Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-17T08:26:59.382Z Has data issue: false hasContentIssue false

Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery

Published online by Cambridge University Press:  26 September 2007

N. RAMA RAO*
Affiliation:
Centre for Remote Sensing, Department of Civil Engineering, Indian Institute of Technology-Roorkee, Roorkee 247 667, India
P. K. GARG
Affiliation:
Centre for Remote Sensing, Department of Civil Engineering, Indian Institute of Technology-Roorkee, Roorkee 247 667, India
S. K. GHOSH
Affiliation:
Centre for Remote Sensing, Department of Civil Engineering, Indian Institute of Technology-Roorkee, Roorkee 247 667, India
V. K. DADHWAL
Affiliation:
Indian Institute of Remote Sensing, Department of Space, Government of India, 4, Kalidas Road, Dehradun 248 001, India
*
*To whom all correspondence should be addressed.

Summary

Remotely sensed estimates of biochemical parameters of agricultural crops are central to the precision management of agricultural crops (precision farming). Past research using in situ and airborne spectral reflectance measurements of various vegetation species has proved the usefulness of hyperspectral data for the estimation of various biochemical parameters of vegetation. In order to exploit the vast spectral and radiometric resources offered by space-borne hyperspectral remote sensing for the improved estimation of plant biochemical parameters, the relationships observed between spectral reflectance and various biochemical parameters at in situ and airborne levels needed to be evaluated in order to establish the existence of a reliable and stable relationship between spectral reflectance and plant biochemical parameters at the pixel scale. The potential of the EO-1 Hyperion hyperspectral sensor was investigated for the estimation of total chlorophyll and nitrogen concentrations of cotton crops in India by developing regression models between hyperspectral reflectance and laboratory measurements of leaf total chlorophyll and nitrogen concentrations. A comprehensive and rigorous analysis was carried out to identify the spectral bands and spectral indices for accurate retrieval of leaf total chlorophyll and nitrogen concentrations of cotton crop. The performance of these critical spectral reflectance indices was validated using independent samples. A new vegetation index, named the plant biochemical index (PBI), is proposed for improved estimation of the plant biochemicals from space-borne hyperspectral data; it is simply the ratio of reflectance at 810 and 560 nm. Further, the applicability of PBI to a different crop and at a different geographical location was also assessed. The present results suggest the use of space-borne hyperspectral data for accurate retrieval of leaf total chlorophyll and nitrogen concentrations and the proposed PBI has the potential to retrieve leaf total chlorophyll and nitrogen concentrations of various crops and at different geographical locations.

Type
Crops and Soils
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Asseng, S., Van Keulen, H. & Stol, W. (2000). Performance and application of the APSIM Nwheat model in the Netherlands. European Journal of Agronomy 12, 3754.CrossRefGoogle Scholar
Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment 66, 273285.Google Scholar
Blackmer, T. M. & Schepers, J. S. (1994). Techniques for monitoring crop nitrogen status in corn. Communications in Soil Science and Plant Analysis 25, 17911800.Google Scholar
Chappelle, E. W., Kim, M. S. & McMurtrey, J. III. (1992). Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of Environment 39, 239247.Google Scholar
Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B. & McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74, 229239.CrossRefGoogle Scholar
Donahue, R. A., Berg, V. S. & Vogelmann, T. C. (1983). Assessment of the potential of the blue light gradient in soybean pulvini as a leaf orientation signal. Physiologia Plantarum 79, 593598.CrossRefGoogle Scholar
Filella, I., Serrano, L., Serra, J. & Penuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science 35, 14001405.Google Scholar
Gates, D. M., Keegan, H. J., Schleter, J. C. & Weidner, V. R. (1965). Spectral properties of plants. Applied Optics 4, 1120.Google Scholar
Gitelson, A. A. & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18, 26912697.CrossRefGoogle Scholar
Gitelson, A. A., Kaufman, Y. J. & Merzlyak, M. N. (1996 a). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58, 289298.CrossRefGoogle Scholar
Gitelson, A. A., Merzlyak, M. N. & Lichtenthaler, H. K. (1996 b). Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology 148, 501508.CrossRefGoogle Scholar
Hinzman, L. D., Bauer, M. E. & Daughtry, C. S. T. (1986). Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat. Remote Sensing of Environment 19, 4761.Google Scholar
Jamieson, P. D., Porter, J. R., Goudriaan, J., Ritchie, J. T., Van Keulen, H. & Stol, W. (1998). A comparison of the models AFRCWHEAT2, CERES-wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought. Field Crops Research 55, 2344.CrossRefGoogle Scholar
Johnson, L. F. & Billow, C. R. (1996). Spectrometric estimation of total nitrogen concentration in Douglas-fir foliage. International Journal of Remote Sensing 17, 489500.Google Scholar
Mariotti, M., Ercoli, L. & Masoni, A. (1996). Spectral properties of iron-deficient corn and sunflower leaves. Remote Sensing of Environment 58, 282288.Google Scholar
Martin, M. E. & Aber, J. D. (1993). High spectral resolution remote sensing of forest canopy lignin, nitrogen and ecosystem process. Ecological Applications 7, 431443.Google Scholar
Massart, D. L., Vandeginste, B. G. M., Deming, S. N., Michotte, Y. & Kaufman, L. (1988). Chemometrics: A Textbook. Amsterdam: Elsevier.Google Scholar
Matthew, M. W., Adler, S. M., Berk, A., Felde, G. W., Anderson, G. P., Gorodetzky, D., Paswaters, S. E. & Shippert, M. (2003). Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data. Proceedings of the SPIE 5093, 474482.Google Scholar
Nelson, D. W. & Sommers, L. E. (1972). A simple digestion procedure for estimation of total nitrogen in soils and sediments. Journal of Environmental Quality 1, 423425.CrossRefGoogle Scholar
Peleg, K. (1998). Fast Fourier-Transform based calibration in remote sensing. International Journal of Remote Sensing 19, 23012315.Google Scholar
Penuelas, J., Filella, I. & Gamon, J. A. (1995). Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist 131, 291296.CrossRefGoogle Scholar
Peterson, D. L., Aber, J. D., Matson, P. A., Card, D. H., Swanberg, N., Wessman, C. A. & Spanner, M. (1988). Remote sensing of forest canopy and leaf biochemical contents. Remote Sensing of Environment 24, 85108.CrossRefGoogle Scholar
Shibayama, M. & Akiyama, T. A. (1986). A spectroradiometer for field use: VI. Radiometric estimation for chlorophyll index of rice canopy. Japanese Journal of Crop Science 55, 433438.Google Scholar
St-Jacques, C. & Bellefleur, P. (1991). Determining leaf nitrogen concentration of broadleaf tree seedlings by reflectance measurements. Tree Physiology 8, 391398.CrossRefGoogle Scholar
Takihashi, W., Nguyen-Cong, V., Kawaguchi, S., Minamiyama, M. & Ninomiya, S. (2000). Statistical models for prediction of dry weight and N accumulation based on visible and near-infrared hyper spectral reflectance. Plant Production Science 3, 377386.Google Scholar
Tarpley, L., Reddy, K. R. & Sassenrath-Cole, G. F. (2000). Reflectance indices with precision and accuracy in predicting cotton plant leaf nitrogen concentration. Crop Science 40, 18141819.CrossRefGoogle Scholar
Thomas, J. R. & Gausman, H. W. (1977). Leaf reflectance vs. leaf chlorophyll and carotenoid concentration for eight crops. Agronomy Journal 69, 799802.CrossRefGoogle Scholar
Thomas, J. R. & Oerther, G. F. (1972). Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal 64, 1113.CrossRefGoogle Scholar
Tsay, M. L., Gjerstad, D. H. & Glover, G R. (1982). Tree leaf reflectance: a promising technique to rapidly determine nitrogen and chlorophyll content. Canadian Journal of Forestry Research 12, 788792.Google Scholar
Wang, K., Shen, Z. Q. & Wang, R. C. (1998). Effects of N nutrition on the spectral reflectance characteristics of rice leaf and canopy. Journal of Zhejiang Agricultural University 24, 9397.Google Scholar
Wood, C. W., Reeves, D. W. & Himelrick, D. G. (1993). Relationships between chlorophyll meter reading and leaf chlorophyll concentration, N status, and crop yield: a review. Proceedings of the Agronomy Society of New Zealand 23, 19.Google Scholar
Woolley, J. T. (1971). Reflectance and transmittance of light by leaves. Plant Physiology 47, 656662.CrossRefGoogle ScholarPubMed
Yoder, B. J. & Daley, L. S. (1989). Development of a visible spectroscopic method for determining chlorophyll a and b in vivo in leaf samples. Spectroscopy 5, 4450.Google Scholar
Yoder, B. J. & Pettigrew-Crosby, R. E. (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sensing of Environment 53, 199211.Google Scholar