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This article in AJ

  1. Vol. 75 No. 6, p. 987-993
    Received: Dec 9, 1982



Relation of Agronomic and Multispectral Reflectance Characteristics of Spring Wheat Canopies1

  1. J. S. Ahlrichs and
  2. M. E. Bauer2



Remote sensing technology is nearing operational use for area estimation of wheat (Triticum aestivum L.). However, the relationships between crop canopy variables such as leaf area index (LAI) which might be used in large-scale applications of growth and yield models of wheat and other crops and their multispectral reflectance properties are not well defined. The objective of this investigation was to identify these relationships and assess the potential for estimating selected canopy variables from remotely sensed reflectance measurements. Reflectance spectra over the 0.4 to 2.5 µm wavelength range were acquired during each of the major development stages of spring wheat canopies at Williston, N.D., during the 1975, 1976, and 1977 seasons. The soil was a Williams loam (fine-loamy mixed typic Argiboroll). Treatments in the experiment included planting date, N fertilization, cultivar, and soil moisture. Agronomic characterization of the canopies included measurements of development stage, plant height, fresh and dry biomass, LAI, and percent soil cover. Correlation and regression analyses were used to relate the agronomic variables to reflectance factor. High correlations were found between reflectance factor and percent soil cover, LAI, biomass, and plant water content. A near infrared wavelength band, 0.76 to 0.90 µm, was most important in explaining variation in green LAI and percent soil cover, while a middle infrared band, 2.08 to 2.35 µm, explained the most variation in fresh and dry biomass and plant water content. Transformations, including the near infrared/red reflectance ratio and greenness index, were also highly correlated with canopy variables. The relationship of canopy variables to reflectance was influenced by the development stage of the crop and decreased after anthesis. The canopy variables could be accurately predicted using measurements from three to five wavelength bands. The reflective wavelength bands of the thematic mapper sensor were more strongly related to and better predictors of the canopy variables than the Landsat MSS bands.

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