ellite derived
homogeneous
sponse within
This spatial
it in the SAR
ng Definiens
the power
epresented as
tation, (5» ),
veg
of the model
ates LAI as a
model, SAR
le (©) can be
(I)
Q)
plumetric soil
(3)
(4)
canopy layer,
or coefficients
B,C,D and E
tal data. 4, B
type. E is a
ident on soil
as follows:
(5)
080)
vere available
n the WCM.
is determined
(2011) found
1.0 the SAR
butions, with
only minimally contributions from soil moisture. For example,
at a derived LAI of 3.0, 90% of the total canopy backscatter
originates from vegetation contributions at C-band. For the
AgriSAR data, the remaining parameters in the model (A, B,
and E) were simultaneously determined using a nonlinear least
squares method in the Matlab Curve Fitting Toolbox
environment, based on the Levenberg-marquardt algorithm.
A look up table (LUT) was produced based on the fitted WCM.
LAI values for the look up table ranged from 0 to 8.0, in
increments of 0.01. Soil moisture ranged from 0 to 50% in 0.5%
intervals. The LUT was subsequently used to invert the SAR
response. The K-nearest neighbour (KNN) search technique
was used to find the K closed points in the LUT to a set of
query points (in this case the SAR response). A KD tree was
build to facilitate more efficient searching of the LUT.
3. RESULTS AND DISCUSSION
3.1 Sensitivity Analysis
Correlations between RADARSAT-2 responses and optically
derived LAI are presented in Table 2 and Figure 1. As observed
for broadleaf crops, SAR parameters which characterize volume
scattering from the canopy are most sensitive to grain LAL
These parameters include the linear cross-polarization intensity
(HV), entropy and the volume scattering component derived
from the Freeman-Durden decomposition. Entropy is calculated
by the Cloude-Pottier decomposition and is a measure of the
randomness of scattering occurring within a target. As crops
emerge and biomass accumulates, the degree of randomness in
scattering would be expected to increase.
SAR parameter Wheat Oats | Barley
HH 0.58 0.41 0.26
HV 0.91 0.89 0.52
VV 0.26 -0.28 -0.46
HV/HH ratio -0.78 -0.73 -0.75
HV/VV ratio 0.84 0.71 0.80
HH/VV ratio 0.69 0.75 0.80
entropy 0.94 0.90 0.81
pedestal height 0.87 0.70 0.63
total power 0.62 0.28 0.08
volume scattering 0.86 0.89 0.39
Table 2. Correlation coefficients (R) betveen RADARSAT-2
response and optically derived LAI
The correlation between LAI for barley and SAR response was
noticeably weaker than that reported for wheat and oats.
Significant variation in optical reflectance from these barley
fields was observed and may be indicative of greater variability
in the growth of this Crop.
Spring wheat Oat
y-01783x +0.315 2
y=0.0903x +0.48
R 208923
y - 0.0848x « 0.4435.
03 WR 20.662
Derived LAI
Figure 1. Relationship between entropy and optically derived
LAI
There are advantages and disadvantages in selecting either
linear intensities or polarimetric variables for LAI estimation.
The modelling of LAI from the intensity of backscatter,
especially the intensity associated with the cross-polarization,
will require a well calibrated sensor. However, many satellite
sensors have imaging modes which provide HV backscatter data
over wide swaths, necessary for large area monitoring.
Provision of parameters from polarimetric decompositions
(such as entropy and volume scattering) is restricted to imaging
modes of limited swath. RADARSAT-2, for example, provides
a wide fine quad-polarimetric mode which acquires data over a
swath of only 50 km.
3.2 Water Cloud Model
The entropy parameter produced the strongest sensitivity to
optically derived LAI. Consequently this parameter was
selected for modelling the radar response, and for model
inversion. Barley, oats, and wheat were pooled together for the
purpose of fitting the WCM, and for model inversion. The
degree of model fit is indicated by the coefficient of
determination (R*). The fit of LAI and soil moisture to entropy,
produced a coefficient of determination of 0.7. The fitted model
is displayed in Figure 2.
