1. Introduction
Soil moisture is a key variable within earth system dynamics from
regional to pedon scales (Famiglietti et al., 1998; Korres et al.,
2015). Identification and prediction of soil moisture patterns at
different scales are important in a wide range of agronomic,
hydrological, pedological, and environmental studies (e.g., Grayson et
al., 2002; Lin et al., 2006). However, obtaining accurate information on
soil moisture at an appropriate temporal and spatial scales is still
challenging (e.g. Crow et al., 2012; Vereecken et al., 2008). This
shortage of information has impeded the modeling, predication, and
management of water resources (Owe et al., 1982; Grayson et al., 1997;
Zhu and Lin, 2010; Shi et al., 2015). Linking soil moisture mapping with
monitoring can provides a more integrated approach to understanding
soils and water resources (Dunn and Lilly, 2001; Joshi and Mohanty,
2010; Ma et al., 2017), and provides a way of combatting the general
decline of field hydrology relative to modelling (Beven et al., 2020).
Previous studies have indicated that the spatial pattern of soil
moisture is highly dependent on various controlling factors such as
parent material, soil, landuse/vegetation, topography and climate
(Grayson et al., 1997; Famiglietti et al., 1998; Western et al., 1999;
Baldwin et al., 2017). However, separating the relative importance of
individual factors is usually extremely difficult as some of the
influencing factors are interdependent due to the scale-dependence,
dry-wet cycles, and interface exchange from top to deep soil layers
(Zeleke and Si, 2006; Famiglietti et al., 1998; Western et al., 1999).
During wet conditions, subsurface lateral flow may be important, whereas
during dry conditions, the surface vertical flow at the onset of
precipitation has more influence (Grayson et al., 1997; Gómez-Plaza et
al., 2001). The correlation length of soil moisture ranges from near
zero to a thousand meters. It has been found to be highly dependent on
the scale of the study and on the density of the measurements (Western
and Blöschl, 1999), with a clear direct proportionality of increasing
correlation lengths with increasing scales having been observed. For the
on-site soil moisture monitoring system or networks, the correlation
measurements typically include the several sites, whereas for the
measurements characterizing soil moisture variation within the field,
the area can cover several ha areas, or even much large areas for
satellite-based investigations. It is evident that the potential exists
for more accurate estimations of soil moisture by using synergistic
approaches among a variety of earth observation methodologies.
The
procedures for associating the relatively stable spatial patterns of
soil moisture and specific soil hydrologic processes to precipitation
events are sorely lacking. The temporal variations associated with field
measurements of soil moisture have been displayed within numerous
studies that have shown the static, terrain-derived indices rarely
explained more than 50% of the soil moisture variability (Famiglietti
et al. 1998; Western et al. 1999; Baldwin et al., 2017). Although
climate seasonality and localized vs. non-local hydrological fluxes
exert a strong influence on surface or near-surface soil moisture
distribution (Grayson et al., 1997; Western et al., 1999; D’Odorico and
Porporato, 2010), less is understood regarding how the subsurface soil
moisture spatial patterns change with climate seasonality and seasonal
changes in hydrological fluxes. By integrating spatially-distributed
soil physical properties, topographical variables and meteorological
data, Schwärzel et al. (2009) developed a novel approach in model-based
mapping of soil moisture within forested sites. This approach provided a
more objective description of soil moisture variability than the
traditional mapping by an integrated ecological approach. Other research
using a modification of this approach showed that both the spatial
distribution and temporal evolution of soil moisture may be investigated
at multiple scales (e.g., Deiana et al., 2008). Another potential
improvement was the integration of geophysical scanning with real-time
soil moisture monitoring and dye staining (Guo et al., 2019). Real-time
soil moisture monitoring may provide information concerning actual
hydrologic dynamics and the timing of preferential flow occurrence,
while dye staining patterns may validate the spatial distribution of
preferential flow pathways as revealed by geophysical scans. The
synthesis of these methods may enhance the data accuracy and associated
processes, thus increasing our understanding of hydrological processes
in various soils and landscapes (Ma et al., 2019).
Spatial dependency is commonly characterized and quantified by
geostatistical methods, such as autocorrelation and variogram analysis
(Zhao et al., 2011). An additional way to analyze the spatial patterns
of soil moisture and their connection to regional characteristics is
through an empirical orthogonal function (EOF) analysis (Jawson and
Niemann, 2007; Zhao et al., 2012). The EOF analysis decomposes a dataset
into a series of orthogonal spatial patterns. These patterns may be
correlated with regional characteristics to identify whether the
characteristics have an influence on the most important tendencies of
the soil moisture. Utilizing correlation analyses, these underlying
(stable) patterns of soil moisture variations can be connected to
parameters derived from topography, soil, vegetation, land management
and meteorology. Yoo and Kim (2004) investigated the spatial and
temporal variability of field-scale soil moisture and concluded that
there is no simple and unique mechanism that can be applied to explain
the evolution of the soil moisture field. Wagenet (1998) summarized the
main factors that influence soil moisture from the pore scale to the
global scale, and recognized soil and topography as important local
controls of soil moisture variations. During dry periods, soil moisture
distribution may be conceptualized as being controlled predominantly by
soil properties, whereas during wet periods the topography is the
controlling factor within a landscape. However, the pedon/plot scale
results have not been easily transferable to hillslope and catchment
scales, and both theoretical and empirical approaches have been used to
quantify hydrological dynamics based on such ‘point-scale’ data that
tends to over- or underestimate parameters and fluxes (Sidle et al.,
2017). While the temporal frequency for the manual measurement of soil
moisture has been found to normally be about weekly-or monthly-based,
the automated monitoring data may be hourly-based or daily-based (Korres
et al., 2015).
In this study, part of our soil moisture dataset contains manually
measured data that offers better spatial coverage with many sites, but
with limited temporal frequency as weekly measurements were made.
Another part of our soil moisture dataset contains automatically
monitored data that offers better temporal frequency with repetitive
ten-min to one-hour measurements, but limited spatial coverage with a
limited number of selected sites. It is expected that the product of
this combination captures the temporal variation of soil moisture from
the automatically monitored data and improved spatial resolution using
the information from manual measurements. The objectives of this study
were to: (1) derive the dominant soil moisture spatial-temporal patterns
based on multi-year datasets; (2) determine how these patterns are
controlled by terrain, soil, and vegetation as a function of scale,
wetness, and depth; and
(3)
provide a possible way to integrate spatial-extensive datasets with
temporal-extensive datasets.