water vapor, and other background information. Although
the author conducted pretreatment, such as smooth denois-
ing, analyses were affected by the soil, water vapor, and other
background information; but it just eliminates part of the
background noise. The spectral characteristics of vegetation
leaves will still be weakened; this reduces the universality
and reliability of the model. In future studies, we could use
the
ASD
spectrometer of the hand-held leaf blade spectrum
detector to measure the vegetation spectrum. The hand-held
leaf blade spectrum detector is equipped with a quartz halide
lamp, and stable source; it is not affected by the weather
because of the built-in light source. It is easy to be measured
under different conditions. The probe placed blade in blade
clip when measuring, then put the blade clamping, ensure
the blade is placed horizontally; this operation can eliminate
background information, obtain the vegetation leaf spectra of
high precision, thus to enhance the universality and reliabil-
ity of the model (Lin
et al
., 2011).
Hyperspectral image data has a high resolution, which
could obtain continuous spectral features to achieve the
map unification. The calculated vegetation water index can
improve the inversion accuracy of
VWC
, which based on the
hyperspectral image. In the next step, hyperspectral image
data can be used for the inversion and validation of other
biochemical parameters.
Conclusions
This research analyzed the spectral characteristics of vegeta-
tion under different leaf water content, and investigated the
vegetation indices for estimating the
VWC
of
Halophyte.
The
major conclusions are:
1. The absorption bands were mainly centered at 380 to 400
nm, 680 to 720 nm, 1420 to 1450 nm, 1900 to 1940 nm,
and 2450 to 2500 nm, when six plants with different leaf
water contents were used. The spectral curves decreased
with increasing
VWC
.
These changes were affected by the
change in
VWC
, as well as by the material composition of
different vegetation leaves.
2. The vegetation types could be effectively identified by
characteristic bands. The water contents of most types of
vegetation were difficult to identify based on first order
differential treatment.
3. The newly developed two-band vegetation indices
DVI
(1712,1382)
,
NDSI
(2201,1870)
and
RSI
(2259,1870)
showed good applica-
bility for estimating
VW
. Compared with the estimations
using only one vegetation index, the linear combination of
DVI
(1712,1382)
,
NDSI
(2201,1870)
and
RSI
(2259,1870)
improved the estima-
tion of
VWC
.
However, the published spectral indices had a poor
correlation with
VWC
. Therefore, the combination of newly
developed vegetation indices are recommended as a potential
indicator for estimating
VWC
.
Acknowledgments
The research was carried out with the financial support
provided by the Xinjiang Local Outstanding Young Talent
Cultivation Project of National Natural Science Foundation
of China (U1503302), the Natural Science Foundation of
Xinjiang Uygur Autonomous Region, China (2016D01C029),
National Natural Science Foundation of China (41361045),
and Scientific and technological talent training program of
Xinjiang Uygur Autonomous Region. The authors wish to
thank the referees for providing helpful suggestions to im-
prove this manuscript.
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