3.3. Data combining method of spatially- and
temporally-extensive soil moisture
In this study, we found that the first EOF can predict the general
patterns of soil moisture, for example at the 20-cm soil depth with 85%
of the variance explained. Although the relative importance of the first
EOF on daily patterns of soil moisture waxes and wanes during cycles of
wetting and drying, the spatial pattern of the EOF is invariant in time.
Therefore, we considered it is an efficient way to integrate the
spatially extensive (but temporally limited) manual measurement sites
with other field long-term automatic monitoring datasets that are
temporally extensive (but spatially limited). Conceivably, based on the
EOFs for all spatially extensive sites, it was possible to predict the
spatially-distributed soil moisture for all those sites based on the
derived regressive equation between the EOFs of the temporally-extensive
sites and its automated monitoring datasets. For instance, the first EOF
of soil moisture at 20-cm soil depth was derived from a manual dataset
with the higher explained variations, but there also was a
strong-correlated regression coefficient between the soil moisture
automated monitoring sites (e.g., five sites 15, 51, 55, 61, and 74) and
their corresponding EOFs values. Based on the derived equations, all
manual measurement values could be predicted by either the manual
measurements or the monitored values at those five monitoring sites. To
validate this assumption, we selected three wetness conditions on the
same dates as used in Figure 3 (i.e. wet: ; moist: ; dry: ). Remarkably,
the predicted values via the manually-measured data (Fig. 6) have a
strong linear correlation to the measurements with high confidence
levels (95%). These results mean that the suggested method is practical
to combine the manually-measured datasets with the automated- monitored
datasets.
Note that the results showed a relatively large scatter when the
automated- monitored values were used to predicate soil moisture values
at the spatially extensive sites (Fig. 7). Whether this approach is
accurate is also dependent on how well the manually-measured data and
the automated-monitored data closely match for the same soil depths at
the same sites. Due to the differences in the measured thickness and
horizonation, spatial dimensions and scales for the two methods (Gu et
al., 2018), the values between manually-measured and automated-monitored
datasets may not necessarily match well with one another. As indicated
in Figure 8, except for site 55, there were large differences between
manually-measured and automated-monitored soil moisture values. For
instance, the manually-measured moisture contents are consistently
higher than the automated- monitored values for the site 51 during the
entire measurement period. Even worse, the trends between both datasets
for sites 15 and 74 are somewhat irregular. These results challenged the
suitability of this approach when the automated-monitored data, instead
of the manually-measured data were used at the temporally-limited site.
As shown in Figure 9, we found that the fit between
manual-measured and auto-recorded
soil moisture datasets were significant, but relatively weak. Therefore,
to apply this method reasonably, it is important for the predicted data
accuracy accounting the manually-measured and automated monitored data
to be somewhat in agreement. It is expected that the EOF method could be
a practical and efficient data merging method if the primary EOF
explains >60% of the variation. Nevertheless, taking into
account those differences, the EOF method as applied in this study could
be quite valuable, and therefore provide an essential way to assimilate
data from multiple sources.
Furthermore, we explored the EOF method to breakdown a more dynamic time
series of soil moisture in to a lesser number of orthogonal spatial EOF
patterns (that are invariant in time) and the corresponding EC
components (that are invariant in space). This modification greatly
simplifies our task as we can just deal with only a few spatial EOF
structures instead of the whole data set. The higher-order EOFs are
usually taken into account depending on the amount of the total variance
explained by them. The associated EC components show the variation in
the influence of the EOFs during the wetting/drying phases, which could
be reasonably associated with the automated monitoring moisture dynamics
and theoretically provided the basic for the data fusion. To determine
the dominant physical controls, the EOF patterns were correlated to the
geophysical characteristics of the region. From our analyses, we
inferred that some of the variability of the soil moisture EOF patterns
is related to both topography and soil texture. We assessed that, using
the EOF analysis, it is particularly applicable to combine the manual
datasets with the automatic datasets in terms of different resolutions
for different data sources. The soil moisture dataset is currently
providing either better spatial coverage or better temporal coverage.
Our data assimilation approach provides an important way to combine both
datasets together which certainly improved the explanations for the
variation and data use.