characteristics of input features are presented. Because the

results from

WBNN

and

BPNN

are not very different, this type of

information is apparently captured sufficiently well using the

more basic

BPNN

. Since the results for each scenario and the

AOI

are very similar between the

BPNN

and the

WBNN

algo-

rithms, detailed plots of the results are only presented for the

BPNN

in the following discussion.

Figure 8 shows the results after applying the algorithm

based on scenario 1 to the four areas of study. The plots show

the relationship between the testing data estimates from the

algorithm and the target data which is the

EC

reference data.

The correlation between the

EC

estimates and targets for the

test data is indicated by the correlation coefficient

. As

Figure 8 shows, the correlation between the estimation and

target in

AOI

1 is weaker than for the other areas. This is con-

sistent with Equations 3, 4, and 5 where if the height of grass

increases,

0

and volume scattering increase, and therefore

we get a weaker relationship between the soil moisture and

the backscatter coefficients. A better estimate of conductiv-

ity is obtained in

AOI

3 (see Figure 2c) which covers a mixed

bare and weed area. It seems this bare soil provides stronger

correlation between the

SAR

data and conductivity. This is ex-

pected due the fact that more vegetation will contribute a rela-

tively greater amount to the backscatter than the soil it covers,

thus resulting in less correlation to the actual soil moisture.

By comparing the results of

AOI

2 and

AOI

4, it can be inferred

that the dead grass (laid over short Bermuda grass) reduces

the estimation correlation, perhaps due to the increased atten-

uation and the presence of higher volume scattering. As can

be seen from Table 2,

AOI

4 has weaker correlation than

AOI

2

by 15 percent, which may be due to high volume scattering.

Since each area has a different conductivity range, we did not

compare the

RMSE

between them. We use the

RMSE

evaluation

for comparing the scenario results within each

AOI

.

Figure 9 shows the results of applying the algorithm to

the area of study based on scenario 2. As the figure shows,

inclusion of the mean feature for sliding window (3

3)

improves the correlation between the estimates and the target

for all

AOIs

. Since this feature is a texture feature (when used

in combination with the individual pixel backscatter values),

we expect to have improvement in the estimation based on

Equations 6, 7, and 8. This is because some knowledge of the

surface roughness gained from the texture feature can im-

prove the NN’s ability to estimate the dielectric constant (

)

and thus also

EC

. The amount of correlation improvement is

around 10 to 20 percent in all

AOIs

when this texture feature

is included. Therefore, we can conclude that using a mean of

HH

and

VV

around a pixel can improve the soil conductivity

estimation. Figure 9 also shows that

AOI

3 has better correla-

tion than other

AOIs

in this scenario.

As we compare

RMSE

and correlation results of scenario

2 to scenario 1 in Table 2, we can find correlation improve-

ment at all

AOIs

of 8 to 18 percent. Also, the

RMSE

is lower in

scenario 2.

RMSE

decreases more in

AOI

3 in which we have

some bare soil.

Figure 10 shows the result of conductivity estimation

based on scenario 3. By comparing this output to the results

of scenarios 1 and 2, we see more improvement for all

AOIs

.

Similar to scenarios 1 and 2, the best estimate of conductiv-

ity is obtained in

AOI

3 where a correlation of approximately

64 percent is obtained. By comparing the results of scenarios

2 and 3 in Table 2, we can see that scenario 3 improves the

correlation for most

AOIs

with a 1 to 17 percent improvement.

The results also show that scenario 3 has stronger correlation

Figure 8. Conductivity estimation for the AOIs in scenario 1 [using only SAR backscatter coefficients of HH and VV along with the local

incidence angle (

)]

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