Research Article
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Year 2020, Volume: 7 Issue: 3, 325 - 334, 06.12.2020
https://doi.org/10.30897/ijegeo.673038

Abstract

Supporting Institution

T.C Tarım ve Orman Bakanlığı Tarımsal Araştırmalar ve politikalar Genel Müdürlüğü

Project Number

Proje Adı : "Buğdayda Farklı Azot Uygulamalarının Verim ve Hiperspektral (Çok Bantlı) Yansıma Özellikleri Üzerine Etkilerinin Araştırılması" Proje No:: TAGEM/TSKAD/14/A13/P08/05

Thanks

Projeye katkılarından dolayı T.C Tarım ve Orman Bakanlığı Tarımsal Araştırmalar ve politikalar Genel Müdürlüğü'ne Teşekkürlerimizi sunarız.

References

  • AACC, C. (2000). Approved methods of the American association of cereal chemists. Methods, 54, 21.
  • Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L., & Royo, C. (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92 (1), 83-91.
  • Birth, G. S., & McVey, G. R. (1968). Measuring the Color of Growing Turf with a Reflectance Spectrophotometer 1. Agronomy Journal, 60 (6), 640-643.
  • Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area indice and canopy chlorophyll density. Remote sensing of environment, 76 (2), 156-172.
  • Curran, P. J. (1989). Remote sensing of foliar chemistry. Remote sensing of environment, 30(3), 271-278.
  • Carter, G. A., & Spiering, B. A. (2002). Optical properties of intact leaves for estimating chlorophyll concentration. Journal of environmental quality, 31 (5), 1424-1432.
  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment, 74 (2), 229-239.
  • Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote sensing of environment, 54(1), 38-48.
  • Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N., & Zucca, C. (2009). Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation, 11 (4), 233-243.
  • Fernandez, S., Vidal, D., Simon, E., & SOLl3-SUGRANES, L. (1994). Radiometric characteristics of Triticum aestivum cv, Astral under water and nitrogen stress. International Journal of Remote Sensing, 15(9), 1867-1884.
  • Gamon, J. A., Penuelas, J., & Field, C. B. (1992). A narrow - waveband spectral indices that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of environment, 41(1), 35-44.
  • Gitelson, A. A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of plant physiology, 148 (3-4), 494-500.
  • 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 (2-3), 416-426.
  • Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote sensing of environment, 86 (4), 542-553.
  • Huete, A. R. (1988). A soil-adjusted vegetation indice (SAVI). Remote sensing of environment, 25(3), 295-309.
  • Kahriman F., Demirel K., İnalpulat M., Egesel C.Ö., Genç L., (2016). Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize, Journal of Agricultural Science And Technology, 18,1705-1718.
  • Li, F., Gnyp, M. L., Jia, L., Miao, Y., Yu, Z., Koppe, W.& Zhang, F. (2008). Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Research, 106 (1), 77-85.
  • Li, F., Miao, Y., Hennig, S. D., Gnyp, M. L., Chen, X., Jia, L., & Bareth, G. (2010). Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture, 11 (4), 335-357.
  • Schepers, J. S., Hagopian, D. S., & Varvel, G. E. (1998, January). Monitoring crop stresses. In Illinois fertilizer conference proceedings (pp. 26-28).
  • Penuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31 (2), 221-230.
  • Pen Uelas, J., Filella, I., Lloret, P., MUN¯ OZ, F., & Vilajeliu, M. (1995). Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing, 16(14), 2727-2733.
  • Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote sensing of Environment, 51 (3), 375-384.
  • Rouse, J.W., Haas, R. H., Schell, J. A.and Deering, D. W. (1973). Monitoring vegetation system in great plains with ERTS. Proc. 3rd ERTS-1 Symp, GSFC, NASA, SP-351. pp.48-62.
  • Stroppiana, D., Boschetti, M., Brivio, P. A., & Bocchi, S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field crops research, 111 (1-2), 119-129.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8 (2), 127-150.
  • Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., & Briggs, J. M. (1999). Relationships between leaf area indice and Landsat TM spectral vegetation indices across three temperate zone sites. Remote sensing of environment, 70 (1), 52-68.
  • Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. Title Remote sensing, 14 (8), 1563-1575.
  • Wei, F., Yan, Z., Yongchao, T., Weixing, C., Xia, Y., & Yingxue, L. (2008). Monitoring leaf nitrogen accumulation in wheat with hyper-spectral remote sensing. Acta Ecologica Sinica, 28(1), 23-32.
  • Zarco-Tejada, P. J., Pushnik, J. C., Dobrowski, S., & Ustin, S. L. (2003). Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84(2), 283-294.

Evaluating Hyperspectral Vegetation Indices for Estimating Nitrogen Concentration of Winter Wheat in Different Growth Stages

Year 2020, Volume: 7 Issue: 3, 325 - 334, 06.12.2020
https://doi.org/10.30897/ijegeo.673038

Abstract

Plant health and plant density can be monitored through indices values calculated from reflectance charateristics of plants. This study aims to investigate the effects of different rates of nitrogen fertilizer on spectral reflectance characteristics of dryland winter wheat and to determine and select the most reliable vegetation indices for Nitrogen (N) status assesment (N contents of leaves) of winter wheat canopy in early, late and whole development stages. The field experiment was carried out in randomized block design with three replications and 0, 40, 80, 120, 160 kg/ha N doses applied between 2012-2013 years in İkizce Farm experiment field. In each plot, spectroradiometer readings were taken during growing period from planting to harvest various plant vegetation indices (NDVI, RDVI, SAVI, MTVI, MCARI-1, MCARI-2, TCARI, TVI, SIPI, NPCI, Red Edge (750-700), Red Edge (740-720)) were calculated from measured spectroradiometric values. A total of 90 different indices were calculated to obtain the relationship between nitrogen accumulation and hyperspectral indices (structural, chlorophyll pigment, red edge indices). The simple linear determination coefficients (R²) between those indices and leaf nitrogen contents in different development stages of early, late, and whole season were calculated. During the early period of tillering to bolting, (Feekes 4-7) NDI-1, NDI-2, SR-7, Red Edge indices (708-850 nm.) have the highest determination coefficients (R²) of 0.643, 0.641, 0.620 with RMSE values of 5.996, 6.039, 6.129 and relative percentage error values (%RE) of 23.07, 23.23, 23.58 % respectively. During the period of heading to ripening (Feekes 8-10), PhRI, NDV-3, NDVI-4 visible (Green Zone), red, red edge and near infrared (NIR) indices (531-800 nm.) showed the highest determination coefficients of 0.734, 0.708, 0.699 with RMS values of 3.089, 3.205, 3.149 and relative percentage error values of 40.37, 41.89, 41.16 % respectively. Considering all growing period in 2013 of tillering to ripening, ( Feekes 4-10); SR-14, SRPI, TVI visible area (blue + green) and Red Edge indices (415-750 nm.) have determination coefficients of 0.742, 0.699, 0.646 and RMS values 1.203, 0.902, 0.697 and relative percentage error values of 7.15, 5.36, 4.14 %, respectively. High determination coefficient (R²) between plant nitrogen uptake and reflectance charecteristics were attained as HVI (r= 0.806 p<0.0.1 **), OSAVI (r= 0.794, p<0.0.1**), NDVI (r= 0.794, p<0.0.1**) and HNDVI (r= 0.793, p<0.0.1**).

Project Number

Proje Adı : "Buğdayda Farklı Azot Uygulamalarının Verim ve Hiperspektral (Çok Bantlı) Yansıma Özellikleri Üzerine Etkilerinin Araştırılması" Proje No:: TAGEM/TSKAD/14/A13/P08/05

References

  • AACC, C. (2000). Approved methods of the American association of cereal chemists. Methods, 54, 21.
  • Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L., & Royo, C. (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92 (1), 83-91.
  • Birth, G. S., & McVey, G. R. (1968). Measuring the Color of Growing Turf with a Reflectance Spectrophotometer 1. Agronomy Journal, 60 (6), 640-643.
  • Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area indice and canopy chlorophyll density. Remote sensing of environment, 76 (2), 156-172.
  • Curran, P. J. (1989). Remote sensing of foliar chemistry. Remote sensing of environment, 30(3), 271-278.
  • Carter, G. A., & Spiering, B. A. (2002). Optical properties of intact leaves for estimating chlorophyll concentration. Journal of environmental quality, 31 (5), 1424-1432.
  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment, 74 (2), 229-239.
  • Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote sensing of environment, 54(1), 38-48.
  • Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N., & Zucca, C. (2009). Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation, 11 (4), 233-243.
  • Fernandez, S., Vidal, D., Simon, E., & SOLl3-SUGRANES, L. (1994). Radiometric characteristics of Triticum aestivum cv, Astral under water and nitrogen stress. International Journal of Remote Sensing, 15(9), 1867-1884.
  • Gamon, J. A., Penuelas, J., & Field, C. B. (1992). A narrow - waveband spectral indices that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of environment, 41(1), 35-44.
  • Gitelson, A. A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of plant physiology, 148 (3-4), 494-500.
  • 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 (2-3), 416-426.
  • Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote sensing of environment, 86 (4), 542-553.
  • Huete, A. R. (1988). A soil-adjusted vegetation indice (SAVI). Remote sensing of environment, 25(3), 295-309.
  • Kahriman F., Demirel K., İnalpulat M., Egesel C.Ö., Genç L., (2016). Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize, Journal of Agricultural Science And Technology, 18,1705-1718.
  • Li, F., Gnyp, M. L., Jia, L., Miao, Y., Yu, Z., Koppe, W.& Zhang, F. (2008). Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Research, 106 (1), 77-85.
  • Li, F., Miao, Y., Hennig, S. D., Gnyp, M. L., Chen, X., Jia, L., & Bareth, G. (2010). Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture, 11 (4), 335-357.
  • Schepers, J. S., Hagopian, D. S., & Varvel, G. E. (1998, January). Monitoring crop stresses. In Illinois fertilizer conference proceedings (pp. 26-28).
  • Penuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31 (2), 221-230.
  • Pen Uelas, J., Filella, I., Lloret, P., MUN¯ OZ, F., & Vilajeliu, M. (1995). Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing, 16(14), 2727-2733.
  • Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote sensing of Environment, 51 (3), 375-384.
  • Rouse, J.W., Haas, R. H., Schell, J. A.and Deering, D. W. (1973). Monitoring vegetation system in great plains with ERTS. Proc. 3rd ERTS-1 Symp, GSFC, NASA, SP-351. pp.48-62.
  • Stroppiana, D., Boschetti, M., Brivio, P. A., & Bocchi, S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field crops research, 111 (1-2), 119-129.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8 (2), 127-150.
  • Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., & Briggs, J. M. (1999). Relationships between leaf area indice and Landsat TM spectral vegetation indices across three temperate zone sites. Remote sensing of environment, 70 (1), 52-68.
  • Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. Title Remote sensing, 14 (8), 1563-1575.
  • Wei, F., Yan, Z., Yongchao, T., Weixing, C., Xia, Y., & Yingxue, L. (2008). Monitoring leaf nitrogen accumulation in wheat with hyper-spectral remote sensing. Acta Ecologica Sinica, 28(1), 23-32.
  • Zarco-Tejada, P. J., Pushnik, J. C., Dobrowski, S., & Ustin, S. L. (2003). Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84(2), 283-294.
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Metin Aydoğdu 0000-0001-6920-1976

Hakan Yıldız This is me 0000-0002-7627-7503

Ediz Ünal This is me 0000-0001-6463-2670

Seda Külen 0000-0002-6140-3079

Project Number Proje Adı : "Buğdayda Farklı Azot Uygulamalarının Verim ve Hiperspektral (Çok Bantlı) Yansıma Özellikleri Üzerine Etkilerinin Araştırılması" Proje No:: TAGEM/TSKAD/14/A13/P08/05
Publication Date December 6, 2020
Published in Issue Year 2020 Volume: 7 Issue: 3

Cite

APA Aydoğdu, M., Yıldız, H., Ünal, E., Külen, S. (2020). Evaluating Hyperspectral Vegetation Indices for Estimating Nitrogen Concentration of Winter Wheat in Different Growth Stages. International Journal of Environment and Geoinformatics, 7(3), 325-334. https://doi.org/10.30897/ijegeo.673038