3.2. SWC semivariance characteristics of sampling plots
We used Variogram analysis and Kriging interception to characterize and
generate spatial structures of soil moisture for each plot area (Table
3). RSS for most plot areas was < 1 (with an average value of
0.23 for plots #2 and #7), indicating a clear spatial auto-correlation
relationship among data distribution in their effective ranges. Further,
the models which we used for semivariance analysis were appropriate and
the parameter combination for each fitting function model can be used
for explanation of the spatial pattern in the study plots. RSS in
plot #1 was 12.9 suggesting that the model used for plot #1 was not as
fitting as theoretically needed, and the interception map should be
carefully considered in the following analysis.
We used the Gaussian model for plot #3, spherical for plots #2, #5,
#6 and #10, and exponential for plots #4, #7 and #9, indicating
that the fitting model was robust and interpolation results were based
on a function that can represent the actual spatial pattern of SWC. The
optimal function was linear for plot #1 and #8 which indicated that
the model was not as ideal as the RSS showed, and that may be a
limitation due to the size of the dataset. A spatial correlation of
< 0.5 between SWC in plot #1 and #8 indicated that there was
no spatial correlation, closely with what was reflected by RSS (Yang et
al., 2017). Other sampling plots showed a strong spatial
auto-correlation, indicating that parameters based on the models can be
used for spatial interpolation of SWC at each sampling plot.