Skip to main content
SpringerLink
Log in

Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

This study was conducted to explore whether hyperspectral data could be used to discriminate between the effects of different rates of nitrogen application to a potato crop. The field experiment was carried out in the Central Potato Research Station, Jalandhar, on seven plots with different nitrogen (N) treatments. Spectral reflectance was measured using a 512-channel spectroradiometer with a range of 395–1075 nm on two different dates during crop growth. An optimum number of bands were selected from this range based on band–band r 2, principal component analysis and discriminant analysis. The four bands that could discriminate between the rates of N applied were 560, 650, 730, and 760 nm. An ANOVA analysis of several narrow-band indices calculated from the reflectance values showed the indices that were able to differentiate best between the different rates of N application. These were reflectance ratio at the red edge (R740/720) and the structure insensitive pigment index (SIPI). To estimate leaf N, reflectance ratios were determined for each band combination and were evaluated for their correlation with the leaf N content. A regression model for N estimation was obtained using the reflectance ratio indices at 750 and 710 nm wavelengths (F-ratio = 32 and r 2 = 0.551, P < 0.000).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Blackmer, T. M., Schepers, J. S., & Varvel, G. E. (1994). Light reflectance compared with other nitrogen stress measurements in corn leaves. Agronomy Journal, 86, 934–938.

    Article  Google Scholar 

  • Blackmer, T. M., Schepers, J. S., Varvel, G. E., & Walter-Shea, E. A. (1996). Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agronomy Journal, 88, 1–5.

    Article  CAS  Google Scholar 

  • Broge, N. H., & Leblanc, E. (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156–172.

    Article  Google Scholar 

  • Carter, G. A. (1998). Reflectance bands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sensing of Environment, 63, 61–72.

    Article  Google Scholar 

  • Chen, J. M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22, 229–242.

    Google Scholar 

  • Daughtry, C. H. T., Walthall, C. L., Kim, M. S., de Colstoun, E. B., & Mc-Murtrey, J. E. III (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239.

    Article  Google Scholar 

  • De Longueville, F., Tychon, B., Ledent, J. F., Foucart, G., Hoffmann, L., Touré, S. (2005). Hyperspectral derived nitrogen indicators for maize crop. http://telsat.belspo.be/docext/bruhyp2005/papers/Tychon.doc

  • Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54, 38–48.

    Article  Google Scholar 

  • Elvidge, C. D., Chen, Z., & Groeneveld, D. P. (1993). Detection of trace quantities of green vegetation in 1990 AVIRIS data. Remote Sensing of Environment, 44, 271–279.

    Article  Google Scholar 

  • FieldSpec®Pro. (2000). User’s guide manual release. Boulder, CO, USA: Analytical Spectral Devices Inc.

    Google Scholar 

  • Gitelson, A. A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing. Journal of Plant Physiology, 148, 493–500.

    Google Scholar 

  • Green, P. E., & Carroll, J. D. (1978). Analyzing multivariate data (p. 519). Illinois: The Dryden Press.

    Google Scholar 

  • Gupta, P. K. (1999). Soil, plant, water and fertilizer analysis (pp. 123–288). Jodhpur, India: Agrobios.

    Google Scholar 

  • Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337–352.

    Article  Google Scholar 

  • 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, 416–426.

    Article  Google Scholar 

  • Horler, D. N. H., Dockray, M., & Barber J. (1983). The red edge of plant reflectance. International Journal of Remote Sensing, 4, 273–288.

    Article  Google Scholar 

  • Huete, A. R. (1988). A soil adjusted vegetation index. Remote Sensing of Environment, 25, 295–309.

    Article  Google Scholar 

  • Janetos, A. C., & Justice, C. O. (2000). Land cover and global productivity: A measurement strategy for the NASA programme. International Journal of Remote Sensing, 21, 1491–1512.

    Article  Google Scholar 

  • Jaynes, D. B., Colvin, T. S., Karlen, D. L., Cambardella, C. A., & Meek, D. W. (2001). Nitrate loss in subsurface drainage as affected by nitrogen fertilizer rate. Journal of Environment Quality, 30, 1305–1314.

    CAS  Google Scholar 

  • Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50, 663–666.

    Article  Google Scholar 

  • Kumar, L., Schmidt, K. S., Dury, S., & Skidmore, A. K. (2001). Review of hyperspectral remote sensing and vegetation science. In F. Van Der Meer (Ed.), Hyperspectral remote sensing. Dordrecht: Kluwer Academic Press.

    Google Scholar 

  • Liang, S. (2004). Quantitative remote sensing of land surfaces. John Wiley & Sons, Inc.

  • McGwire, K., Minor, T., & Fenstermaker, L. (2000). Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. Remote Sensing of Environment, 72(3), 360–374.

    Article  Google Scholar 

  • Milap-Chand, Benbi, D. K., & Azad, A. S. (2004). Modifying soil test based fertilizer P recommendations for targeted yield of rice on Typic Haplustalf. Journal Indian Society of Soil Science, 52, 258–261.

  • Peñuelas, J., Baret, F., & Filella, I. (1995a). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31, 221–230.

    Google Scholar 

  • Peñuelas, J., Filella, I., Lloret, P., Munoz, F., & Vilajeliu, M. (1995b). Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing, 16, 2727–2733.

    Article  Google Scholar 

  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of the Environment, 48, 119–126.

    Article  Google Scholar 

  • Ramamoorthy, B., Narasimhan, R. L., & Dinesh, R. S. (1967). Fertilizer application for specific yield targets of Sanora-64 wheat. Indian Farming, 17, 43–45.

    Google Scholar 

  • Ray, S. S., Das, G., Singh, J. P, & Panigrahy, S. (2006). Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments. International Journal of Remote Sensing, 27, 5373–5387.

    Article  Google Scholar 

  • Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.

    Article  Google Scholar 

  • Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancements and retrogradation of natural vegetation. In: NASA/GSFC Final Report, NASA, Greenbelt, MD, USA, pp. 1–137.

  • SPSS. (1999). SPSS for Windows Release 10.0.5 (27 November 1999). SPSS Inc.

  • Strachan, I. B., Pattey, E., & Boisvert, J. B. (2002). Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 80, 213–214.

    Article  Google Scholar 

  • Thenkabail, P. S., Enclona, E. A., Ashton, M. S., & Van Der Meer, B. (2004). Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91, 354–376.

    Article  Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & Pauw, E. D. (2002). Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering and Remote Sensing, 68, 607–627.

    Google Scholar 

  • Vogelmann, J. E., Rock, B. N., & Miller, J. R. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14, 1563–1575.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H., & Sampson, P. H. (2001). Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39, 1491–1507.

    Article  Google Scholar 

  • Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, S. (2005). Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agronomy Journal, 97, 89–98.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The work is carried out under the “Precision Farming” project of Space Applications Centre (SAC). Authors are grateful to Dr R R Navalgund, Director, SAC, and Dr J S Parihar, Deputy Director, RESA and Mission Director, EOAM, SAC for their keen interest and encouragement. We are also grateful to Dr G S Kang, Head, CPRS and Dr S S Lal, Head, Crop Production Division, CPRI for their help to carry out the field experiment.

Author information

Authors and Affiliations

  1. Agriculture Forestry & Environment Group, RESA, Space Applications Centre, ISRO, Ahmedabad, 380 015, India

    Namrata Jain, Shibendu Shankar Ray & Sushma Panigrahy

  2. Central Potato Research Station, Jalandhar, 144 003, India

    J. P. Singh

Authors
  1. Namrata Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shibendu Shankar Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. P. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sushma Panigrahy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shibendu Shankar Ray.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jain, N., Ray, S.S., Singh, J.P. et al. Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precision Agric 8, 225–239 (2007). https://doi.org/10.1007/s11119-007-9042-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-007-9042-0

Keywords

Search

Navigation