Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Comparing LAI Estimates of Corn and Soybean from Vegetation Indices of Multi-resolution Satellite Images

  • Kim, Sun-Hwa (National Academy of Agricultural Science, Rural Development Administration (RDA)) ;
  • Hong, Suk Young (National Academy of Agricultural Science, Rural Development Administration (RDA)) ;
  • Sudduth, Kenneth A. (Cropping Systems and Water Quality Research Unit, USDA-ARS) ;
  • Kim, Yihyun (National Academy of Agricultural Science, Rural Development Administration (RDA)) ;
  • Lee, Kyungdo (National Academy of Agricultural Science, Rural Development Administration (RDA))
  • Received : 2012.10.18
  • Accepted : 2012.11.07
  • Published : 2012.12.31
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Abstract

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

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References

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