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

Rasmus Houborg, Matthew McCabe, Feng Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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 languageEnglish (US)
Title of host publication2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3317-3320
Number of pages4
ISBN (Print)9781479979295
DOIs
StatePublished - Nov 12 2015

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