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  1. Vol. 52 No. 2, p. 849-857
     
    Received: Apr 25, 2011
    Published: Mar, 2012


    * Corresponding author(s): samwang@ag.arizona.edu
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doi:10.2135/cropsci2011.04.0222

Association of Spectral Reflectance Indices with Plant Growth and Lint Yield in Upland Cotton

  1. Mario Gutierreza,
  2. Randall Nortonb,
  3. Kelly R. Thorpc and
  4. Guangyao Wang *a
  1. a School of Plant Sciences, Maricopa Agricultural Center, Univ. of Arizona, 37860 W. Smith-Enke Rd., Maricopa, AZ 85138
    b Safford Agricultural Center, Univ. of Arizona, 2134 S. Montierth Lane, Safford, AZ 85546
    c U.S. Arid Land Agricultural Research Center, USDA-ARS, 21881 North Cardon Lane, Maricopa, AZ 85138

Abstract

Canopy reflectance plays an increasingly important role in crop management and yield prediction at large scale. The relationship of four spectral reflectance indices with cotton (Gossypium hirsutum L.) biomass, leaf area index (LAI), and crop yield were investigated using three cotton varieties and five N rates in the irrigated low desert in Arizona during the 2009 and 2010 growing seasons. Biomass, LAI, and canopy reflectance indices (normalized difference vegetation index [NDVI], simple ratio [SR], near-infrared index [NIR], and ratio vegetation index [RVI]) were determined at different growth stages. The commonly used NDVI and the other three canopy reflectance indices explained over 87% variation in cotton biomass (all R2 > 0.87) and LAI (R2 > 0.93). Indices SR, NIR, and RVI all had higher coefficients of determination (R2) compared to NDVI because these indices were not saturated at late growth stages. There was no significant relationship between lint yield and the spectral indices measured at early growth stages. However, the spectral indices determined at peak bloom showed significant correlations with lint yield. Indices SR, NIR, and RVI explained 56, 60, and 58% of variations in cotton lint yield, respectively, while NDVI only explained 47% of variation in lint yield. This study suggests canopy reflectance indices can be used to predict cotton lint yield at peak bloom and the accuracy of yield prediction can be significantly improved when SR, NIR, and RVI are used.

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Copyright © 2012. Copyright © by the Crop Science Society of America, Inc.