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Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data

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Prasad S Thenkabail, Isabella Mariotto, Murali Krishna Gumma, Tonya E Middleton, Joshua D Landis, and Karl (Fred) Huemmrich, 2013, Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Summary

The 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 [...]

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  • Global Croplands and Their Water Use for Food Security in the Twenty-first Century
  • John Wesley Powell Center for Analysis and Synthesis

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doi http://www.loc.gov/standards/mods/mods-outline-3-5.html#identifier 10.1109/JSTARS.2013.2252601

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citationTypeJournal
editionVol. 6, No. 2
journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
noteThenkabail, P.S., Mariotto, I., Gumma, M.K., Middleton, E.M., Landis, and D.R., Huemmrich, F.K. (2013). Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6, No. 2, April 2013 doi: 10.1109/JSTARS.2013.2252601

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