Use of Remote Sensing to Detect Soybean Cyst Nematode-Induced Plant Stress


  • F. W. Nutter, Jr.
  • G. L. Tylka
  • J. Guan
  • A. J. D. Moreira
  • C. C. Marett
  • T. Rosburg


detection, heterodera glycines, nematode, management, plant disease losses, remote sensing, soybean cyst nematode


Integrating remote sensing and geographic information systems (GIS) technologies offers tremendous opportunities for farmers to more cost effectively manage the causes of crop stress. Initial soybean cyst nematode (SCN) population densities from 995 2-×-3-m quadrats were obtained from a soybean field near Ames, Iowa, in 2000. The percentage of sunlight reflected from each quadrat was measured weekly using a multispectral radiometer beginning in mid-May and continuing through mid-September. Aerial images were obtained at heights above the field ranging from 45 to 425 m on 12 dates during the soybean growing season. This was accomplished using color film and infrared film in conjunction with a filter to measure reflectance in the near-infrared region (810 nm). Satellite images (Landsat 7) were obtained for five dates during the 2000 growing season. Maps depicting initial SCN population densities, soybean yield, soy oil, and soy protein were generated using the GIS software program ArcView. Percentage reflectance (810 nm), aerial image intensity, and satellite image intensity data then were regressed against soybean yield, soy oil, and soy protein concentrations obtained from each geospatially referenced soybean quadrat. Percentage reflectance measurements explained up to 60% of the variation in initial SCN population densities within soybean quadrats and up to 91% of the variation in soybean yield. Aerial image and satellite image intensities explained up to 80% and 47% of the variation in soybean yield, respectively. Percentage reflectance data also explained 36% and 54% of the variation in oil and protein concentrations of the harvested soybeans, respectively. These results indicate that remote sensing coupled with GIS technologies may provide new tools to detect and quantify SCN population densities and their impacts on the quantity and quality of soybean yield.