Evaluating spectral vegetation indices for a practical estimation of nitrogen concentration in dual-purpose (forage and grain) triticale

F. Rodriguez-Moreno, F. Llera-Cid

Abstract


There is an ample literature on spectral indices as estimators of the crop’s chlorophyll concentration, and, by extension, of the nitrogen concentration. In this line, the suitability of 21 of these indices was evaluated as nitrogen concentration indicators for the dual-purpose (fodder and grain) triticale (X Triticosecale Wittmack). The interval of interest was the one in that it would be possible to intervene to correct the deficiency of nitrogen (defined according to practical criteria); one peculiarity of this study is that it only develops a model for that period; more developments complicate the profitability, because the annual stability is not guaranteed and calibration studies are expensive. The results showed that, although there are significant correlations between the greenness indices and the crop’s nitrogen concentration, for none of the spectral indices the relationship can reach acceptable values that encourage their use in the new techniques of precision agriculture of low cost. One solution for improving the effectiveness and reduce costs could be to use the information contained in the spectral signature beyond what is easily explicable by biochemistry and biophysics, in other words, using data mining in the search for new spectral indices directly related to the concentration of nitrogen in plant and stable throughout crop development. At present, the squared correlation coefficient (R2) of the best fits reach 0.5 for the later phenological stages, this mark is reduced to 0.3 with an approach of low cost


Keywords


cereals; leaf reflectance; nutritional status; precision agriculture; radiometry; remote sensing

Full Text:

PDF

References


Azcón-Bieto J., Talón M., 2003. Fundamentos de Fisiología Vegetal. McGraw-Hill-Interamericana de España. [In Spanish]. PMid:12946099

Blackburn G.A., 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens Environ 66, 273-285. http://dx.doi.org/10.1016/S0034-4257(98)00059-5

Blackburn G.A., 1999. Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves. Remote Sens Environ 70, 224-237. http://dx.doi.org/10.1016/S0034-4257(99)00048-6

Daughtry C.S.T., Walthall C.L., Kim M.S., Colstoun E.B., Mcmurtrey J.E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74, 229-239. http://dx.doi.org/10.1016/S0034-4257(00)00113-9

Deering D.W., 1978. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph. D. diss. Texas A&M Univ, College Station. 338 pp.

Feekes W., 1941. De Tarwe en haar milieu [The wheat and its environment]. Vers. XVII Tech. Tarwe Comm. Groningen, 560-561. [In Dutch].

Gitelson A., Merzlyak M., 1996. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148, 495-500. http://dx.doi.org/10.1016/S0176-1617(96)80284-7

Gitelson A.A., Buschmann C., Lichtenthaler H.K., 1999. The chlorophyll fluorescence ratio R735/F700 as an accurate measure of the chlorophyll content in plants. Remote Sens Environ 69, 296-302. http://dx.doi.org/10.1016/S0034-4257(99)00023-1

Guyot G., Baret F., Major D.J., 1988. High spectral resolution: determination of spectral shifts between the red and infrared. Int Arch Photogram Remote Sens 11, 750-760.

Heege H.J., Reusch S., Thiessen E., 2008. Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany. Precis Agric 9, 115-131. http://dx.doi.org/10.1007/s11119-008-9055-3

Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25, 295-309. http://dx.doi.org/10.1016/0034-4257(88)90106-X

Large E.G., 1954. Growth stages in cereals: illustration of the Feeke's scale. Plant Pathol 3, 128-129. http://dx.doi.org/10.1111/j.1365-3059.1954.tb00716.x

Li F., Miao Y., Hennig S., Gnyp M., Chen X., Jia L., Bareth G., 2010. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis Agric 11, 335-357. http://dx.doi.org/10.1007/s11119-010-9165-6

Marschner H., 1995. Mineral nutrition of higher plants, 2nd ed. Academic Press.

R Development Core Team, 2004. R: a language and environment for statistical computing. Foundation for Statistical Computing, Vienna. Available in http://www.Rproject.org. [1 May, 2010].

Rawson H.M., Gómez Macpherson H., 2000. Irrigated wheat. FAO, Roma.

Rondeaux G., Steven M., Baret F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55, 95-107. http://dx.doi.org/10.1016/0034-4257(95)00186-7

Sims D.A., Gamon J.A., 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures, and developmental stages. Remote Sens Environ 81, 337-354. http://dx.doi.org/10.1016/S0034-4257(02)00010-X

Ustin S.L., Jacquemoud S., Zarco-Tejada P., Asner G., 2004. Remote sensing of environmental processes: State of the science and new directions. In: Manual of remote sensing, vol. 4. (Ustin S.L., vol. ed). Remote sensing for natural resource management and environmental monitoring. Asprs. John Wiley And Sons, Ny. pp. 679-730.

Yoder B.J., Pettigrew-Crosby R.E., 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2,500 nm) at leaf and canopy scales. Remote Sens Environ 53, 199-211. http://dx.doi.org/10.1016/0034-4257(95)00135-N

Zadoks J.C., Chang T.T., Konzak C.F., 1974. A decimal code for the growth stages of cereals. Weed Res 14, 415-421. http://dx.doi.org/10.1111/j.1365-3180.1974.tb01084.x

Zarco-Tejada P.J., Miller J., Noland T.L., Mohammed G.H., Sampson P.H., 2001. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE T Geosci Remote Sens 39, 1491-1507. http://dx.doi.org/10.1109/36.934080

Zarco-Tejada P.J., Miller J.R., Morales A., Berjon A., Aguera J., 2004. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sens Environ 90, 463-476. http://dx.doi.org/10.1016/j.rse.2004.01.017




DOI: 10.5424/sjar/20110903-265-10