Leaf area index (LAI) is a key parameter in most land surface models. Models that operate at multiple spatial scales may require consistent LAI inputs at different spatial resolutions or from different sensors. For example, the atmosphere-land exchange inverse model and associated disaggregation algorithm (DisALEXI) use the moderate resolution imaging spectroradiometer (MODIS) LAI product to model fluxes at regional scales (1- to 10-km grid resolution), and Landsat-based LAI to disaggregate to field scale (30-m grid). In order to make a MODIS-consistent LAI product from Landsat imagery for this combined scheme, a simple reference-based regression tree approach was developed. This approach uses homogeneous and high-quality LAI retrievals from MODIS as references to develop a regression tree relating these MODIS LAI samples to Landsat surface reflectances. Results show that the approach can produce accurate estimates of LAI from Landsat, as evaluated using field measurements collected during the soil moisture experiment of 2002, conducted in central Iowa during a period of rapid vegetation growth. The coefficient of determination (r2) computed between Landsat retrievals and field measurements was 0.94 at the field scale, with an overall mean bias error (MBE) of −0.07 and mean absolute difference (MAD) of 0.23. MAD values of 0.17 and 0.32 were obtained for low to moderate LAI (0-3) and high LAI (>3), respectively, with some underestimation for the high LAI (MBE = −0.28). The LAI maps retrieved from Landsat were consistent with the MODIS estimates when aggregated to coarser scales. MAD computed between Landsat- and MODIS-derived LAI ranged from 0.07 to 0.83 for different Landsat dates, with no significant bias compared to MODIS high-quality retrievals. This approach demonstrates a simple framework for producing MODIS-consistent LAI from Landsat data for modeling the land surface at different spatial scales.