2.4 Data combination
One of the primary benefits of the EOF analysis is a small number of orthogonal spatial patterns was identified that together explain a large proportion of the total variability of the soil moisture data. We now examined how closely these underlying patterns resembled regional characteristics that might dominate the spatial variability of soil moisture. For this analysis, we used the correlation coefficient between the EOFs and the available regional characteristics. Statistical analyses were conducted using SPSS for Windows (SPSS Inc., Chicago). The Pearson’s correlation was used to investigate the correlation between soil moisture and soil-terrain attributes. Linear regression was used to predict soil moisture (as a dependent variable) based on using either soil properties and/or terrain attributes (as independent variables).
Furthermore, given that the first EOFs exhibited the general patterns of soil moisture across the whole investigated spatial coverage, if the EOFs of the manual soil moisture measurements at some sites had the strong correlations with its automatically recorded values, then it was possible to apply this relationship to the first EOFs to derive the soil moisture across the entire area at any automated monitoring time. Consequently, we considered that this method may have provided an appropriate way to integrate the spatially-extensive (but temporally-limited) manual datasets and the temporally-extensive (but spatially-limited) automated monitoring datasets. The following narrative describes in detail the four steps involved with the data merging methods.
First, an EOF decomposition was performed on the soil moisture dataset to identify the patterns of covariation (the EOFs) and their importance on each date (the ECs).
Second, statistical tests were used to determine the EOFs between whole spatially-extensive data and automated monitored data that were statistically significant and should be retained in the transferring method.
Third, a multiple linear regression was performed to identify empirical relationships between automated monitored soil moisture data and its EOFs.
Fourth, the identified empirical relationships were employed to calculate soil moisture at each manual measured site within the entire catchment via its EOFs.