Comparison of Remote Sensing-Based LAI Estimation Techniques for Assimilation Into Crop Growth Simulations.

Leaf area index (LAI) is a crop biophysical variable that can be estimated using remote sensing techniques.  It is also a primary state variable in the crop growth simulation of many cropping systems models.  With the development of appropriate techniques to merge LAI estimates from remote sensing into crop model simulations, we can improve simulations of key model output variables, such as yield and evapotranspiration.  Our objective was to investigate several techniques for estimating the LAI of wheat in a central Arizona plot study that included four replicates of six wheat varieties and five nitrogen rates.  Hyperspectral measurements of plant canopy reflectance were obtained weekly using both the GER 1500 and the ASD FieldSpec Pro field spectroradiometers.  Spectral reflectance was also measured using a broad-band, active sensor (Crop Circle).  Two algorithms, including the PROSAIL canopy reflectance model and a simpler empirical model, were used to estimate LAI from the spectral reflectance data.  Direct LAI measurements were also obtained weekly using a Li-Cor LAI-2000 Plant Canopy Analyzer.  Biomass was destructively sampled on five occasions over the growing season, and LAI was derived from leaf area measurements using a Li-Cor LI-3100 area meter.  The various methodologies and algorithms for estimation of leaf area index were assessed and compared in light of their usefulness for LAI data assimilation into crop growth simulations.