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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #165588

Title: COMPARISON OF REMOTE SENSING AND CROP GROWTH MODELS FOR ESTIMATING WITHIN-FIELD LAI VARIABILITY

Author
item HONG, S
item Sudduth, Kenneth - Ken
item Kitchen, Newell
item FRAISSE, C
item PALM, H
item WIEBOLD, W

Submitted to: Korean Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/27/2004
Publication Date: 8/1/2004
Citation: Hong, S.Y., Sudduth, K.A., Kitchen, N.R., Fraisse, C.W., Palm, H.L., Wiebold, W.J. 2004. Comparison of remote sensing and crop growth models for estimating within-field LAI variability. Korean Journal of Remote Sensing. 20(3):175-188.

Interpretive Summary:

Technical Abstract: The objective of this study was 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 corn (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 (r2 = 0.59 to 0.61 for corn; r2 = 0.66 to 0.68 for soybean) in this single-year dataset. CERES-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 LAI for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. For these data, greater improvements might be expected with corn than with soybean.