Soil-line vegetation indices for corn nitrogen content prediction

• Autorzy: Bagheri N. , Ahmadi H. , Alavipanah S. K. , Omid M.
• Języki publikacji: EN

Abstrakty

EN

The soil-line vegetation indices for prediction of corn canopy nitrogen content were investigated. Results indicated that the vegetation indices applied were correlated with corn canopy nitrogen content and the wavelengths between 630-860 nm are suitable for nitrogen diagnosis. The second-order polynomial equation was the best model for nitrogen content prediction among different regression types. Analyses based on both predicted and measured data were carried out to compare the performance of existing vegetation indices.

• Wydawca: -
• Czasopismo: International Agrophysics
• Rocznik: 2012
• Tom: 26
• Numer: 2
• Opis fizyczny: p.103-108,fig.,ref.

Twórcy

autor

Bagheri N.

• Department of Agricultural Technology and Engineering, University of Tehran, Karaj, Iran

autor

Ahmadi H.

• Department of Agricultural Technology and Engineering, University of Tehran, Karaj, Iran

autor

Alavipanah S. K.

• Department of Geography, University of Tehran, Karaj, Iran

autor

Omid M.

• Department of Agricultural Technology and Engineering, University of Tehran, Karaj, Iran

Bibliografia

• Abrams M., 2003. The advanced space borne thermal emission and reflection radiometer (ASTER): data products for the high spatial resolution imager on NASA's Terra platform. J. Int. J. Remote Sens., 21, 847-859.
• Beeri O., Phillips R., Carson P., and Liebig M., 2005. Alternate satellite models for estimation of sugar beet residue nitrogen credit. J. Agric. Ecosys. Environ., 107, 21-35.
• BlackmerT.M.,Schepers J.S.,VarvelG.E., andWalter-Shea E.A., 1996. Nitrogen deficiency detection using reflected shortwave radiation fromirrigated corn canopies. J.Agron., 88, 1-5.
• Borhan M., Panigrahi S., Lorenzen S., and Gu H., 2004. Multispectral and color imaging techniques for nitrate and chlorophyll determination of potato leaves in a controlled environment. J. Trans. ASABE, 47(2), 599-608.
• Buscaglia H.J. and Varco J.J., 2002. Early detection of cotton leaf nitrogen status using leaf reflectance. J. Plant Nutr., 25, 2067-2080.
• Eldaw Elwadie M., Francis J., and Qi J., 2005. Remote sensing of canopy dynamics and biophysical variables estimation of corn in Michigan. J. Agron., 97, 99-105.
• Feng W., Yao X., Zhu Y., Tian Y.C., and Cao W.X., 2008. Monitoring leaf nitrogen status with hyper spectral reflectance in wheat. J. Eur. Agron., 28, 394-404.
• Haboudane D., Miller J.R., Pattey E., Zarco-Tejada P.J., and Strachan I.B., 2004. Hyper spectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. J. Remote Sens. Environ., 90, 337-352.
• Huete A.R., 1988. A soil vegetation adjusted index (SAVI). J. Remote Sens. Environ., 25, 295-309.
• Jin-Heng Z., Ke W., Bailey J.S., and Ren-Chao W., 2006. Predicting nitrogen status of rice using multi spectral data at canopy scale. Pedosphere, 16(1), 108-117.
• Jongschaap R.E.E. and Booij R., 2004. Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy status. J. Int. Appl. Earth Obser. Geoinf., 5, 205-218.
• Karimi Y., Prasher S.O., Mcnairn H., Bonnell R.B., Dutilleul P., and Goel P.K., 2005. Discriminant analysis of hyper spectral data for assessing water and nitrogen stresses in corn. J. Trans. ASABE, 48(2), 805-813.
• KostrzewskiM.,Waller P.,Guertin P.,Haberland J.,Colaizzi P., Barnes E., Thompson T.,Clarke T.,Riley E., andChoi C., 2002. Ground-based remote sensing of water and nitrogen stress. J. Trans. ASABE, 46(1), 29-38.
• Lawrence R.L. and Ripple W.J., 1998. Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape. J. Remote Sens. Environ., 64, 91-102.
• Li F., Gnyp M., Jia L., Miao Y., Yu Z., Koppe W., Bareth G., Chen X., and Zhang F., 2008. Estimating N status of winter wheat using a handheld spectrometer in the North China plain. J. Field Crops. Res., 106, 77-85.
• Lough L.J. and Varco L.L., 2000. Effects of varing N and K nutrition on the spectral reflectance properties of cotton. Proc. 5th Int. Conf. Precision Agric., July 16-19, Bloomington, MI, USA.
• Min M. and Lee W.S., 2005. Determination of significant wavelengths and prediction of nitrogen content for citrus. J. Trans. ASABE, 48(2), 455-461.
• Pagola M., Ortiz R., Irigoyen I., Bustince H., Barrenechea E., Aparicio-Tejo P., Lamsfus C., and Lasa B., 2008. New method to assess barely nitrogen nutrition status based on image colour analysis comparisonwithSPAD-502. J. Comput. Electron. Agric., 65(2), 213-218.
• Perry M.E. and Davenport J.R., 2007. Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple. J. Comput. Electron. Agric., 59, 56-65.
• Piekarczyk J., 2001. Studies of soil temperature on the basis of satellite data. Int. Agrophysics, 15, 101-107.
• Qi J.,Chehbouni A.,Huete A.R.,Keer Y.H., and Sorooshian S., 1994. A modified soil vegetation adjusted index. J. Remote Sens. Environ., 48, 119-126.
• Reum D. and Zhang Q., 2007. Wavelet based multi-spectral image analysis of maize leaf chlorophyll content. J. Comput. Electron. Agric., 56, 60-71.
• Sena D.G., jr, Pinto F.A.C., Queiroz D.M., and Souza M.A., 2007. Multivariate classifiers using image texture features for nitrogen doses discrimination in wheat. Proc. ASABE Annual Meeting, June 15-17, Minneapolis, MI, USA.
• Xue L. and Yang L., 2008. Recommendations for nitrogen fertilizer topdressing rates in rice using canopy reflectance spectra. J. Biosyst. Eng., 100, 524-534.
• Zhao C., Liu L., Wang J., Huang W., Song X., and Li C., 2005. Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress. Int. J. Appl. Earth Obser. Geoinf., 7, 1-9.

Uwagi

PL

Rekord w opracowaniui