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Hyperspectral remote sensing of vegetation and agricultural crops

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Relation http://oar.icrisat.org/11984/
 
Title Hyperspectral remote sensing of vegetation and agricultural crops
 
Creator Thenkabail, P S
Gumma, M K
Teluguntla, P
Irshad, A M
 
Subject Remote Sensing
GIS Techniques/Remote Sensing
 
Description There are now over 40 years of research in hyperspectral remote sensing (or
imaging spectroscopy) of vegetation and agricultural crops (Thenkabail et
al., 2011a). Even though much of the early research in hyperspectral remote
sensing was overwhelmingly focused on minerals, now there is substantial
literature in characterization, monitoring, modeling, and mapping of vegetation
and agricultural crops using ground-based, platform-mounted, airborne,
Unmanned Aerial Vehicle (UAV) mounted, and spaceborne hyperspectral
remote sensing (Swatantran et al., 2011; Atzberger, 2013; Middleton et al., 2013;
Schlemmer et al., 2013; Thenkabail et al., 2013; Udelhoven et al., 2013; Zhang
et al., 2013). The state-of-the-art in hyperspectral remote sensing of vegetation
and agriculture shows significant enhancement over conventional remote
sensing, leading to improved and targeted modeling and mapping of specific
agricultural characteristics such as: (a) biophysical and biochemical quantities
(Galvão, 2011; Clark and Roberts, 2012), (b) crop type\species (Thenkabail
et al., 2013), (c) management and stress factors such as nitrogen deficiency,
moisture deficiency, or drought conditions (Delalieux et al., 2009; Gitelson,
2013; Slonecker et al., 2013), and (d) water use and water productivities
(Thenkabail et al., 2013). At the same time, overcoming Hughes’ phenomenon
or curse of dimensionality of data and data redundancy (Plaza et al., 2009)
is of great importance to make rapid advances in a much wider utilization of
hyperspectral data. This is because, for a specific application, a large number
of hyperspectral bands are redundant (Thenkabail et al., 2013). Selecting the
relevant bands will require the use of data mining techniques (Burger and
Gowen, 2011) to focus on utilizing the optimal or best ones to maximize the
efficiency of data use and reduce unnecessary computing.
 
Publisher Amer SOC Photogrammetry
 
Date 2014-08
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11984/1/06_PERS-hyperspectral-anlaysis.pdf
Thenkabail, P S and Gumma, M K and Teluguntla, P and Irshad, A M (2014) Hyperspectral remote sensing of vegetation and agricultural crops. Photogrammetric Engineering & Remote Sensing (TSI), 80 (8). pp. 695-723. ISSN 0099-1112