Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based on hyperspectral reflectance data (Spectral resolution <10 nm) have been successfully used to predict LAI without the need for cost intensive ground measurements. However, hyperspectral data are expensive, challenging to use, and not widely available. The objective of this study was to determine whether radiative transfer model inversion based on readily available, broad band multispectral imagery (Spectral resolution >40 nm) is suitable for predicting LAI. In 2006, LAI measurements were taken on the ground at the early heading stage of growth in two eastern Oregon fields planted to spring wheat (Triticum aestivum L.). Broad band aerial imagery was used to invert the SAIL+PROSPECT radiative transfer model. Measured and predicted LAI values were highly correlated (RMSE of 0.32 and r2 = 0.67 in field 1; and RSME of 0.47 and r2 = 0.64 in field 2). The results indicate that radiative transfer model inversion based on readily available broad-band multispectral data might provide acceptable estimates of LAI in dryland spring wheat.