Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice

  • Regular Article
  • Published:
Plant and Soil Aims and scope Submit manuscript

Abstract

Background and aims

Variations in the water and soil background in the signal path can cause variations in canopy spectral reflectance, which leads to uncertainty in estimating the canopy nitrogen (N) status. The primary objective of this study was to explore the optimum vegetation indices that were highly correlated with canopy leaf N concentration (LNC) but less influenced by the canopy leaf area index (LAI) and vegetation coverage (VC) in rice.

Methods

A systematic analysis of the quantitative relationships between various hyperspectral vegetation indices and LNC, VC and LAI was conducted based on 4-year rice field experiments using different rice varieties, N rates and planting densities. New spectral indices were derived to estimate LNC in rice under variable vegetation coverage.

Results

Although the newly developed simple green ratio indices, SR (R553, R537) and SR (R545, R538), and the three-band index (R605-R521-R682)/(R605+R521+R682) correlated well with the LNC. Only SR (R553, R537) was less influenced by VC/LAI and showed a stable performance in both the independent calibration and validation datasets. For the published indices tested in the present study, NDVIg-b and ND (R503, R483) showed a good predictive ability for the LNC. However, both of these indices and other published indices were found to be significantly dominated by the VC/LAI.

Conclusion

SR (R553, R537) was the best index to reliably estimate the LNC in rice under various cultivation conditions, and is recommended for this use. However, other spectral indices need to be examined to determine if they influenced by factors such as VC/LAI. Such studies will improve the applicability of these indices to different types of rice cultivars and production systems.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Baret F, Guyot G, Major DJ (1989) TSAVI-A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. 12th Canadian Symposium on Remote Sensing, Canada 1355–1358

  • Barnes JD, Balaguer L, Manrique E, Elvira S, Davison AW (1992) A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ Exp Bot 32(2):85–100

    Article  CAS  Google Scholar 

  • Blackburn GA (1998) Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Int J Remote Sens 19(4):657–675

    Article  Google Scholar 

  • Blackmer TM, Schepers JS, Varvel GE, Walter-Shea EA (1996) Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron J 88(1):1–5

    Article  CAS  Google Scholar 

  • Broge NH, 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 Sens Environ 76(2):156–172

    Article  Google Scholar 

  • Buschman C, Nagel E (1993) In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation. Int J Remote Sens 14:711–722

    Google Scholar 

  • Daughtry CST, Walthall CL, Kim MS, Brown de Colstoun E, McMurtrey JE III (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74(2):229–239

    Article  Google Scholar 

  • Demetriades-Shah TH, Steven MD, Clark JA (1990) High resolution derivative spectra in remote sensing. Remote Sens Environ 33(1):55–64

    Article  Google Scholar 

  • Gamon JA, Peñuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41(1):35–44

    Article  Google Scholar 

  • Gitelson AA, Merzlyak MN (1994) Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol 143:286–292

    Article  CAS  Google Scholar 

  • Gitelson AA, Merzlyak MN (1998) Remote sensing of chlorophyll concentration in higher plant leaves. Adv Space Res 22(5):689–692

    Article  CAS  Google Scholar 

  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58(3):289–298

    Article  Google Scholar 

  • Guan L, Liu XN (2009) Two types of modified spectral indices for retrieval of crop canopy chlorophyll content. Adv Earth Sci 24(5):548–554 (in Chinese)

    CAS  Google Scholar 

  • Gupta RK, Vijayan D, Prasad TS (2003) Comparative analysis of red-edge hyperspectral indices. Adv Space Res 32(11):2217–2222

    Article  Google Scholar 

  • Guyot G, Baret F, Jacquemoud S (1992) Imaging spectroscopy for vegetation studies. Imaging spectroscopy: fundamentals and prospective applications. Kluwer Academic Publishers, Dordrecht, pp 145–165

    Google Scholar 

  • Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ 81(2–3):416–426

    Article  Google Scholar 

  • Hansen PM, Schjoerring JK (2003) Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ 86(4):542–553

    Article  Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309

    Article  Google Scholar 

  • Huete AR, Jackson RD, Post DF (1985) Spectral response of a plant canopy with different soil backgrounds. Remote Sens Environ 17(1):37–53

    Article  Google Scholar 

  • Inoue Y, Penuelas J (2001) An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. Int J Remote Sens 22(18):3883–3888

    Article  Google Scholar 

  • LaCapra VC, Melack JM, Gastil M, Valeriano D (1996) Remote sensing of inundated rice with imaging spectrometry. Remote Sens Environ 55:50–58

    Article  Google Scholar 

  • Li Y, Demetriades-Shah TH, Kanemasu ET, Shultis JK, Kirkham MB (1993) Use of second derivatives of canopy reflectance for monitoring prairie vegetation over different soil backgrounds. Remote Sens Environ 44(1):81–87

    Article  Google Scholar 

  • Li YX, Zhu Y, Tian YC, Yao X, Qin XD, Cao WX (2006) Quantitative relationship between leaf nitrogen concentration and canopy reflectance spectra. Acta Agron Sin 32(3):358–362 (in Chinese)

    CAS  Google Scholar 

  • Maccioni A, Agati G, Mazzinghi P (2001) New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J Photochem Photobiol 61(1–2):52–61

    Article  CAS  Google Scholar 

  • Marshak A, Knyazikhin Y, Davis A, Wiscombe W, Pilewskie P (2000) Cloud-vegetation interaction: use of normalized difference cloud index for estimation of cloud optical thickness. Geophys Res Lett 27(12):1695–1698

    Article  Google Scholar 

  • Martin ME, Aber JD (1997) High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecol Appl 7(2):431–443

    Article  Google Scholar 

  • Pearson RL, Miller LD (1972) Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie. Remote Sens Environ 2:1357–1381

    Google Scholar 

  • Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB (1994) Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves. Remote Sens Environ 48(2):135–146

    Article  Google Scholar 

  • Peñuelas J, Baret F, Filella I (1995a) Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31(2):221–230

    Google Scholar 

  • Peñuelas J, Filella I, Gamon JA (1995b) Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol 131(3):291–296

    Article  Google Scholar 

  • Qi J, Chehbouni A, Huete AR, Kerr H, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126

    Article  Google Scholar 

  • Reserch Systems Ins (2005) ENVI user manual. Space Flight Digital Press, Beijing (in Chinese)

    Google Scholar 

  • Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Rem S 43:1541–1552

    Google Scholar 

  • Rodriguez D, Fitzgerald GJ, Belford R, Christensen LK (2006) Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Aust J Agric Res 57(7):781–789

    Article  CAS  Google Scholar 

  • Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55(2):95–107

    Article  Google Scholar 

  • Roujean JL, Breon FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51:375–384

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA (1974) Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC, Type III, Final Report, Greenbelt, MD, USA, 1–371

  • Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81(2–3):337–354

    Article  Google Scholar 

  • Stone ML, Solie JB, Raun WR, Whitney RW, Taylor SL, Ringer JD (1996) Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Trans Am Soc Agric Eng 39(5):1623–1631

    Google Scholar 

  • Stroppiana D, Boschetti M, Brivio PA, Bocchi S (2009) Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crop Res 111(1–2):119–129

    Article  Google Scholar 

  • Sun L, Cheng LJ (2011) Anti-soil background capacity with vegetation biochemical component spectral model. Acta Ecol Sin 31(6):1641–1652 (in Chinese)

    Google Scholar 

  • Tang YL, Wang XZ, Huang JF, Wang RC (2003) Action of derivative spectra and vegetation indices for rice. Trans Chin Soc Agric Eng 19(1):145–150 (in Chinese)

    Google Scholar 

  • Tarpley L, Reddy KR, Sassenrath-Cole GF (2000) Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Sci 40(6):1814–1819

    Article  Google Scholar 

  • Tian YC, Yao X, Yang J, Cao WX, Hannaway DB, Zhu Y (2011) Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crop Res 120(2):299–310

    Article  Google Scholar 

  • Verstraete MM, Pinty B (1996) Designing optimal spectral indexes for remote sensing applications. Trans Geosci Remote Sens 34(5):1254–1265

    Article  Google Scholar 

  • Vogelmann JE, Rock BN, Moss DM (1993) Red edge spectral measurements from sugar maple leaves. Int J Remote Sens 14(8):1563–1575

    Article  Google Scholar 

  • Wang XZ, Wang RC, Huang JF (2002) Derivative spectrum remote sensing and its application in measurement of rice agronomic parameters of rice. Trans Chin Soc Agric Eng 18(1):9–13 (in Chinese)

    Google Scholar 

  • Wang W, Yao X, Tian YC, Liu XJ, Ni J, Cao WX, Zhu Y (2012) Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crop Res 129:90–98

    Article  Google Scholar 

  • Wu CY, Niu Z, Tang Q, Huang WJ (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric For Meteorol 148(8–9):1230–1241

    Article  Google Scholar 

  • Xue LH, Cao WX, Luo WH, Zhang X (2004) Correlation between leaf nitrogen status and canopy spectral characteristics in wheat. Acta Phytoecol Sin 28(2):172–177 (in Chinese)

    Google Scholar 

  • Yao X, Zhu Y, Tian YC, Liu XJ, Cao WX (2010) Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. Int J Appl Earth Obs 12(2):89–100

    Google Scholar 

  • Zhang JH, Wang K, Bailey JS, Wang RC (2006) Predicting nitrogen status of rice using multispectral data at canopy scale. Soil Sci Soc China 16(1):108–117

    CAS  Google Scholar 

  • Zhao DL, Reddy KR, Kakani VG, Read JJ, Koti S (2005) Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agron J 97(1):89–98

    Article  CAS  Google Scholar 

  • Zhu Y, Tian YC, Yao X, Liu XJ, Cao WX (2007) Analysis of common canopy reflectance spectra for indicating leaf nitrogen concentrations in wheat and rice. Plant Prod Sci 10(4):400–411

    Google Scholar 

Download references

Acknowledgments

This work was supported by grants from the National 863 High-tech Program (2013AA102301 and 2011AA100703), the National Natural Science Foundation of China (31371535), the Special Fund for Agro-scientific Research in the Public Interest (201303109), the Science and Technology Support Program of Jiangsu (BE2010395, BE2011351 and BE2012302), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China.

Author information

Authors and Affiliations

  1. National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu, 210095, Peoples Republic of China

    Yong-Chao Tian, Kai-Jian Gu, Xu Chu, Xia Yao, Wei-Xing Cao & Yan Zhu

Authors
  1. Yong-Chao Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai-Jian Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xu Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xia Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei-Xing Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Zhu.

Additional information

Responsible Editor: Alfonso Escudero.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tian, YC., Gu, KJ., Chu, X. et al. Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant Soil 376, 193–209 (2014). https://doi.org/10.1007/s11104-013-1937-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11104-013-1937-0

Keywords

Search

Navigation