Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-29T14:52:20.510Z Has data issue: false hasContentIssue false

The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

Published online by Cambridge University Press:  03 October 2008

R. K. Mahey
Affiliation:
Department of Agronomy, Punjab Agricultural University, Ludhiana-141 004, India
Rajwant Singh
Affiliation:
Department of Agronomy, Punjab Agricultural University, Ludhiana-141 004, India
S. S. Sidhu
Affiliation:
Department of Agronomy, Punjab Agricultural University, Ludhiana-141 004, India
R. S. Narang
Affiliation:
Department of Agronomy, Punjab Agricultural University, Ludhiana-141 004, India

Summary

Ground-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.

Efectos de lafalta de agua en el trigo

Type
Research Article
Copyright
Copyright © Cambridge University Press 1991

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

Duncan, W. G., Loomis, R. S., Williams, W. A. & Hanau, R. (1967). A model for simulating photosynthesis in plant communities. Hilgardia 38:482485.CrossRefGoogle Scholar
Jackson, R. D. (1983). Spectral indices in n-space. Remote Sensing of Environment 13:409421.CrossRefGoogle Scholar
Kamat, D. S.,Gopalan, A. K. S., Ajai, & Shashikumar, M. N. (1985). Assessment of water-stress effects on crops. International Journal of Remote Sensing 6:577589.Google Scholar
Knipling, E. B. (1970). Physical and physiological bases for the reflectance of visible and near infrared radiation from vegetation. Remote Sensing of Environment 1:155159.Google Scholar
Lemeur, R. & Blad, B. L. (1974). A critical review of light models for estimating the shortwave radiation regime of plant canopies. Agricultural Meteorology 14:255286.Google Scholar
O'Toole, J. C., Turner, N. C., Namuco, O. P., Dingkuhn, M. & Gomez, K. A. (1984). Comparison of some crop stress measurement methods. Crop Science 24:11211128.Google Scholar
Ritchie, J. T. (1972). Model for predicting evaporation from a row crop with incomplete cover. Water Resource Research 8:12041213.CrossRefGoogle Scholar
Robinson, B. F. & Biehl, L. L. (1979). Calibration procedures for measurement of reflectance factors in remote sensing field research. Proceedings Society of Photo-Optical Instruments Engineering 196:1626.Google Scholar
Rosenthal, W. D., Kanemasu, E. T., Raney, R. J. & Stone, L. R. (1977). An evaluation of an evapotranspiration model for corn. Agronomy Journal 69:461464.Google Scholar
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetations. Remote Sensing of Environment 8:127150.Google Scholar
Tucker, C. J., Elgin, J. H. Jr, McMurtrey, J. E. & Fan, C.J.(1979). Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sensing of Environment 8:237248.Google Scholar
Tucker, C. J., Holben, B. N., Elgin, J. H. & Momurtrey, J. E. (1980). Relationships of spectral data to grain yield variations. Photogrammetric Engineering and Remote Sensing 46:657666.Google Scholar
Wiegand, C. L., Gausman, H. W. & Allen, W. A. (1972). Physiological factors and optical parameters as bases of vegetation discrimination and stress analysis. In Proceedings Seminar, Operational Remote Sensing,Houston 1–4 Feb. 1972,82–102.Google Scholar
Wiegand, C. L. & Richardson, A. J. (1984). Leaf area, light interception and yield estimates from spectral components analysis. Agronomy foumal 76:543548.Google Scholar