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dc.contributorMariotto, Isabellaen_US
dc.contributorGumma, Murali Krishnaen_US
dc.contributorMiddleton, Elizabeth Men_US
dc.contributorLandis, David Ren_US
dc.contributorHuemmrich, Freden_US
dc.creatorThenkabail, Prasaden_US
dc.date2013-05-13en_US
dc.date.accessioned2017-01-05T19:41:12Z
dc.date.available2017-01-05T19:41:12Z
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifier.citationPrasad Thenkabail, Isabella Mariotto, Murali Krishna Gumma, Elizabeth M Middleton, David R Landis, Fred Huemmrich. (13/5/2013). Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Re ectance and Hyperion/EO-1 Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6 (2), pp. 427-439.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/5228
dc.description.abstractThe overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world’s main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks’ Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Datawere collected for the eight crop types in six distinct growth stages. These included (a) eld spectroradiometer measurements (350–2500 nm) sampled at 1-nm discrete bandwidths, and (b) eld biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classi ed using 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was 25% better than the multi-spectral results possible fromLandsat-7’s Enhanced Thematic Mapper+ or EO-1’s Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of speci c two-band normalized difference HVIs to best model and study speci c biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identi ed in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The ndings of this study will make a signi cant contribution to future hyperspectral missions such as NASA’s HyspIRI.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;6,(2013) Pagination 427,439en_US
dc.subjecthyperionen_US
dc.subjectimaging spectroscopyen_US
dc.subjecthyspirien_US
dc.subjectbiophysical parametersen_US
dc.subjecthyperspectral vegetation indicesen_US
dc.subjecthyperspectral narrowbandsen_US
dc.subjectbroadbandsen_US
dc.subjectfield reflectanceen_US
dc.titleSelection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Re ectance and Hyperion/EO-1 Dataen_US
dc.typeJournal Articleen_US
cg.contributor.centerU.S. Geological Survey - USGSen_US
cg.contributor.centerThe University of Texas at El Paso - UTEPen_US
cg.contributor.centerInternational Crops Research Institute for the Semi-Arid Tropics - ICRISATen_US
cg.contributor.centerNational Aeronautics and Space Administration - NASAen_US
cg.contributor.centerUniversity of Maryland, Baltimore County - UMBCen_US
cg.contributor.crpCGIAR Research Program on Dryland Systems - DSen_US
cg.contributor.funderCGIAR System Organization - CGIARen_US
cg.date.embargo-end-dateTimelessen_US
cg.coverage.regionSouthern Asiaen_US
cg.coverage.regionNorthern Africaen_US
cg.coverage.countryINen_US
cg.coverage.countrySDen_US
cg.contactpthenkabail@usgs.goven_US
cg.identifier.doihttps://dx.doi.org/10.1109/JSTARS.2013.2252601en_US
dc.identifier.statusTimeless limited accessen_US
mel.sub-typeManuscript-unpublisheden_US
mel.impact-factor2.777en_US


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