Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species

International Journal of Remote Sensing

Volumn 26, Issue 18, 2005, Pages 4083-4095

  Ferwerda, Jelle G ^a   Skidmore, Andrew K ^a   Mutanga, Onisimo ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CHLOROPHYLL; CORRELATION METHODS; NITROGEN; PLANTS (BOTANY); VEGETATION;

NITROGEN CONTENT DETECTION; NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI);

REMOTE SENSING;

NDVI; NITROGEN; PLANT; REMOTE SENSING;

EID: 27744451462     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160500181044     Document Type: Article

References (37)

1. Aber, J.D. and Federer, C.A., 1992, A generalized linked parameter model of photosynthesis, evapotranspiration and net primary production in temporate and boreal forest ecosystems. Oecologia, 92, pp. 463-474.
2. Baret, F. and Guyot, G., 1991, Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, pp. 161-173.
3. Berardo, N., 1997, Prediction of the chemical composition of white clover by near-infrared reflectance spectroscopy. Grass and Forage Science, 52, pp. 27-32.
4. Bernays, E.A., Driver, G.C. and Bilgener, M., 1989, Herbivores and plant tannins. Advances in Ecological Research, 19, pp. 263-302.
5. Broge, N.H. and Leblanc, E., 2000, Comparing predictive 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, pp. 156-172.
6. Ceccato, P., Gobron, N., Flasse, S., Pinty, B. and Tarantola, S., 2002, Designing a spectral index to estimate vegetation water content from remote sensing data: 1. Theoretical approach. Remote Sensing of Environment, 82, pp. 188-197.
7. Cohen, J., 1960, A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, pp. 37-47.
8. Curran, P.J., 1989, Remote sensing of foliar chemistry. Remote Sensing of Environment, 30, pp. 271-278.
9. Curran, P.J., Dungan, J.L. and Peterson, D.L., 2001, Estimating the foliar biochemical concentration of leaves with reflectance spectrometry. Testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76, pp. 349-359.
10. Gitelson, A.A. and Merzlyak, M.N., 1998, Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22, pp. 689-692.
11. Gong, P., Pu, R. and Heald, R.C., 2002, Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. International Journal of Remote Sensing, 23, pp. 1827-1850.
12. Gupta, R.K., Vijayan, D. and Prasad, T.S., 2001a, Characterization of red-near infrared transition for wheat and chickpea using 3 nm bandwidth data. Advances in Space Research, 28, pp. 189-194.
13. Gupta, R.K., Vijayan, D. and Prasad, T.S., 2001b, New hyperspectral vegetation characterisation parameters. Advances in Space Research, 28, pp. 201-206.
14. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J. and Strachan, I.B., 2004, Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modelling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, pp. 337-352.
15. Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J. and Dextraze, L., 2002, Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, pp. 416-426.
16. Hansen, P.M. and Schoerring, J.K., 2003, Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86, pp. 542-553.
17. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G., 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, pp. 195-213.
18. Huete, A.R., 1988, A soil adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, pp. 295-309.
19. Johnson, L.F., 2001, Nitrogen influence on fresh-leaf NIR spectra. Remote Sensing of Environment, 78, pp. 314-320.
20. Kokaly, R.F. and Clark, R.N., 1999, Spectroscopic determination of leaf biochemistry using optical band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67, pp. 267-287.
21. Larsson, H., 2002, Acacia canopy cover changes in Rawashda forest reserve, Kassala Province, Eastern Sudan, using linear regression NDVI models. International Jounal of Remote Sensing, 23, pp. 335-339.
22. Maire, G.L., Francois, C. and Dufrene, E., 2004, Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89, pp. 1-28.
23. Meer, F.V.D., Bakker, W.K.L., Scholte, K., Skidmore, A.K., Jong, S.D., Clevers, J., Addinks, E. and Epema, G., 2001, Spatial scale variations in vegetation indices and above-ground biomass estimates: Implications for MERIS. International Jounal of Remote Sensing, 22, pp. 3381-3396.
24. Mutanga, O. and Skidmore, A.K., 2004, Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Jounal of Remote Sensing, 25, pp. 3999-4014.
25. Mutanga, O., Skidmore, A.K. and Prins, H.H.T., 2003, Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment, 89, pp. 393-408.
26. Novozamsky, I., Houba, V.J.G., Van Eck, R. and Van Vark, W., 1983, A novel digestion technique for multi-element plant analysis. Communications in Soil Science and Plant Analysis, 14, pp. 239-249.
27. Peterson, D.L., Aber, J.D., Matson, P.A., Card, D.H., Swanberg, N., Wessman, C. and Spanner, M., 1988, Remote sensing of forest canopy and leaf biochemical contents. Remote Sensing of Environment, 24, pp. 85-108.
28. Peterson, D.L. and Hubbard, G.S., 1992, Scientific Issues and potential remote-sensing requirements for plant biochemical content. Journal of Imaging Science and Technology, 36, pp. 446-456.
29. Prince, S.D., Goetz, S.J. and Goward, S.N., 1995, Monitoring primary production from earth observing satellites. Water, Air and Soil Pollution, 82, pp. 509-522.
30. Scanlon, T.M., Albertson, J.D., Caylor, K.K. and Williams, C.A., 2002, Determining land surface fractional cover from NDVI and rainfall time series for a savanna ecosystem. Remote Sensing of Environment, 82, pp. 376-388.
31. Sims, D.A. and Gamon, J.A., 2002, Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, pp. 337-354.
32. Suryanarayana Reddy, G., Narasimha Rao, C.L., Venkataratnam, L. and Krishna Rao, P.V., 2003, Influence of plant pigments on spectral reflectance of maize, groundnut and soybean grown in semi-arid environments. International Jounal of Remote Sensing, 22, pp. 3373-3380.
33. Thenkabail, P.S., Enclona, E.A., Ashton, M.S. and Van der Meer, B., 2004, Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91, pp. 354-376.
34. Tian, Q., Tong, Q., Pu, R., Guo, Q. and Zhao, C., 2001, Spectroscopic determination of wheat water status using 1650-1850 nm spectral absorption features. International Journal of Remote Sensing, 22, pp. 2329-2338.
35. Wachtendonk, J.W.V. and Root, R.R., 2003, The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuel models in Yosemite National Park, USA. International Jounal of Remote Sensing, 24, pp. 1639-1651.
36. Zarco-Tejeda, P.J., Miller, J.R., Morales, A., Berjon, A. and Aguera, J., 2004, Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment, 90, pp. 463-476.
37. Zhao, D., Reddy, K.R., Kakani, V.G. and Reddy, V.R., in press, Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, in press.