relationSchlemmer, 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
dc.relationSavitzky, 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
dc.relationSerrano, 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
dc.relationSims, 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
dc.relationThompson, 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, Switzerland. Doi: 10.1007/978-3-319-53626-2_2
dc.relationWan, 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
dc.relationWang, 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
dc.relationWen, 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
dc.relationWu, 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
dc.relationYu, 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
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceRevista Colombiana de Ciencias Hortícolas; Vol. 16 (2022): Revista Colombiana de Ciencias Hortícolas;p. 1 -15.
dc.subjectCultivo - F01
dc.subjectReflectancia
dc.subjectMaíz
dc.subjectCultivo
dc.subjectNutrición de las plantas
dc.subjectTransitorios
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_28538
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_12332
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_2018
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_16379
dc.thumbnailhttps://repository.agrosavia.co/bitstreams/e63898e9-0f55-4350-90ae-b2c82d1a2ec1/download
dc.titleEstimating chlorophyll and nitrogen contents in maize (Zea mays L.) leaves with spectroscopic analysis
dc.typeArtículo científico

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