Determination of nitrogen and chlorophyll content in two varieties of winter wheat plants means of ground and airborne spectrometry
DOI:
https://doi.org/10.36023/ujrs.2020.26.178Keywords:
vegetation indices, total nitrogen content, chlorophyll content, UAV, ground spectrometric survey, winter wheat cropsAbstract
Nitrogen in plants is part of the green pigment chlorophyll, as well as proteins, nucleic acids, phytohormones and alkaloids that indicates the key role of this element in plant life. Chlorophyll is the most important pigment of the photosynthetic process determining the life of all heterotrophic organisms on the planet. The facts mentioned above presuppose close relationships between nitrogen and chlorophyll in plants. The nitrogen content in plants serves as a basis for adjusting their nitrogen nutrition and calculating fertilization rates for high yields. This causes comstant importance of studying the content of nitrogen and chlorophyll in plants, especially by means of novel techniques with involving remote sensing. This study was focused on relationship between 19 vegetation indices (VI) and biochemical characteristics of vegetation, in particular nitrogen and chlorophyll content. Study areas were located within production fields of two varieties of winter wheat grown for harvest in 2016 by the grain company Baryshivska. The test plots varied by phytopathological situation in the phase of milk ripeness. Fungal infection of Bogdana variety caused significant varietal differences in biochemical parameters that were calculated by Kjeldahl makro-method for total nitrogen and by aerial survey with UAV (drone) for chlorophyll content. Among 19 VIs calculated by ground spectrometry the major part (16 VIs) were consistent with changes in nitrogen and chlorophyll content in the cultivars. In particular, CI rededge , CI green , MTCI, RVI, D731 / D700 and D735 / D700 were more than doubled, and NDRE1 and D718 / D700 were almost 1.5 times higher in the Skagen variety compared to the Bogdan variety. Only 3 indices: NDVI, Green NDVI and NI had limits of fluctuations of the values within the same limits, as varietal differences of biochemical indicators.
References
Barnes, E.M., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R.J., Li, H., Moran, M.S. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. 5th International Conference on Precision Agriculture, Bloomington, 16-19 July 2000, 1-15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.463.8007&rep=rep1&type=pdf
Dash J., Curran P.J. (2004) The MERIS terrestrial chlorophyll index. Int. Journal of Remote Sensing. 2004. 25. P. 5403-5413 https://doi.org/10.1080/0143116042000274015
Device of express diagnostics "N-tester" - "eyes" of an agronomist in nitrogen nutrition. (2012) Agricultural Technique and Equipment. 1(18), 30-32 Retrieved from http://www.agritech.com.ua/pdf/1(18)03_2012/ (in Ukrainian)
Dugin, S. S., Sybirtseva, O. M., Golubov, S. I., Dorofey, Ye. M. (2019). Verification of multispectral data processing for the Sentinel-2A bands, field and FieldSpec® 3FR and UAV with DJI STS –VIS. Ukrajinsjkyj zhurnal dystancijnogho zonduvannja Zemli. 21, 29–39. (in Ukrainian). https://doi.org/10.36023/ujrs.2019.21.147
Filella, I.; Serrano, L.; Serra, J.; Peñuelas, J. (1995) Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 1995, 35, 1400–1405. https://doi.org/10.2135/cropsci1995.0011183X003500050023x
Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N. (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment. – 1996. – V. 58, № 3. – P. 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7
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. – 2006.–Letters 33, L11402. https://doi.org/10.1029/2006GL026457
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, 286–292. https://doi.org/10.1016/S0176-1617(11)81633-0
Gitelson, A., Solovchenko, A. (2017). Generic algorithms for estimating foliar pigment content. Geophysical Research Letters, 44. 9293–9298. https://doi.org/ 10.1002/2017GL074799
Gitelson, A.A. (2018) Hyperspectral Remote Sensing of Vegetation, (Thenkabail, P.S., Lyon, J.G., Huete, A., Eds), Chapter 1, Vol. III, pp. 3-24. CRC Press- Taylor and Francis group, Boca Raton, London, New York. ISBN-13: 978-1138066250
Hallik, L., Kazantsev, T., Kuusk, A. Galmés J., Tomás M., Niinemets Ü. (2017). Generality of relationships between leaf pigment contents and spectral vegetation indices in Mallorca (Spain). Reg. Environ. Change 17, 2097–2109 https://doi.org/10.1007/s10113-017-1202-9
Horler D.N.H., Dockray M., Barber J. (1983) The red edge of plant leaf reflectance. Int. Journal of Remote Sensing. 1983. 4. P. 273-288. Retrieved from https://doi.org/10.1080/01431168308948546
Huete A. R. (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. – 1988.– V.25, N 3.– P.295-309. https://doi.org/10.1016/0034-4257(88)90106-X
Hunt Jr., E.R., Daughtry, C. S. T., Eitel, J. U. H., Long, D. S. (2011) Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index. Agronomy Journal. V. 103. 1090-1099. https://doi.org/10.2134/agronj2010.0395
Jiang, Z., Huete, A.R., Didan, K., Miura, T. (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ.2008. V. 112. P. 3833–3845 https://doi.org/10.1016/j.rse.2008.06.006
Jordan C. F. (1969) Derivation of Leaf‐Area Index from Quality of Light on the Forest Floor. Ecology, V.50, I.4. July 1969. Р. 663-666 https://doi.org/10.2307/1936256
Kazantsev, T., Shevchenko, V., Bondarenko, O., Furier, M., Samberg, A., Ametov, F., Iakovenko, V. (2018) COTS UAV-borne multispectral system for vegetation monitoring. Proc. of SPIE. 2018, 107830A, 1-10. DOI: 10.1117/12.2501859
Kochubey, S., Kazantsev, T. (2007) Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology, 2007, 164 (12), P. 1648-1655. DOI: 10.1016/j.jplph.2006.11.007
Kochubey, S., Kazantsev, T. (2012) Derivative vegetation indices as a new approach in remote sensing of vegetation. Frontiers of Earth Science, 2012, 6(2), P. 188-195. DOI: 10.1007/s11707-012-0325-z
Lyalko, V. I., Sakhatsky, O. I., Zholobak, G. M., Sybirtseva, O. M., Dugin, S. S., Vakolyuk, M. V. (2017) Analysis and comparison of vegetation indices of winter wheat crop areas, calculated on the basis of Sentinel-2 and FieldSpec spectroradiometer data. 12, 37–46. Ukrainskyi zhurnal dystantsiinoho zonduvannia Zemli. (in Ukrainian). https://doi.org/10.36023/ujrs.2017.12.94
Ma, B.L., Morrison, M.J., Dwyer L.M. (1996) Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of corn. Agronomy J. 88:915-920. https://doi.org/10.2134/agronj1996.00021962003600060011x
Nigon T.J., .Mulla D.J., Rosen C.J., CohenY.,Alchanatis V., Knight J., Rud R. (2015) Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars. Computers and Electronics in Agriculture 2015. V. 112, March 2015. P. 36-46 https://doi.org/10.1016/j.compag.2014.12.018
Rouse J.W., Jr., Haas R.H., Schell J.A., Deering D.W. (1973) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation // Prog. Rep. RSC 1978-1. – 1973. – 93 p. Retrieved from https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19750020419.pdf
Spirina, V. Z., Solovieva, T. P. (2014) Agrochemical methods for the study of soils, plants and fertilizers: tutorial. Tomsk: Publishing House of Tomsk State University. 336 р. (in Russian) Retrieved from http://vital.lib.tsu.ru/vital/access/services/Download/vtls:000491605/SOURCE1
Stroppiana, D., Boschetti, M., Brivio, P.A., Bocchi, S. (2006) Remotely sensed estimation of rice nitrogen concentration for forcing crop growth models. Italian Journal of Agrometeorology. 2006. № 3. P. 50-57/ Retrieved from https://pdfs.semanticscholar.org/25f3/656a35d440c0b94c3a045c767cc4df0a0c9a.pdf
Tian, Y.C., Yao, X., Yang, J., Cao, W. X., Hannaway, D. B., Zhu, Y. (2011) Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentrationwith ground- and space-based hyperspectral reflectance. Field Crops Res.– 2011.– V.120.– P.299–310. https://doi.org/10.1016/j.fcr.2010.11.002
Walsh, O.S., Shafian, S., Marshall, J.M., Jackson, C.,McClintick-Chess, J.R., Blanscet, S.M., Swoboda, K., Thompson, C., Belmont, K.M., Walsh, W.L. (2018) Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat. Advances in Remote Sensing. 2018. № 7. P.71-90. https://doi.org/10.4236/ars.2018.7200
Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H, Xu B, Yang X, Zhu D, Zhang X, Zhang R, Feng H, Zhao X, Li Z, Li H, Yang H. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front. Plant Sci. 8:1111. https://doi.org/10.3389/fpls.2017.01111
Zholobak, G. M., Sybirtseva, О. M., Vakolyuk, M. V., Romanciuc, I. F. (2018) Analysis of dynamics for 15 vegetation indices based on Sentinel-2A image data for the test sites of winter wheat crop different on the state from each other within the forest-steep zone in Ukraine Ukrainskyi zhurnal dystantsiinoho zonduvannia Zemli. 18, 32–39 (in Ukrainian). DOI: https://doi.org/10.36023/ujrs.2018.18.135
Zholobak, G. M., Sybirtseva, О. M., Vakolyuk, M. V., Zakharchyk, Yu. V. (2017) Remote monitoring of the state of winter wheat during the spring-summer vegetation of 2016 year, by using vegetatin indices of Sentinel-2A satellite (case study by forest-steppe area of Ukraine). Ukrainskyi zhurnal dystantsiinoho zonduvannia Zemli. 15. 23-30. (in Ukrainian). DOI: https://doi.org/10.36023/ujrs.2017.15.115
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