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Detecting weed-free and weed-infested areas of a soybean field using near-infrared spectral data

Published online by Cambridge University Press:  20 January 2017

Jiyul Chang ,
Sharon A. Clay ,
David E. Clay  and
Kevin Dalsted
Jiyul Chang
Affiliation:
Plant Science Department, South Dakota State University, Brookings, SD 57007
David E. Clay
Affiliation:
Plant Science Department, South Dakota State University, Brookings, SD 57007
Kevin Dalsted
Affiliation:
Engineering Resource Center, South Dakota State University, Brookings, SD 57007
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Abstract

Weed distribution maps can be developed from remotely sensed reflectance data if collected at appropriate times during the growing season. The research objectives were to determine if and when reflectance could be used to distinguish between weed-free and weed-infested (mixed species) areas in soybean and to determine the most useful wavebands to separate crop, weed, and soil reflectance differences. Treatments included no vegetation (bare soil), weed-free soybean, and weed-infested soybean and, in 1 yr, 80% corn residue cover. Reflectance was measured at several sampling times from May through September in 2001 and 2002 using a handheld multispectral radiometer equipped with band-limited optical interference filters (460 to 1,650 nm). The spatial resolution was 0.8 m2. The reflectance in the visible spectral range (460 to 700 nm) generally was similar among treatments. In the near-infrared (NIR) range (> 700 to 1,650 nm), differences among treatments were observed from soybean growth stage V-3 (about 4 wk after planting) until mid-July to early August depending on crop vigor and canopy closure (76-cm row spacing in 2001 and 19-cm row spacing in 2002). Reflectance rankings in the NIR range when treatments could be differentiated were consistent between years and, from lowest to highest reflectance, were soil < weed-free < weed-infested areas. Increased reflectance from weed-infested areas was most likely due to increased biomass and canopy cover. Residue masked differences between weed-free and weed-infested areas during the early stages of growth due to high reflectance from the residue and reduced weed numbers in these areas. These results suggest that NIR spectral reflectance collected before canopy closure can be used to distinguish weed-infested from weed-free areas.

Keywords

Soybean, Glycine max (L.) MerrMultispectral dataremote sensingreflectance characteristics
Type
Weed Management
Information
Weed Science , Volume 52 , Issue 4 , August 2004 , pp. 642 - 648
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L., and Royo, C. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J 92:83–91.Google Scholar
Avery, T. E. and Berlin, G. L. 1992. Fundamentals of remote sensing and airphoto interpretation. 5th ed. Upper Saddle River, NJ: Prentice-Hall. 472 p.Google Scholar
Bahrun, A., Mogensen, V. O., and Jensen, C. R. 2003. Water stress detection in field-grown maize by using spectral vegetation index. Comm. Soil Sci. Plant Anal 34:65–79.Google Scholar
Barnes, E. M., Clarke, T. R., and Richards, S. E. et al. 2001. Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data [CD-ROM computer file]. in Robert, P. C., et al., eds. Proceedings of the 5th International Conference on Precision Agriculture; Bloomington, MN; July 16–19, 2000. Madison, WI: ASA.Google Scholar
Blackmer, T. M., Schepers, J. S., Varvel, G. E., and Meyer, G. E. 1996a. Analysis of aerial photography for nitrogen stress within a corn field. Agron. J 88:729–733.CrossRefGoogle Scholar
Blackmer, T. M., Schepers, J. S., Varvel, G. E., and Walter-Shea, E. A. 1996b. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopy. Agron. J 88:1–5.Google Scholar
Broulik, B. L., Lems, G. J., Clay, S. A., and Clay, D. E. 1997. Analysis of spatial distribution of Canada thistle (Cirsium arvense) in no-till soybean (Glycine max). Proc. SD Acad. Sci 76:159–169.Google Scholar
Brown, R. B., Steckler, J. P. G. A., and Anderson, G. W. 1994. Remote sensing for identification of weeds in no-till corn. Trans. ASAE 37:297–302.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci 45:364–373.Google Scholar
Chang, J., Clay, D. E., Dalsted, K., Clay, S., and O'Neill, M. 2003. Corn (Zea mays L.) yield prediction using multispectral and multidate reflectance. Agron. J 95:1447–1453.CrossRefGoogle 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.Google Scholar
Clay, S. and Johnson, G. 1999. Scouting for weeds. SSMG-15. in Clay, D. E., Armstrong, D., Darst, R., Griffen, K., Reese, C., and Reitsma, K., eds. Site Specific Management Guideline. Norcross, GA: Potash and Phosphate Institute. Website: www.ppi-far.org/ssmg. Accessed: January 2004.Google Scholar
Dalsted, K. and Queen, L. 1999. Interpreting remote sensing data. SSMG-26. in Clay, D. E., Armstrong, D., Darst, R., Griffen, K., Reese, C., and Reitsma, K., eds. Site Specific Management Guideline. Norcross, GA: Potash and Phosphate Institute. www.ppi-far.org/ssmg.Google Scholar
Fairve, R. and Fischer, A. 1997. Predicting crop reflectances using satellite data observing mixed pixels. J. Agric. Biol. Environ. Stats 2:87–107.Google Scholar
Huete, A. R., Jackson, R. D., and Post, D. F. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ 17:37–53.Google Scholar
Johnson, G. A., Mortensen, D. A., and Gotway, C. A. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci 44:704–710.CrossRefGoogle Scholar
LaMastus, F. E., Smith, C. M., Shaw, D. R., and King, R. P. 2001. Potential for weed species differentiation using remote sensing. [CD-ROM computer file]. in Robert, P. C., et al., eds. Proceedings of the 5th International Conference on Precision Agriculture; Bloomington, MN; July 16–19, 2000. Madison, WI: ASA.Google Scholar
Lems, G. J. 1998. Weed Spatial Variability and Management on a Field-Wide Scale. . South Dakota State University, Brookings, SD. 105 p.Google Scholar
Ma, B. L., Dwyer, L. M., Costa, C., Cober, E. R., and Morrison, M. J. 2001. Early prediction of soybean yield from canopy reflectance measurements. Agron. J 93:1227–1234.Google Scholar
Marshall, E. J. P. 1988. Field-scale estimates of grass weed population in arable land. Weed Res 28:191–198.Google Scholar
Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max . Weed Sci 48:393–398.Google Scholar
Oki, K., Omasa, K., and Inamura, M. 2002. Separation of vegetation coverage and vigor for vegetation remote sensing by unmixing method: evaluation of unmixing method in laboratory experiment. J. Agric. Meteorol 58:33–39.Google Scholar
Price, J. C. 1992. Estimating vegetation amount from visible and near infrared reflectances. Remote Sens. Environ 41:29–34.Google Scholar
Richardson, A. J., Menges, R. M., and Nixon, P. R. 1985. Distinguishing weed from crop plants using video remote sensing. Photogramm. Eng. Remote Sens 51:1785–1790.Google Scholar
Ritchie, S. W., Hanway, J. J., Thompson, H. E., and Benson, G. O. 1997. How a soybean plant develops. in Herman, J. C., ed. Special Rep. 53. Ames, IA: Iowa State Cooperative Extension, Iowa State University.Google Scholar
[SAS] Statistical Analysis Systems. 1996. SAS User's Guide. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Sembiring, H., Raun, W. R., Johnson, G. V., Stone, M. L., Solie, J. B., and Phillips, S. B. 1998. Detection of nitrogen and phosphorus nutrient status in bermudagrass using spectral radiance. J. Plant Nutr 21:1198–1206.Google Scholar
Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., Schlemmer, M. R., and Major, D. J. 2001. Use of remote-sensing images to estimate corn grain yield. Agron. J 93:583–589.Google Scholar
Strachan, I. B., Pattey, E., and Biosvert, J. B. 2002. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sens. Environ 80:213–224.Google Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot 9:337–342.Google Scholar
Verhoeye, J. and DeWulf, R. 2002. Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sens. Environ 79:96–104.Google Scholar
Wang, D. and Shannon, M. C. 2001. Detecting salinity effect on soybean growth using a multi-spectral radiometer. [CD-ROM computer file]. in Robert, P. C., et al., eds. Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN. 16–19 July 2000. Madison, WI: ASA.Google Scholar
Zwiggelaar, R. 1998. A review of spectral properties of plants and their uses for crop/weed discrimination in row-crops. Crop Prot 17:189–206.Google Scholar