Dergiler / International Journal of Environment and Geoinformatics / 2020 / Cilt: 7 - Sayı: 3

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

Sayfa
325–334
Yayın Tarihi
2022-10-19
DOI
—
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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**).

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Plant health and plant density can be monitored through indices values calculated from reflectance charateristics of plants.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı: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**).
🔬Tür:Araştırma Makalesi
📍Kapsam:2012-2013 Verileri
⚙️Değişkenler:Hyperspectral · spectroradiometer · vegetation indices

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**).

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Bu Makaleye Atıf Yap

YILDIZ, H., AYDOĞDU, M., ÜNAL, E., KÜLEN, S. (2022). Evaluating Hyperspectral Vegetation Indices for Estimating Nitrogen Concentration of Winter Wheat in Different Growth Stages. International Journal of Environment and Geoinformatics, Cilt: 7 - Sayı: 3, 325–334. https://ajindex.com/articles/540971

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Kaynakça (12)

  1. 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.
  2. Birth, G. S., & McVey, G. R. (1968). Measuring the Color of Growing Turf with a Reflectance Spectrophotometer 1. Agronomy Journal, 60 (6), 640-643.
  3. 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.
  4. Curran, P. J. (1989). Remote sensing of foliar chemistry. Remote sensing of environment, 30(3), 271-278.
  5. Carter, G. A., & Spiering, B. A. (2002). Optical properties of intact leaves for estimating chlorophyll concentration. Journal of environmental quality, 31 (5), 1424-1432.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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), 416-42