Agriculture Reference
In-Depth Information
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methods of data sampling and processing, communication and random
errors), their removal is crucial for improving data interpretation. There-
fore, the initial processing included postlaunch calibration of VIS and NIR,
calculation of NDVI, and converting IR radiance to brightness temperature
(BT), which was corrected for nonlinear behavior of the sensor (Rao and
Chen, 1995, 1999). The three-channel algorithm routines included a com-
plete removal of high-frequency noise from NDVI and BT values, stratifica-
tion of world ecosystems, and detection of medium-to-low frequency fluc-
tuations in vegetation condition associated with weather variations (Ko-
gan, 1997). These steps were crucial in order to use AVHRR-based indices
as a proxy for temporal and spatial analysis and interpretation of weather-
related vegetation condition and health.
Finally, three indices characterizing moisture (VCI), thermal (TCI), and
vegetation health (VT) conditions were constructed following the princi-
ple of comparing a particular year NDVI and BT with the entire range
of their variation during the extreme (favorable/unfavorable) conditions.
Based on the LOM, LOT, and CC, the extreme conditions were derived by
calculating the maximum (max) and minimum (min) NDVI and BT values
us
ing 14-year satellite data. The maximum/minimum criteria were used to
cl
assify carrying capacity of ecosystems in response to climate and weather
va
riations. The VCI, TCI, and VT were formulated as:
[81],
Line
——
10.
——
Norm
PgEn
VCI
=
[
(
NDVI
−
NDVI
min
)/(
NDVI
max
−
NDVI
min
)
]100
[1]
=
[
(
BT
max
−
BT
)/(
BT
max
−
BT
min
)
]100
TCI
[2]
[81],
VT
=
a
(
VCI
)
+
(
1
−
a
)
TCI
[3]
w
here NDVI, NDVI
max
, and NDVI
min
are the smoothed weekly NDVI,
its
multiyear absolute maximum and minimum, respectively; BT, BT
max
,
an
d BT
min
are similar values for brightness temperature; and
a
is a coeffi-
ci
ent that quantifies a share of VCI and TCI contribution to the vegetation
co
ndition. For example, if other conditions are near normal, vegetation is
m
ore sensitive to moisture during canopy formation (leaf appearance) and
to
temperature during flowering. Therefore, the share of moisture contri-
bu
tion into the total vegetation condition is higher than temperature dur-
in
g leaf canopy formation and lower during flowering. Because moisture
an
d temperature contributions during a vegetation cycle are currently not
kn
own, the share of weekly VCI and TCI can be assumed to be equal.
Table 6.1 explains the algorithm development. As seen in the first row,
bo
th NDVI and BT data fluctuate considerably, primarily due to clouds,
su
n-sensor position, bidirectional reflectance, and random noise. In June,
for example, clouds triggered considerable reduction in NDVI and BT;
but such a reduction in August was smaller. The smoothing procedure
(second row) eliminates outliers and emphasizes seasonal cycle. Since the
smoothed NDVI was close to multiyear maximum (third row) values and
BT was close to minimum (fourth row) values, VCI, TCI, and VT are
>
60,
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