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dc.contributor.authorHouborg, Rasmus*
dc.contributor.authorMcCabe, Matthew*
dc.contributor.authorGao, Feng*
dc.date.accessioned2016-10-12T10:18:41Z
dc.date.available2016-10-12T10:18:41Z
dc.date.issued2015-12-12
dc.identifier.citationRasmus Houborg, Matthew F. McCabe, Feng Gao, A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI), International Journal of Applied Earth Observation and Geoinformation, Volume 47, May 2016, Pages 15-29, ISSN 0303-2434, http://dx.doi.org/10.1016/j.jag.2015.11.013.en
dc.identifier.issn0303-2434
dc.identifier.doi10.1016/j.jag.2015.11.013
dc.identifier.urihttp://hdl.handle.net/10754/620911
dc.description.abstractSatellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r2 = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.en
dc.description.sponsorshipThe research undertaken here was funded by the King Abdullah University of Science and Technology (KAUST).en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0303243415300568en
dc.rights© <2015>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectLandsaten
dc.subjectMODISen
dc.subjectSTARFMen
dc.subjectLAIen
dc.subjectData fusionen
dc.subjectDownscalingen
dc.subjectMulti-scaleen
dc.subjectRegression treeen
dc.subjectSpatio-temporal enhancementen
dc.subjectData miningen
dc.titleA Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI), International Journal of Applied Earth Observation and Geoinformationen
dc.typeArticleen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)*
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division*
dc.contributor.departmentEnvironmental Science and Engineering Program*
dc.contributor.departmentWater Desalination & Reuse Research Cntr*
dc.identifier.journalInternational Journal of Applied Earth Observation and Geoinformationen
dc.eprint.versionPost-printen
dc.contributor.institutionUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA*
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)*
kaust.personHouborg, Rasmus*
kaust.personMcCabe, Matthew*
refterms.dateFOA2017-12-11T00:00:00Z


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