I
ntroduction
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.
H
yperspectral
R
emote
S
ensing
of
V
egetation
and
A
gricultural
C
rops
Prasad S. Thenkabail,
Murali Krishna Gumma,
Pardhasaradhi Teluguntla, and
Irshad A. Mohammed
E
volution
of
H
yperspectral
S
ensors
Detailed discussions on hyperspectral sensors on various platforms can be
found in a number of publications (Ortenberg, 2011; Qi, 2011; Staenz and Held,
2012; Verrelst
et al.
, 2012; Cook
et al.
, 2013; Middleton
et al.
, 2013). An over-
whelming proportion of hyperspectral data of vegetation and agricultural crops
hitherto has been based on hand-held spectrometers such as the Analytical
Spectral Devices (ASD, 2013) suite of instruments as a result of their easy use,
absence of hindrance from cloud cover, and as a result of high cost of airborne
systems and very few existing spaceborne systems (e.g., Thenkabail
et al.
, 2000;
“Hyperspectral Remote
Sensing (or Imaging
Spectroscopy) is the future
of remote sensing, providing
continuous data along the
electromagnetic spectrum
(spectral signatures of
objects) rather than few
data points averaged over
broad wavelengths”
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
August 2014
697
