Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Comparison of Remote Sensing and Crop Growth Models for Estimating Within-Field LAI Variability

  • Hong, Suk-Young (National Institute of Agricultural Science and Technology, Rural Development Administration) ;
  • Sudduth, Kenneth-A. (Cropping Systems and Water Quality Research Unit, USDA-ARS) ;
  • Kitchen, Newell-R. (Cropping Systems and Water Quality Research Unit, USDA-ARS) ;
  • Fraisse, Clyde-W. (Agricultural and Biological Engineering Department, University of Florida) ;
  • Palm, Harlan-L. (Agronomy Department, University of Missouri) ;
  • Wiebold, William-J. (Agronomy Department, University of Missouri)
  • Published : 2004.06.01
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Abstract

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

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References

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