Downscaling of coarse resolution LAI products to achieve both high spatial and temporal resolution for regions of interest

This paper presents a flexible tool for spatio-temporal enhancement of coarse resolution leaf area index (LAI) products, which is readily adaptable to different land cover types, landscape heterogeneities and cloud cover conditions. The framework integrates a rule-based regression tree approach for estimating Landsat-scale LAI from existing 1 km resolution LAI products, and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to intelligently interpolate the downscaled LAI between Landsat acquisitions. Comparisons against in-situ records of LAI measured over corn and soybean highlights its utility for resolving sub-field LAI dynamics occurring over a range of plant development stages.