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Spectral reflectance curves to distinguish soybean from common cocklebur (Xanthium strumarium) and sicklepod (Cassia obtusifolia) grown with varying soil moisture

Published online by Cambridge University Press:  20 January 2017

W. Brien Henry ,
David R. Shaw ,
Kambham R. Reddy ,
Lori M. Bruce  and
Hrishikesh D. Tamhankar
David R. Shaw
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Kambham R. Reddy
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Lori M. Bruce
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762
Hrishikesh D. Tamhankar
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762
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Abstract

Experiments were conducted to examine the use of spectral reflectance curves for discriminating between plant species across moisture levels. Weed species and soybean were grown at three moisture levels, and spectral reflectance data and leaf water potential were collected every other day after the imposition of moisture stress at 8 wk after planting. Moisture stress did not reduce the ability to discriminate between species. As moisture stress increased, it became easier to distinguish between species, regardless of analysis technique. Signature amplitudes of the top five bands, discrete wavelet transforms, and multiple indices were promising analysis techniques. Discriminant models created from data set of 1 yr and validated on additional data sets provided, on average, approximately 80% accurate classification among weeds and crop. This suggests that these models are relatively robust and could potentially be used across environmental conditions in field scenarios.

Keywords

Soybean, Glycine max (L.) MerrLeaf water potentialspectral reflectance curvesNDVIRVISAVIDVINDVIgIPVIMSI
Type
Weed Management
Information
Weed Science , Volume 52 , Issue 5 , October 2004 , pp. 788 - 796
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Allen, L. H. Jr., Valle, R. R., Jones, J. W., and Jones, P. H. 1998. Soybean leaf water potential responses to carbon dioxide and drought. Agron. J 90:375–383.CrossRefGoogle Scholar
Burrus, S., Gopinath, R., and Guo, H. 1998. Introduction to Wavelets and Wavelet Transforms: A Primer. 1st ed. Upper Saddle River, New Jersey: Prentice-Hall. Pp. 6–7.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequences of weed spatial distribution. Weed Sci 45:364–373.Google Scholar
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., and Gregoire, J. M. 2001. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ 77:22–33.Google Scholar
Clay, S. A., Lems, G. J., Clay, D. E., Forcella, F., Ellsbury, M. M., and Carlson, C. G. 1999. Sampling weed spatial variability on a fieldwide scale. Weed Sci 47:674–681.CrossRefGoogle Scholar
Cohen, W. B. 1991. Response of vegetation indices to changes in tree measures of leaf water stress. Photogram. Eng. Remote Sens 57:195–202.Google Scholar
Crippen, R. E. 1990. Calculating the vegetation index faster. Remote Sens. Environ 34:71–73.Google Scholar
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 Sens. Environ 76:349–359.Google Scholar
Danson, F. M., Steven, M. D., Malthus, T. J., and Clark, J. A. 1992. High-spectral resolution data for determining leaf water content. Int. J. Remote Sens 13:461–470.Google Scholar
Duda, R. O., Hart, P. E., and Stark, D. G. 2001. Pattern Classification. 2nd ed. New York: J. Wiley. Pp. 117–124.Google Scholar
Gausman, H. W. 1985. Plant Leaf Optical Properties in Visible and Near-Infrared Light. Graduate Studies. No. 29. Texas Technical University, Lubbock, TX: Texas Tech. Press. Pp. 1–78.Google Scholar
Gitelson, A., Kaufman, Y., and Merzlyak, M. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ 58:289–298.Google Scholar
Gond, V., DePury, D. G. G., Veroustraete, F., and Ceulemans, R. 1999. Seasonal variations in leaf area index, leaf chlorophyll, and water content; scaling-up to estimate fAPAR and carbon balance in a multiplayer, multispecies temperate forest. Tree Physiol 19:673–679.CrossRefGoogle Scholar
Graps, A. 1995. An introduction to wavelets. IEEE Comput. Sci. Eng 2:1–18.Google Scholar
Hanley, J. and McNeil, B. 1982. The meaning and use of the area under a receiver operating characteristics (ROC) curve. Diagn. Radiol 143:29–36.Google Scholar
Hardy, C. C. and Burgan, R. E. 1999. Evaluation of NDVI for monitoring live moisture in three vegetation types of the western U.S. Photogram. Eng. Remote Sens 65:603–610.Google Scholar
Hoagland, D. H. and Arnon, D. I. 1950. The water-culture method for growing plants without soil. Calif Agric. Exp. Stn. Cir 347:1–32.Google Scholar
Huang, Y., Bruce, L. M., Byrd, J., and Mask, B. 2001. Using wavelet transforms of hyperspectral reflectance curves for automated monitoring of Imperata cylindrica (cogongrass). Pages 2244–2246 in Proceedings of the IEEE Geoscience and Remote Sensing Symposium. Sydney, Australia. New York: IEEE.Google Scholar
Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ 25:295–309.Google Scholar
Hunt, E. R. Jr. and Rock, B. N. 1989. Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sens. Environ 30:43–54.Google Scholar
Jordan, C. F. 1969. Derivation of leaf area index from quality of light on the forest floor. Ecol 50:663–666.Google Scholar
Koger, C. H. 2001. Remote Sensing Weeds, Cover Crop Residue, and Tillage Practices in Soybean. Ph.D. dissertation. Mississippi State University, Mississippi State, MS. Pp. 120–140.Google Scholar
Leon, C. T. 2001. Crop Monitoring Utilizing Remote Sensing, Soil Parameters, and GPS Technologies. . Mississippi State University, Mississippi State, MS. Pp. 27–84.Google Scholar
Lillesand, T. M. and Kiefer, R. W. 1987. Remote Sensing and Image Interpretation. 2nd ed. New York: J. Wiley. 721 p.Google Scholar
Moran, M. S., Clarke, T. R., Inoue, Y., and Vidal, A. 1994. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens. Environ 49:246–263.Google Scholar
Patakas, A. and Noitsakis, B. 2001. Leaf age effects on solute accumulation in water-stressed grapevines. J. Plant Physiol 158:63–69.CrossRefGoogle Scholar
Peñuelas, J., Filella, I., Biel, C., Serrano, L., and Save, R. 1993. The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens 14:1887–1905.CrossRefGoogle Scholar
Richardson, A. J. and Everitt, J. H. 1992. Using spectra vegetation indices to estimate rangeland productivity. Geocart. Int 1:63–69.Google Scholar
Roberts, D. A., Adams, J. B., and Smith, M. O. 1993. Discriminating green vegetation, non-photosynthetic vegetation and soils in AVIRIS data. Remote Sens. Environ 44:255–270.Google Scholar
Roberts, D. A., Green, R. O., and Adams, J. B. 1997. Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sens. Environ 62:223–240.CrossRefGoogle Scholar
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W. 1973. Monitoring vegetation systems in the great plains with ERTS. Pages 309–317 in Third ERTS Symposium, NASA SP-351, Volume 1. Washington DC: NASA.Google Scholar
Steinmetz, S., Guerif, M., Delecolle, R., and Baret, F. 1990. Spectral estimates of the absorbed photosynthetically active radiation and light-use efficiency of a winter wheat crop subjected to nitrogen and water deficiencies. Int. J. Remote Sens 11:1797–1808.Google Scholar
Thenkabail, P. 2002. Optimal hyperspectral narrowbands for discriminating agricultural crops. Remote Sens. Rev 20:257–291.Google Scholar
Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ 8:127–150.Google Scholar
Tucker, C. J. 1980. Remote sensing of leaf water content in the near infrared. Remote Sens. Environ 10:23–32.Google Scholar
Unganai, L. S. and Kogan, F. N. 1998. Drought monitoring and corn yield estimation in Southern Africa from AVHRR data. Remote Sens. Environ 63:219–232.CrossRefGoogle Scholar