Downscaling of coarse resolution LAI products to achieve both high spatial and temporal resolution for regions of interest
KAUST DepartmentWater Desalination and Reuse Research Center (WDRC)
Biological and Environmental Sciences and Engineering (BESE) Division
Environmental Science and Engineering Program
Online Publication Date2015-11-12
Print Publication Date2015-07
Permanent link to this recordhttp://hdl.handle.net/10754/621250
MetadataShow full item record
AbstractThis 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.
CitationHouborg R, McCabe MF, Gao F (2015) Downscaling of coarse resolution LAI products to achieve both high spatial and temporal resolution for regions of interest. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Available: http://dx.doi.org/10.1109/IGARSS.2015.7326528.
Conference/Event nameIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015