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Agronomy Journal Abstract - Remote Sensing

Remote Sensing of Canopy Dynamics and Biophysical Variables Estimation of Corn in Michigan


This article in AJ

  1. Vol. 97 No. 1, p. 99-105
    Received: Oct 24, 2003

    * Corresponding author(s): elwadiem@msu.edu
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  1. M. Eldaw Elwadie *a,
  2. Francis J. Pierceb and
  3. J. Qic
  1. a Dep. of Crop and Soil Sciences, Michigan State Univ., East Lansing, MI 48824
    b Center for Precision Agricultural Systems, Washington State Univ., Prosser, WA 99350-8694
    c Dep. of Geography, Michigan State Univ., East Lansing, MI 48824


Remotely sensed data can aid in estimating biophysical variables of corn (Zea mays L.). This study identifies spectral wavelengths, spectral vegetation indices (SVIs), and timing needed for estimating yield and leaf area index (LAI) for corn. Canopy reflectance (460–810 nm range) was measured periodically in 1999 and 2000 within a field study varying N and irrigation management for corn. Corn grain yield was strongly related to canopy reflectance for either individual wavelengths or for SVIs, reaching an optimum (R 2 > 0.9) at R5 dent stage in both years. Green reflectance based on simple ratio (green simple ratio index, GSRI) had the highest R 2, lowest RMSE, and most consistent slope and intercept between years. In contrast, LAI was best predicted by normalized difference vegetation index (NDVI) (RSME = 0.426) while green normalized difference vegetation index (GNDVI) performed poorly (RMSE = 0.604). Corn grain yield in this study was best predicted at stage R5 using the green simple ratio index.

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