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

Handle URI:
http://hdl.handle.net/10754/621250
Title:
Downscaling of coarse resolution LAI products to achieve both high spatial and temporal resolution for regions of interest
Authors:
Houborg, Rasmus; McCabe, Matthew ( 0000-0002-1279-5272 ) ; Gao, Feng
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.
KAUST Department:
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Citation:
Houborg 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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Conference/Event name:
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Issue Date:
12-Nov-2015
DOI:
10.1109/IGARSS.2015.7326528
Type:
Conference Paper
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorHouborg, Rasmusen
dc.contributor.authorMcCabe, Matthewen
dc.contributor.authorGao, Fengen
dc.date.accessioned2016-11-03T06:56:27Z-
dc.date.available2016-11-03T06:56:27Z-
dc.date.issued2015-11-12en
dc.identifier.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.en
dc.identifier.doi10.1109/IGARSS.2015.7326528en
dc.identifier.urihttp://hdl.handle.net/10754/621250-
dc.description.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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectEarthen
dc.subjectIndexesen
dc.subjectMODISen
dc.subjectReflectivityen
dc.subjectRemote sensingen
dc.subjectSatellitesen
dc.subjectSpatial resolutionen
dc.titleDownscaling of coarse resolution LAI products to achieve both high spatial and temporal resolution for regions of interesten
dc.typeConference Paperen
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST), Saudi Arabiaen
dc.identifier.journal2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)en
dc.conference.date26 July 2015 through 31 July 2015en
dc.conference.nameIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015en
dc.contributor.institutionUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United Statesen
kaust.authorHouborg, Rasmusen
kaust.authorMcCabe, Matthewen
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