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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 Re ectance and Hyperion/EO-1 Data

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Title 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
 
Creator Thenkabail, Prasad
 
Contributor Mariotto, Isabella
Gumma, Murali Krishna
Middleton, Elizabeth M
Landis, David R
Huemmrich, Fred
 
Subject hyperion
imaging spectroscopy
hyspiri
biophysical parameters
hyperspectral vegetation indices
hyperspectral narrowbands
broadbands
field reflectance
 
Description 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 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.
 
Date 2013-05-13
2017-01-05T19:41:12Z
2017-01-05T19:41:12Z
 
Type Journal Article
 
Identifier https://mel.cgiar.org/dspace/limited
Prasad 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.
https://hdl.handle.net/20.500.11766/5228
Timeless limited access
 
Language en
 
Rights CC-BY-NC-4.0
 
Format PDF
 
Publisher Institute of Electrical and Electronics Engineers (IEEE)
 
Source IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;6,(2013) Pagination 427,439