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.
|Original language||English (US)|
|Title of host publication||2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||4|
|State||Published - Nov 12 2015|