PE&RS July 2016 Public - page 518

performance than scenario 1 with an 11 to 27 percent im-
Based on these results, it can be inferred that texture fea-
tures can increase the accuracy of conductivity estimation for
. According to Equation 6 roughness information and
slope of surface can affect the backscattering data. We
believe that the texture features used in scenario 3 provide
useful information related to surface roughness which im-
proves the estimate of dielectric constant and thus observed
We should note that because our method is applied to such
a different environment (constructed earthen levees, having
steep slopes) from other studies of soil moisture estimation
from radar, and furthermore uses a shorter wavelength (most
previous work used L or C band), we do not attempt here to
compare our results to them.
Finally it is worth mentioning that our algorithm is trained
based on the training samples from the areas shown in Figure
2. If a region on a levee covers different grass or canopy, we
need to have enough training samples from the new area and
train the machine learning algorithm for that new area.
In this work, a machine learning approach based on
is investigated for estimation soil electrical con-
ductivity on an earthen levee system using X-band
. The
algorithm was examined in four areas of study with different
vegetation covers, using three scenarios based on the extract-
ed feature sets used. TanDEM-X dual-polarization
is used along with
data obtained from lidar to compute
local incidence angles. We used reference data soil electrical
conductivity measurements for training and validation of the
algorithm. The results show that scenario 3, which includes
all backscatter and texture features, has better correlation
performance than the others with around 11 to 27 percent im-
provement compared to scenario 1 (backscatter features only)
and 1 to 17 percent improvement over scenario 2 (backscat-
ter plus window statistics). This result shows that estimat-
ing conductivity based on only backscatter features cannot
provide suitable estimation for this environment and we need
to include texture information. The results also show that the
performance is very similar to that of the
. Based
on these results, our approach shows some promise for use in
a levee health monitoring system based on X-band
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