3.4 Implications
Existing methods for direct field measurement of soil hydraulic
properties remain complex, time-consuming, costly, and significant
spatial and temporal variability challenges the possibility of extensive
measurements (Ma et al., 2018). However, the spatial patten, as
indicated by the EOF analysis, is the relatively stable. This stability
attribute of the spatial pattern has implied that it is possible to
continuously assess the soil moisture distribution in a catchment. In
addition to the spatial coverage maps, adding in the long-term
monitoring of both surface and subsurface soil moisture provides a
comprehensive picture of the spatial-temporal pattern of soil moisture
dynamics across the whole area and allow the identification of factors
which influence it through time. A unique long-term real time soil
moisture data set was previously used to identify local dominant
hydrological processes and its time dynamics. In this perspective, our
approach becomes more effective given that the long-term monitored site
is characterized as a time-stable location via a time-stability analysis
(Zhao et al., 2010). Our approach also has the capability to assimilate
additional data sources, e.g., remote sensed data at this time-stable
site. Given the high accuracy of the soil moisture monitoring, the
time-resolution soil moisture patterns over an area could be obtained by
selecting a temporally stable monitoring site, which is useful in ground
truthing of a remotely sensed footprint for validation of simulation
modelling results (Zhao et al., 2010). Given the importance of soil
moisture in Earth’s land surface interactions and to a large range of
applications, one can appreciate that its accurate estimation is
critical in addressing key practical challenges such as food security,
sustainable planning and management of water resources. The launch of
new, more sophisticated satellites strengthens the development of
innovative research approaches and scientific inventions that will
result in ground-breaking advancements.
One goal of this study was to lay the foundation for the design of
cost-effective real-time soil moisture monitoring networks that fill in
the gap between point sensors and traditional manual measurements or
even remote sensing values. Our study is representative of a novel
approach with the potential benefits for an effective soil moisture
monitoring network design within the study area, determining the
spatiotemporal statistics of the observed soil moisture fields, and the
use of a spatial regression procedure in data merging. It is more
realistic to observe a difference between developed maps as surface
conditions evolve. We believe that this combination reflects more
adequately the basin heterogeneity and complex interactions between soil
moisture and topographic attributes. Although there are the simple
linear data transfer methods that have been applicable to this type of,
our approach may accommodate different data analysis methods, such as a
multi-step regression method based on the EOF analysis (Temimi et al.,
2010). Once the tasks within our approach have been completed, the
EOF-based transfer method may be used as a foundation any region and/or
date under the assumptions that the identified empirical relationships
will be valid for the application conditions.
The present soil-landscape has been shaped through a combination of
long- and short-time processes, and this history can provide some clues
to project future changes. However, linking long-term and slow processes
with shorter-term and fast processes remains one challenge (Ma et al.,
2018). While mapping depicts the spatial distribution of soil-landscape
relationships, as indicated by the dominant EOF patterns; monitoring
captures the temporal dynamics of pedologic and hydrologic properties,
as indicated by the profiled data dynamics (Ma et al., 2018). Given the
spatial-extensive data benefits of traditional mapping, and the
temporal-extensive data benefits of traditional monitoring, the
presented data-integrated method may provide a justifiable basis for the
combination of mapped and monitored data, as well as a conceptual basis
for the coupling of slow and fast processes. Firstly, bridging mapping
with monitoring is very helpful in the dynamic mapping of hydropedologic
functional units (Ma et al., 2018). Secondly, mapping provides
information to aid in optimal site selection for monitoring (Zhao et
al., 2010). Thirdly, mapping and monitoring supplies essential data for
the calibration and validation of modeling, and may help provide
additional information for a more holistic, refined and predictive
management of soil and water resources (Guo et al., 2019). Our approach
provides an essential set of tools to evaluate the improvement of data
use. We assumed that the relationship between different data sources
remains the same over time, but suggest that future studies verify this
behavior.