HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-4403-2017Multi-decadal analysis of root-zone soil moisture applying the exponential
filter across CONUSTobinKenneth J.ktobin@tamiu.eduTorresRobertoCrowWade T.https://orcid.org/0000-0002-8217-261XBennettMarvin E.Texas A&M International University, Center for Earth and
Environmental Studies, Laredo, TX, USAUnited States Department of Agriculture, Agricultural Research
Service Hydrology and Remote Sensing Laboratory, Beltsville, MD,
USAKenneth J. Tobin (ktobin@tamiu.edu)7September2017219440344171March201711April20176July20177July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/4403/2017/hess-21-4403-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/4403/2017/hess-21-4403-2017.pdf
This study applied the exponential filter to produce an estimate of root-zone
soil moisture (RZSM). Four types of microwave-based, surface satellite soil
moisture were used. The core remotely sensed data for this study came from
NASA's long-lasting AMSR-E mission. Additionally, three other products were
obtained from the European Space Agency Climate Change Initiative (CCI).
These datasets were blended based on all available satellite observations
(CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25∘ and taken daily. We applied the filter to produce a soil moisture index
(SWI) that others have successfully used to estimate RZSM. The only unknown
in this approach was the characteristic time of soil moisture variation (T).
We examined five different eras (1997–2002; 2002–2005; 2005–2008; 2008–2011;
2011–2014) that represented periods with different satellite data sensors.
SWI values were compared with in situ soil moisture data from the
International Soil Moisture Network at a depth ranging from 20 to 25 cm.
Selected networks included the US Department of Energy Atmospheric
Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network
(SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US
Climate Reference Network (USCRN; 20 cm). We selected in situ stations that
had reasonable completeness. These datasets were used to filter out periods
with freezing temperatures and rainfall using data from the Parameter
elevation Regression on Independent Slopes Model (PRISM). Additionally, we
only examined sites where surface and root-zone soil moisture had a
reasonably high lagged r value (r>0.5).
The unknown T value was constrained based on two approaches: optimization of
root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both
approaches yielded comparable results; although, as to be expected, the
optimization approach generally outperformed NDVI-based estimates. The best
results were noted at stations that had an absolute bias within 10 %. SWI
estimates were more impacted by the in situ network than the surface
satellite product used to drive the exponential filter. The average
Nash–Sutcliffe coefficients (NSs) for ARM ranged from -0.1 to 0.3 and were
similar to the results obtained from the USCRN network (0.2–0.3). NS
values from the SCAN and SNOTEL networks were slightly higher (0.1–0.5).
These results indicated that this approach had some skill in providing an
estimate of RZSM. In terms of RMSE (in volumetric
soil moisture), ARM values actually outperformed those from other networks
(0.02–0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06
and SNOTEL average RMSE values were higher (0.05–0.07). These
values were close to 0.04, which is the baseline value for accuracy
designated for many satellite soil moisture missions.
Introduction
Soil moisture is one of the most difficult hydrologic variables to either
monitor or model (Lettenmaier et al., 2015).
Understanding soil moisture
dynamics is critical to support many diverse applications in hydrology,
meteorology, and agriculture. In the agricultural sector, a fundamental
limiting factor that constrains crop productivity is root-zone soil moisture
(RZSM). Understanding root-zone moisture dynamics is important also from a
water resource standpoint and is a valuable measure in drought monitoring
(Bolten et al., 2010; Bolten and Crow, 2012). The dimensions of RZSM also
impact other systems beyond the hydrologic cycle, most notably with the
quantification of carbon fluxes within soils. Therefore, direct sensing of
RZSM dynamics will bring us closer to a truer understanding of the carbon
soil pool, with obvious implications for future climate change.
Given the importance of RZSM to agricultural and other applications, more
effort is needed to understand the impacts of climate change associated with
this critical variable. The National Aeronautics and Space Administration
(NASA), European Space Agency (ESA), and other governments across the world
have had a long history of supporting missions that generate remotely sensed
surface soil moisture, including the Scanning Multichannel Microwave
Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), Tropical
Rainfall Measurement Mission (TRMM), Advanced Microwave Scanning
Radiometer-Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity
(SMOS), Soil Moisture Active Passive (SMAP), scatterometers on the European
remote sensing satellites, which includes scatterometer (SCAT)
and the advanced
scatterometer (ASCAT) to name only a few (e.g., Lakshmi et al., 1997; Wagner et
al., 1999; Kerr et al., 2001; Jackson et al., 2002; Hutchinson, 2003;
Njoku et al., 2003; McCabe et al., 2005; Owe et al., 2008; Entekhabi et al., 2010).
Passive microwave soil moisture estimates, like AMSR-E-measured
horizontal and vertical polarization temperatures in several wavelengths,
which include 6.6/6.9 GHz (C band), 10.7 GHz (X band), and 19.3 GHz
(Ku band). In addition, the vertical polarization is examined at 36.5/37.0 GHz (Ka band).
An advantage of the more recent SMOS and SMAP missions is
that they operate at a lower frequency 1.2/1.4 GHz (L band), which has great
penetrative power, especially in highly vegetated areas. In terms of the
active sensors, both SCAT and ASCAT operated at 5.3 GHz (C band) and have a
similar design philosophy. These sensors make sequential observations of the
backscattering coefficient with six sideways-looking antennas and make
sequential observations of the backscattering coefficient using three
polarizing antennas.
Liu et al. (2012) described the development of two extensively validated
surface soil moisture products. These products were created using a
harmonized dataset based on all available soil moisture retrievals: one from
the Vienna University of Technology (TU Wien) based on active microwave
observations (Wagner et al., 2003; Bartalis et al., 2007) and one from the Vrije
Universiteit Amsterdam (VUA), in collaboration with the NASA Goddard Space
Flight Center Hydrological Sciences Laboratory, based on passive microwave
observations (Owe et al., 2008). This effort was a part of the ESA Climate Change
Initiative (CCI). The harmonization of these datasets incorporated the
advantages of both microwave techniques and spanned the entire period from
1978 onward. This effort is unlike NOAA's Soil Moisture Operational Products
System (SMOPS), which was a long-term record of soil moisture based on only
passive microwave data.
A long-standing goal of the soil remote sensing community is to develop
techniques that can observe changes in RZSM at depths greater than 10 cm,
because all of the missions described above are confined to sensing moisture
only within the top 5 cm of the profile. In 2015, NASA launched the SMAP
mission that had the potential to combine the advantages of passive and
active microwave retrievals to estimate soil moisture dynamics at depth.
Unfortunately, early on in this mission, the satellite's radar failed.
Despite this setback, NASA had invested considerable resources into the
development of an ensemble Kalman filter (EnKF)-based level 4 RZSM product
for SMAP (Reichle et al., 2016) and the development of lower-frequency
airborne radar systems for deeper penetration of the soil column (via the
EV-1 AirMOSS project). While this work is to be commended, the limited time
availability of these products precludes their use for long-term climatic
trend studies.
This study used the exponential filter to leverage the longer-duration CCI
surface soil moisture record to produce a record of RZSM that can be
compared over almost two decades (1997–2014). Wagner et al. (1999) developed
the exponential filter to examine soil moisture trends from European remote sensing (ERS)
scatterometer data focusing on Ukraine. A later refinement of this
filter included the development of a recursive version that had the virtue
of a greater ease of implementation (Albergel et al., 2008). In recent years,
several authors have produced RZSM estimates using the exponential filter
and have conducted comparisons at a range of spatial scales (Ford et al., 2014;
Manfreda et al., 2014; Qiu et al., 2014; Peterson et al., 2016; Kedzior and
Zawadzki, 2016). At the heart of the exponential filter method is the
assumption of hydrologic equilibrium within the soil profile that makes it
possible to estimate RZSM by using only surface measurements, provided that
soil physical properties are known. This method also assumes that there is
no loss from the root zone due to transpiration. Transfer of soil moisture
from the surface to the root zone is controlled by a pseudo-diffusivity term
that allows both positive and negative fluxes from and to the deep layer.
This approach overcame a limitation of the EnKF approach in that data
assimilation is not dependent on obtaining data from a land surface model,
in which there can be significant uncertainty in terms of the model
parameters used to constrain water and energy balances (Kumar et al., 2009).
This study presents the results of the application of the exponential filter
produced using four satellite soil moisture products from 1997 to 2014 focusing
on the continental United States (CONUS). As such, this work represents a unique
application of the exponential filter over a multi-decadal timescale, which
is only afforded by the long-duration CCI record.
Observation eras from 1997 to 2014.
EraDescriptionTime range1Pre-AMSR-E27 November 1997–18 June 20022Early AMSR-E19 June 2002–30 June 20053Middle AMSR-E1 July 2005–30 June 20084Late AMSR-E1 July 2008–3 October 20115Post-AMSR-E4 October 2011–31 December 2014DataEra definitions
The data examined in this study span over 17 years. As such, we compared
soil moisture produced by the exponential filter over five roughly equal
eras (3–4.5 years), which were defined based on the available satellite
retrievals during each era (see Liu et al., 2012). These eras included
27 November 1997–18 June 2002 (pre-AMSR-E), 19 June 2002–30 June 2005 (early
AMSR-E), 1 July 2005–30 June 2008 (middle AMSR-E), 1 July 2008–3 October 2011
(late AMSR-E), and 4 October 2011–31 December 2014 (post-AMSR-E; Table 1).
The pre-AMSR-E era relied heavily on the TRMM microwave imager (TMI)
passive observations and SCAT active retrievals that operated until 2006. In
fact, the climatology of the passive dataset during this period was rescaled
based on TMI data and likewise the same was true of AMSR-E during eras 2–4.
During the early AMSR-E era, passive observations from the WindSat satellite
became available online (Gaiser, 2004).
The middle AMSR-E era was a time of transition
in terms of active observations as the SCAT satellite was replaced by ASCAT.
The late AMSR-E era saw the arrival of the ESA SMOS mission. After the
failure of AMSR-E, SMOS observations took on a more prominent role within
the CCI passive microwave framework. Also during the post-AMSR-E era, the
Japanese Space Agency launched AMSR2 (Wentz et al., 2014), which is
considered the replacement for the long-lasting AMSR-E mission.
In situ soil moisture
Direct in situ comparisons were made between RZSM estimates with in situ data from the
International Soil Moisture Network (ISMN; Dorigo et al., 2011). The ISMN
provides access to a host of meteorological and soil moisture data (at many
depths). In this study, we selected soil moisture at two depths. Surface
soil (0–10 cm) and RZSM (20–25 cm) moisture were compared to assess the
performance of the exponential filter method. In this study, we focused on
four networks within CONUS that have been examined in previous studies. Al
Bitar et al. (2012) conducted an extensive evaluation of SMOS data using two
networks; we utilized the Soil Climate Analysis Network (SCAN; 20.32 cm) and
SNOwpack TELemetry (SNOTEL; 20.32 cm). Additionally, we obtained soil
moisture observations from two other CONUS networks: the US Department of
Energy Atmospheric Radiation Measurement (ARM; 25 cm) program (Jackson et
al., 1999) and the US Climate Reference Network (USCRN; 20 cm; Bell et al.,
2013). Complete ARM observations only existed from eras 1 to 4, and USCRN
data were available for only era 5 (Table 1). In situ values were aggregated to a
daily time step (based on UTC time) that matched the surface satellite-based
soil moisture product described below. Figures 1 and 2 show the location of
the stations selected across the five eras.
The ARM network used the Campbell Scientific 229-L heat dissipation matric
potential sensor to estimate soil moisture (Reece, 1996). Calibration of this
method was based on comparison of matric potential with soil water release
curves (Klute, 1986). Conversely, the SCAN, SNOTEL, and USCRN networks all
used a Stevens Water Hydra Probe (Schaefer et al., 2007; Bell et al., 2013).
Seyfried et al. (2005) described the calibration approach and how the
dielectric measurements from the Hydra Probe sensor were converted into
volumetric soil moisture measurements.
Locality map of examined in situ stations (ARM: X; SCAN: ∗; SNOTEL: +)
with (a) era 1, (b) era 2, and (c) era 3. The gray area represents
the central CONUS, whereas white indicates the eastern and western regions of
CONUS.
Locality map of examined in situ stations (ARM: X; SCAN: ∗; SNOTEL: +)
with (a) era 4 and (b) era 5. During era 5, X represents USCRN
instead of ARM stations. The gray area represents the central CONUS, whereas
white indicates the eastern and western regions of CONUS.
Surface satellite-based soil moisture
This study was supported by four surface (5 cm) soil moisture products,
three of which came from the CCI program. We used the CCI-passive,
CCI-active, and CCI-combined products (version 2.2). The harmonization process involved in the creation of
these products was described by Liu et al. (2012) and these datasets are
available online (http://www.esa-soilmoisture-cci.org/node/145). In
addition, we also utilized stand-alone data from the AMSR-E mission during
eras 2–4. In this study, we acquired the version produced by the Land
Surface Parameter Model (LPRM; Owe et al., 2008;
ftp://hydrol.sci.gsfc.nasa.gov/data/s4pa/WAOB). All of these satellite
soil moisture products were produced at a daily time step with a
0.25∘ spatial resolution.
Other datasets
Several other datasets were used in an ancillary role. Air temperature and
precipitation data were obtained from the Parameter elevation Regression on
Independent Slopes Model (PRISM; Daly et al., 1994) from grid cells (4 km
spatial resolution) co-located with examined in situ sites (PRISM Climate Group
2015). These data were used to screen dates below freezing and with
significant precipitation data, as suggested by Dorigo et al. (2011), to
enhance quality control.
In addition, normalized difference vegetation index (NDVI) values (Tucker,
1979)
were used to help constrain the only unknown in the exponential
filter (the characteristic time length) and were derived from Moderate
Resolution Imagining Spectroradiometer (MODIS) data. The version of MODIS
(MOD13Q1) used near-infrared reflectances that were atmospherically
corrected to mask water, clouds, aerosols, and cloud shadows. Datasets were
provided in a sinusoidal grid with a 250 m resolution, and an average of nine
pixels around each in situ station were used to calculate a global average NDVI for
each era.
MethodsInitial station filtering
To ensure selection of the highest-quality in situ stations, we applied two
criteria in our initial station selection. The first criterion involved the
amount of missing data within a candidate station. Sites that had an
excessive number of missing data, a total of over 20 days per year, were
rejected. A second criterion related to a fundamental assumption of the
exponential filter method, which is that there is a hydrologic connection
between the surface and root-zone horizons. One would expect that deeper
within the profile there would be a greater lag in response. Therefore, a
lagged r value between surface measurements (generally made at 5 cm) and
root-zone data from 20 to 25 cm depth was made. Root-zone lag was calculated
between 1 and 40 days, and the day with the highest lagged r value was
selected. Stations whose maximum lagged r value fell below 0.5 were
rejected. Qiu et al. (2014) used a similar selection criterion in their
study.
Exponential filter
Wagner et al. (1999) originally developed the exponential filter and
Albergel et al. (2008) refined this approach with a more robust recursive
version of this method. This version provided an estimate of a soil wetness
index (SWI) within the root zone. This index standardized RZSM based on the
total range of values recorded by the in situ dataset. The recursive formulation
provided a predictor of RZSM at time (tn), which in this study was
given in days and was derived as
SWImn=SWImn(n-1)+Kn[ms(tn)-SWImn(n-1)],
where SWImn(n-1) represented the estimated RZSM at time tn-1,
ms(tn) was the surface soil moisture estimate based on either CCI
products or AMSR-E retrievals, and Kn was the gain at time tn
determined with
Kn=Kn-1Kn-1+etn-tn-1T,
where T represented the timescale of soil moisture variation in days. At
the beginning of each era and after excessively large gaps in ms(tn)
data (> 12 days), the filter was initialized with SWIm(1)=ms(tn) and Kn1 set to 1. Results from a data denial
experiment described below provided support for the selection of 12 days as
an appropriate timescale to reset the filter. The prime advantage of the
exponential filter was that the only unknown was T. Finally, the SWImn
generated from the exponential filter, which ranged from 0 to 1000, was
rescaled to match the range of the in situ data (in volumetric units)
allowing for comparisons between these datasets.
Objective metrics
Direct comparisons were made between CONUS in situ stations that represented
a long time series. While it is true that soil moisture measurements exhibit
a high degree of spatial variability over a wide range of spatial scales from
field plot to watershed (e.g., Western et al., 2004; Wilson
et al., 2004; Brocca et al., 2007), temporal variation is
much more muted. Temporal stability is a concept fully rooted in soil science
(Vachaud et al., 1985; Martinez-Fernandez and Ceballos, 2003). Therefore, the
approach of this study was to use standard objective metrics such as lagged
r values to describe the relationship between (coarse-scale) root-zone
soil moisture estimates based on the exponential filter and (point-scale) in
situ measurements. Other temporal statistics included bias, Nash–Sutcliffe
coefficients (NSs), and root mean square error (RMSE, in volumetric soil
moisture). In terms of bias, results are also evaluated based on whether the
absolute bias is low (within 10 %) or high (greater than 10 %), which
strongly impacts the other objective metrics. Each of these metrics has their
own utility as discussed in the paper below.
Calibration of Topt
Albergel et al. (2008) noted no significant correlation between soil
properties and the optimal timescale of soil moisture variation (Topt).
Therefore, they constrained this parameter by optimizing T based on the NS
metric, an approach also applied by Ford et al. (2014). However, Albergel et
al. (2008) also noted a weak relationship between T with climate.
Specifically, a linkage between increased temperatures and hence soil
evaporation (not transpiration). A lower Topt was representative of a
faster response of SWI present in areas with a higher evaporational demand.
This conjecture was consistent with a relationship developed by Qiu et al. (2014)
using mean NDVI values at in situ sites.
In this study, we used two approaches to determine Topt. The first
method optimized Topt at a time in which the RMSE is minimized. This
was essentially the same approach as finding a maximum NS value. RMSE was
calculated between 1 and 68 days at a 1-day increment. Sites that converged
on the upper 68-day bound were rejected. Qiu et al. (2014) used a similar
upper bound as a means of selecting SCAN sites for their study.
The second approach used the NDVI formulation from Qiu et al. (2014) to
calculate Topt. This relationship is given as
Topt=[-75.263×NDVI]+68.171.
In situ station filtering and data denial experiment
To ensure that the exponential filter was effective in producing a RZSM
estimate, the ms(tn) term was set based on surface (5 cm) in situ data
instead of satellite data. Normally, grid-based satellite surface moisture
estimates are used to drive the exponential filter. However, to establish a
filter based on the quality of in situ data, an initial estimate of RZSM is
determined based on surface in situ data at the 5 cm level. Initial RZSM estimates
with a NS value less than 0.50, which is a common threshold for defining a
satisfactory match between in situ and simulated hydrologic data (Moriasi et al., 2007),
were rejected. This filter removed many of the poor-performing outliers
(NS <-1.00) from consideration. Table 2 describes the issues with the
remaining poor-performing outliers that lingered after this in situ based filtering
approach.
Use of surface (5 cm) in situ data also supported a data denial experiment that
gauged how the filter's performance was impacted by gaps in the ms(tn)
time series. This experiment focused on the SCAN network during era 3
(2005–2008; Table 1). Time series were altered to include only data at 2-, 5-,
8-, and 11-day intervals. This experiment was based on the 32 out of 42 sites
that had in situ based NS in excess of 0.50, i.e., the sites that survived this
filtering process. Both surface (5 cm) in situ and satellite (AMSR-E) data were used in
this experiment.
Number of poor-performing (NS < 1.00) outliers for all four
satellite products.
ARMSCANSNOTELUSCRNRMSE optimization In situ data173151Insufficient SWI01140Lack of range01103Timing issues0090NDVI approach In situ data2216325Insufficient SWI03440Lack of range017158Timing issues0653
Average lagged r values and Topt between SWI based and
in situ soil moisture at the 25 cm depth for the ARM network. Standard
derivation is indicated in parentheses. The n value represents the number
of observations.
Before calculation of SWI values for all four satellite products at each in situ
station, a series of filters were applied to remove any spurious results
following the quality control guidelines articulated by Dorigo et al. (2013).
Surface temperature and precipitation data from co-located PRISM
grid cells flagged problematic dates within the time series of each dataset.
Satellite retrieval from days in which the minimum air temperature was less
than 0 ∘C were removed from the SWI dataset. Satellite soil moisture
retrievals were particularly fraught with difficulty under freezing
conditions (Dorigo et al., 2011). Likewise, precipitation can be problematic
and days with greater than 1 mm day-1 were excised following the guidance of
Dorigo et al. (2013). Three additional flags related to the quality of the
in situ data were applied. Days with values in excess of the porosity reported by
the ISMN were expunged from the rescaled SWI dataset. Likewise, days that
recorded the same value (plateaus) or zero were deemed spurious and removed.
The final filtered rescaled SWI dataset consisted of less than 100 days; this
dataset was rejected following the guidance of Dorigo et al. (2013).
Finally, SWI based estimates in which NS <-1.00 were rejected as
outliers. A detailed discussion of these outliers is given below.
Results
Figure 3 shows the results of the data denial experiment in which both in situ and
satellite data (AMSR-E) were used at the surface. Note a baseline performance
for in situ dataset has average NS values close to 0.7, which was almost
identical to the results based on in situ surface soil moisture datasets in which every
other day was withheld. Even in datasets with every four out of five dates
withheld there was only a slight drop in performance. This result
underscored the ability of the exponential filter to effectively cope with
datasets that have significant gaps. Average NS values fell to 0.5 only when
over 90 % of the surface soil moisture dataset was withheld and
measurements from only every 11th day were used. The data denial experiment
using AMSR-E data to drive the filter yielded a similar drop-off in
performance as the number of withheld days increased.
Box plot of the data denial experiment from the SCAN network during
era 3 (2005–2008). Results for day 1 represent baseline data for the
exponential filter driven by surface soil moisture data (in situ data: ★; low
absolute bias RMSE-optimized AMSR-E: •). Other time series were
altered to include only data at 2-, 5-, 8-, and 11-day intervals.
Average lagged r values and Topt between SWI based on
optimization and in situ soil moisture at the 20.32 cm depth for the SCAN
network (Figs. 1 and 2). Standard derivation is indicated in parentheses. The
n value represents the number of observations.
Average lagged r values and Topt between SWI based on
optimization and in situ soil moisture at the 20.32 cm depth for the SNOTEL
network. Standard derivation is indicated in parentheses. The n value
represents the number of observations.
Average lagged r values Topt between SWI based on
optimization and in situ soil moisture at the 20 cm depth for the USCRN network
during era 5. Standard derivation is indicated in parentheses. Sites are
divided by region (east, central, west) as indicated in Fig. 2. The n
value represents the number of observations.
Figures 1 and 2 show lagged r values between in situ surface (5 cm) and
RZSM (20–30 cm) during the five eras. ARM sites clustered in Oklahoma and
Kansas had higher lagged r values during era 1 (network average r= 0.864) and a drop in this metric during eras 2 to 4 (network average r= 0.793–0.796). SCAN sites exhibited correlation coefficients that varied
spatially. In general, better performances were noted from eastern (network average r= 0.751–0.872) and central sites (network average r= 0.812–0.874). Western sites had slightly lower r values (network average r= 0.699–0.770). Notable outliers were present for the stations
in Montana during eras 4 and 5 (Fig. 2) that could account partly for the
poorer performance noted during these eras. SNOTEL stations were concentrated
in the western CONUS and had consistently high correlation coefficients (network average r= 0.828–0.865). Finally, the USCRN sites examined during era 5
(Table 1) generally had better r values in eastern and central CONUS
(network average r= 0.846–0.882) as opposed to the west (network average
r= 0.768).
The remainder of this section focuses on the results from the exponential
filter driven by the four satellite products. The Topt and lagged
r values discussed are based on results that have a low absolute bias
(±10 %). Note that the proportion of sites that recorded low bias
varies between networks (data not shown). Most ARM stations were
characterized by having low bias (76–100 %), whereas SNOTEL sites had the
lowest number of sites with a low bias (32–45 %). SCAN (53–60 %)
and USCRN (60–66 %) had an intermediate number of sites with a low bias.
The subsequent results focused only on the low bias stations.
As might be expected, the Topt values from the NDVI approach had a
much more limited range of values compared with Topt values derived
using the optimization approach (Tables 3–6). From the ARM network, average
Topt based on the NDVI approach ranged from 32 to 36 days, whereas
optimization produced much greater variation (4–32 days; Table 3). At SCAN,
the NDVI approach yielded a broader range of average era Topt
(28–46 days; Table 3). However, again, optimization produced more variable
Topt values (9–39 days; Table 4). A similar pattern was noted at
SNOTEL sites. The NDVI approach yielded higher network average era
Topt values (42–45 days) vs. the more variable and lower
results from the optimization method (17–36 days; Table 5). Finally, USCRN
sites from era 5 (Table 1) exhibited a broad range of values for both
approaches (NDVI of 30–55 days; optimization of 9–28 days; Table 6).
Tables 3–6 show results from the direct correlation between in situ RZSM- and
SWI-based estimates generated from the four satellite products. Network
average values are excluded in this discussion if there were less than three
measurements within an era for a network. Generally, but not always, the
optimization approach yielded higher lagged r values than NDVI.
Interestingly, in the ARM network, in 5 out of 14 instances, the NDVI approach
yielded network average r values that were greater than those obtained from
the optimization method (Table 3). ARM sites from the central Great Plains
had network average r values based on optimization that ranged from 0.450
to 0.707 across eras 1–4 (Table 1), whereas the NDVI approach yielded a
lower and broader variation in r values (0.323–0.704; Table 3).
For SCAN sites, comparisons were made only for eras 2–5 (Tables 1, 4). Era 1
was excluded in this comparison due to limited data availability during this
period. Network average r values based on optimization (0.458–0.720;
Table 3) generally outperformed those based on the NDVI approach (0.428–0.615;
Table 4). Additionally, when examined from a geographic prospective, western
CONUS sites had slightly higher r values based on optimization
(0.477–0.823) than those from either the eastern (0.332–0.777) or central
regions (0.492–0.717).
SNOTEL stations from the intermountain west showed the greatest variability.
Some sites recorded r values below 0, but there were also quite a few sites
with high correlation coefficients (> 0.75). However, in general, network
average r values were lower in SNOTEL (optimization of 0.370–0.572;
NDVI of 0.228–0.590) than at SCAN western sites (Table 5). Finally, the
data from USCRN sites during era 5 (Table 1) had higher network average
r values in central sites vs. the western CONUS (Table 6).
Box plots that depict the NS metric for the ARM (eras 1–4) and
USCRN (era 5) networks. Results for high absolute bias RMSE-optimized
datasets are squares, low absolute bias RMSE-optimized datasets are circles,
and low absolute bias NDVI datasets are triangles.
Box plots depicting the NS metric for the SCAN network. Symbols are the same as
in Fig. 4.
Box plots depicting the NS metric for the SNOTEL network. Symbols are
the same as in Fig. 4.
NS values across the five eras were depicted in Figs. 4–6. Stations with low
absolute bias (±10 %) consistently outperformed stations with high
bias within all networks and during all eras. This was true for both the
optimization and NDVI (data not shown) approaches to constraining T. Not
surprisingly, the optimization approach generally outperformed the NDVI
method. Also, the four satellite products had quite consistent results and
did not exhibit any clear temporal trends. All NS and RMSE network averages
described below were based on the optimization approach to constraining T
and had a low absolute bias. Figure 4 showed NS results from the ARM and
USCRN networks. Network average NS values for ARM ranged from -0.1 to 0.3,
similar to the results from the USCRN network (0.2–0.3). Network average NS
values from the SCAN and SNOTEL networks were shown in Figs. 5 and 6, which
were slightly higher (0.1–0.5).
Box plots depicting the RMSE metric for the ARM (eras 1–4) and
USCRN (era 5) networks. Symbols are the same as in Fig. 4.
Box plots depicting the RMSE metric for the SCAN network. Symbols are
the same as in Fig. 4.
Box plots depicting the RMSE metric for the SNOTEL network. Symbols
are the same as in Fig. 4.
Selected time series associated with poorly performing (NS < 1.00)
outliers with in situ data as solid gray and SWI estimates in dashed
black. Panel (a) shows an example of problematic in situ data. Panel (b) is an example where
there was insufficient SWI data. Panel (c) illustrates an SWI dataset that lacked
the dynamic range present in the in situ data. Panel (d) depicts a discrepancy in timing
between SWI and in situ datasets. Dates are indicated in mm/dd/yyyy format.
Figures 7–9 depicted RMSE values again across the five eras (Table 1). In
many respects, RMSE mirrors NS as a performance metric. Like NS stations,
RMSE values with a low absolute bias outperformed those with high bias.
However, the difference between low and high bias datasets was generally not
as pronounced for the RMSE metric as it was for NS. However, like with NS,
RMSE results showed no discernable temporal trends. RMSE values from the ARM
and USCRN networks were illustrated in Fig. 7. Network average RMSE values
for ARM ranged from 0.02 to 0.04 and were significantly lower than values
from the other networks examined in this study. USCRN network average RMSE
values ranged from 0.04 to 0.05 (Fig. 7). Figure 8 illustrated results from
the SCAN network, and network average RMSE values were similar to USCRN sites
(0.04–0.06). Finally, SNOTEL RMSE results (Fig. 9) were higher than all
other networks (0.05–0.07).
Discussion and conclusions
A long-standing goal of the soil remote sensing community has been to develop
techniques that can observe changes in RZSM. Regrettably, the technology had
not yet progressed to support a global RZSM product based only on remote
sensing retrievals. The use of land surface models such as the community NOAH
model (Chen et al., 1996), Global Land Data Assimilation System (GLDAS;
Rodell et al., 2007), and European
Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis products
(Uppala et al., 2005; Massari et al., 2014) have been used to fill this gap
in recent years. These platforms have become popular and provide an estimate
of root-zone soil moisture that has been applied to field-scale studies
(Albergel et al., 2012; Blankenship et al., 2016; Kedzior and Zawadski,
2016). In addition, another approach that has been suggested is based on
thermal infrared-based remote sensing (e.g., Hain et al., 2011).
Besides ease of use, the exponential filter methodology is an attractive
alternative because it leverages existing remotely sensed soil moisture
platforms. As such, this approach is not hindered by the incipit assumptions
built in to every modeling platform and relies purely on observational data.
Given the potential utility of the exponential filter approach, a detailed
analysis of the potential errors associated with the method is in order.
There are four main sources of error. Two of these errors are associated with
the SWI estimate and include (1) the unsuitability of the exponential filter
at a given site and (2) retrieval errors in the surface soil moisture
dataset. The other two errors are not related to the actual SWI estimate but
instead are errors in the independent datasets that were applied to verify
the SWI estimate at the scale of the 0.25∘ satellite grid. These
errors included (3) issues with in situ datasets (Dorigo et al., 2011, 2013)
and (4) non-representativeness of a point site when compared with the large
(0.25∘) footprint of a surface soil moisture grid used to drive the
filter (Crow et al., 2012). A significant quality control measure involved driving
the filter with surface in situ data instead of satellite soil moisture data.
Stations that scored a NS value below 0.5 based on this approach were rejected as
not suitable. At these sites, perhaps the fundamental assumption of the
exponential filter method that there was hydrologic equilibrium between
the surface and root zone was violated. Therefore, the gross errors recorded
at some sites cannot be ascribed to issues with the exponential filter, and
the data denial experiment demonstrated the robustness of this method at
least in certain instances (Fig. 3).
Extending this approach, we examined the quality of exponential filter results
driven by surface in situ data against background conditions including soil
texture, land cover, and climate zone (data not shown). In terms of soil
texture, in situ sites with loamy textures has a general tendency to
outperform (based on NS value) sand- or clay-dominated sites. This is not
surprising given that the exponential filter generally works best when soil
moisture is moderate (Ford et al., 2014). Soil textures with a low available
water capacity such as sand and clay are more likely to have extreme, both
dry and wet, moisture contents. In terms of land cover, the only consistent
result is that in the SNOTEL network the more open rangeland settings
exhibited slightly better NS values than forest-dominated areas. However,
this pattern was not observed at sites from the other networks. Finally,
there is no clear trend in performance of the exponential filter as a
function of climate zone.
Analysis of poor-performing outliers (NS <-1.00) provided additional
insights into how the exponential filter can fail at some sites (Table 2).
Within the ARM network, all outliers could be attributed to in situ data
issues such as spikes, breaks, anomalous high values that exceed soil
porosity, anomalous low values at zero, and extended plateaus (Dorigo et al.,
2013). An example of such a clearly flawed in situ dataset is shown in
Fig. 10a. Within the SNOTEL network, there was more of a mix in error type
(Table 3). Besides in situ data issues, another significant source of error
was the limited number of days in some of the final SWI datasets. Following
the guidance of Dorigo et al. (2010), SWI datasets with less than 100 days
were rejected. However, based on observations in this study, significant
issues of representativeness were noted when there were less than 400 days
(Fig. 10b). The high altitude of many SNOTEL sites resulted in a longer
freezing season during which a greater number of days were filtered out.
There were some sites with in situ data issues in the SCAN network (Table 2).
However, many of the outliers also were caused by either SWI values that
lacked the dynamic range of the in situ dataset (Fig. 10c) or SWI values that
had significant timing offsets compared with in situ RZSM observations
(Fig. 10d). These issues were the result of either site
non-representativeness or errors in surface soil moisture retrievals.
Finally, USCRN sites exhibited a similar mix of errors as noted in the SCAN
network (Table 2).
A consistent result noted in this study was the impact of bias on other
performance metrics. Consistently better results for all metrics were noted
(Tables 3–6; Figs. 4–9) when there was a low absolute bias (within
10 %) vs. SWI datasets that had a high absolute bias (> 10 %).
Additionally, this observation was observed for SWI values produced with both
approaches to constrain T (minimization of RMSE and the NDVI approach). The
impact of bias on standard objective metrics was a focus of temporal
stability analysis (Vachaud et al., 1985; Martinez-Fernandez and Ceballos,
2005). Sites with little variation
in bias yielded more robust comparisons with remote sensing data (Starks et
al., 2006), which is a result that was confirmed in this study across four distinct in
situ soil moisture networks and satellite products.
Interestingly, the results observed in this study were more impacted by the
in situ network than the surface satellite product used to drive the
exponential filter. In terms of the NS metric, SCAN, SNOTEL, and USCRN
outperformed ARM (Figs. 4–6). The NS metric seemed to have a greater utility
in identifying outliers than the RMSE metric. This was because it ranged
from 1.00 to potentially -∞, unlike RMSE, which ranged in this study
from only 0 to 0.14.
Conversely, when considering the RMSE metric, ARM sites yielded superior
scores compared with SCAN, SNOTEL, and USCRN (Figs. 7–9). Within the ARM
network average RMSE was less than 0.04, which is the baseline value for
accuracy designed for many satellite soil moisture missions (e.g., Kerr et
al., 2001; Entekhabi et al., 2010). SCAN and USCRN were slightly above this
guideline and were similar to RMSE values noted in previous in situ/satellite
soil moisture comparisons (e.g., Brocca et al., 2010; Jackson et al., 2010,
2012; Al Bitar et al., 2012). According to the RMSE metric, SNOTEL sites
performed the worst and was significantly above the 0.04 performance target.
Perhaps the most interesting result from this study was that the performance
metrics in each in situ network did not vary over time. Given that almost two
decades of data were examined, this finding is particularly noteworthy. Therefore,
SWI estimates of RZSM produced by the exponential filter using CCI datasets
can be leveraged for long-term, perhaps even multi-decadal, climate studies
(Manfreda et al., 2011). Another fruitful line of future research could
compare exponential filter estimates of RZSM with those generated by land
surface models. With the proliferation of space-based remote sensing
platforms and the continued development of in situ monitoring networks, the duration
of RZSM time series will only grow. As such, the approaches outlined in this
work can provide the cornerstone to support future assessments of long-term
trends in RZSM, which is an essential climate variable.
The harmonization process
involved in the creation of the surface soil moisture products was described
by Liu et al. (2012), and these datasets are available online
(http://www.esa-soilmoisture-cci.org/node/145).
We also utilized stand-alone data from the AMSR-E mission during eras 2–4. In this
study, we acquired the version produced by the LPRM
(Owe et al., 2008; https://hydro1.gesdisc.eosdis.nasa.gov/data/WAOB/).
The authors declare that they have no conflict of
interest.
Acknowledgements
We acknowledge the support of the NASA Climate Indicator and Data Products
for the National Climate Assessments program through award no. NNX16AH30G.
The assistance of Robert Parinussa (University of New South Wales), Arturo
Diaz (Texas A&M International University), and Luis Carrasco Garza (Texas
A&M International University) is greatly appreciated. Edited by: Erwin Zehe Reviewed by: two
anonymous referees
ReferencesAlbergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N.,
Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From
near-surface to root-zone soil moisture using an exponential filter: an
assessment of the method based on in-situ observations and model simulations,
Hydrol. Earth Syst. Sci., 12, 1323–1337,
10.5194/hess-12-1323-2008, 2008.
Albergel, C., de Rosnay, P., Balsamo, G., Isaksen, L., and Munoz-Sabater, J.:
Soil moisture analyses at ECMWF: evaluation using global-based in situ
observations, Remote Sens. Environ., 118, 215–226, 2012.
Al Bitar, A., Leroux, D., Kerr, Y. H., Merlin, O., Richaume, P., Sahoo, A., and Wood, E. F.:
Evaluation of SMOS soil moisture products over Continental US
using the SCAN/SNOTEL Network, IEEE T. Geosci. Remote, 50, 1572–1586, 2012.Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipai, K., Bonekmap,
H., Figa, J., and Anderson, C.: Initial soil moisture retrievals from the
METOP-A Advanced Scatterometer (ASCAT), Hydrol. Land Surf. Stud., 34, L02401,
10.1029/2007GL031088, 2007.
Bell, J. E., Palecki, M. A., Baker, C. B., Collins, W. G., Lawrimore, J. H.,
Leeper, R. D., Hall, M. E., Kochendorfer, J., Meyers, T. P., Wilson, T., and
Diamond, H. J.: US Climate Reference Network soil moisture and temperature
observations, J. Hydrometeorol., 14, 977–988, 2013.
Blankenship, C. B., Case J. L., Zavodsky, B. T., and Crosson, W. L.:
Assimilation of SMOS retrievals in the Land Information System, IEEE T.
Geosci. Remote, 54, 6320–6332, 2016.Bolten, J. D. and Crow, W. T.: Improved prediction of quasi-global vegetation
conditions using remotely-sensed surface soil moisture, Geophys. Res. Lett.,
39, L19406, 10.1029/2012GL053470, 2012.
Bolten, J. D., Crow, W. T., Zhan, X., Jackson, T. J., and Reynolds, C. A.: Evaluating the utility of remotely sensed soil moisture
retrievals for operational agricultural drought monitoring, IEEE J. Sel. Top.
Appl., 3, 57–66, 2010.
Brocca, L., Morbidelli, R., Melone, F., and Moramarco, T.: Soil moisture
spatial variability in experimental areas of central italy, J. Hydrol., 333,
356–373, 2007.Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z.,
and Hasenauer, S.: Improving runoff prediction through the assimilation of
the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881–1893,
10.5194/hess-14-1881-2010, 2010.
Chen, F., Mitchell, K., Schakke, J., Xue, Y., Pan, H., Koren, V., Duan, Y.,
Ek, M., and Betts, A.: Modeling of land-surface evaporation by four schemes
and comparison with FIFE Observations, J. Geophys. Res., 101, 7251–7268,
1996.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera, R.,
de Rosnav, P., Ryu, D., and Walker, J. P.: Upscaling sparse ground -based soil
moisture observations for the validation of course-resolution satellite soil
moisture products, Rev. Geophys., 50, 2011RG000372, 2012.
Daly, C., Neilson, R. P., and Phillips, D. L.: A statistical-topographic
model for mapping climatological precipitation over mountainous terrain, J.
Appl. Meteorol., 33, 140–158, 1994.Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu,
R. A. M., and Naeimi, V.: Error characterisation of global active and passive
microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14, 2605–2616,
10.5194/hess-14-2605-2010, 2010.Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A.,
Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and
Jackson, T.: The International Soil Moisture Network: a data hosting facility
for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15,
1675–1698, 10.5194/hess-15-1675-2011, 2011.Dorigo, W. A., Xavier, A., Vreugdenhill, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global automated quality control of in situ soil moisture data from the
International Soil Moisture Network, Vadose Zone J., 12, vzj2012.0097,
10.2136/vzj2012.0097, 2013.
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W.N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeir, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsnag, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP)
mission, P. IEEE, 98, 704–716, 2010.Ford, T. W., Harris, E., and Quiring, S. M.: Estimating root zone soil
moisture using near-surface observations from SMOS, Hydrol. Earth Syst. Sci.,
18, 139–154, 10.5194/hess-18-139-2014, 2014.
Gaiser, P. W., St. Germain, K. M., Twarog, E. M., Poe, G. A., Purdy, W.,
Grossman, W., Jones, W. L. Spencer, D., Golba, G., Cleveland, J., Choy, L., and
Bevilacqua, R. M.: The WindSat spaceborne polarimetric microwave radiometer:
Sensor description and early orbit performance, IEEE T. Geosci. Remote,
42, 2347–2361, 2004.Hain, C. R., Crow, W. T., Mecikalski, J. R. Anderson, M. C., and Holmes, T.:
An intercomparison of available soil moisture estimates from thermal-infrared
and passive microwave remote sensing, J. Geophys. Res.-Atmos., 166, D15107,
10.1029/2011JD015633, 2011.
Hutchinson, J. M. S.: Estimating near-surface soil moisture using active
microwave satellite imagery and optical sensor inputs, T. ASAE, 46, 225–236,
2003.
Jackson, T. J., Le Vine, D. M., Hsu, A. Y., Oldak, A., Starks, P. J., Swift,
C. T., Isham, J. D., and Haken, M.: Soil moisture mapping at regional scales
using microwave radiometry: The Southern Great Plains Hydrological
Experiment, IEEE T. Geosci. Remote, 37, 2136–2151, 1999.
Jackson, T. J., Hsu, A. Y., and O'Neill, P. E.: Surface soil moisture
retrieval and mapping using high-frequency microwave satellite observations
in the Southern Great Plains, J. Hydrometeorol., 3, 688–699, 2002.
Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch, D. D.,
Seyfried, M., Goodrich D. C., Moran, M. S., and Du, J.: Validation of
Advanced Microwave Scanning Radiometer Soil Moisture Products, IEEE T.
Geosci. Remote, 48, 4256–4272, 2010.
Jackson, T. J., Bindlish, R., Cosh, M. H., Zhoa, T., Starks, P. J., Bosch, D.
D., Seyfried, M., Moran, M. S., Goodrich, D. C., Kerr, Y. H., and Leroux, D.:
Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over
Watershed Networks in the US, IEEE T. Geosci. Remote, 50, 1530–1543, 2012.
Kedzior, M. and Zawadski, J.: Comparative study of soil moisture from SMOS
satellite mission, GLDAS database, and cosmic ray-neutrons measurements at
COSMOS in Eastern Poland, Geoderma, 283, 21–31, 2016.
Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Maerinuzzi, J. M., Font, J.,
and Berger, M.: Soil moisture retrieval from space: The Soil Moisture and
Ocean Salinity (SMOS) mission, IEEE T. Geosci. Remote, 39, 1729–1735, 2001.
Klute, A.: Water retention: Laboratory methods, Methods of Soil Analysis:
Part 1, in: Physical and Minerological Methods, edited by: Klute, A.,
American Society of Agronomy and Soil Science Society of America, 635–662,
1986.
Kumar, S. V., Reichle, R. H., Koster, R. D., Crow, W. T., and Peters-Lidard,
C. D.: Role of subsurface physics in the assimilation of surface soil
moisture observations, J. Hydrometeorol., 10, 1534–1547, 2009.
Lakshmi, V., Wood, E. F., and Choudhury, B. J.: Investigation of effect of
heterogeneities in vegetation and rainfall on simulated SSM/I brightness
tempeatures, J. Appl. Meteorol., 36, 1309–1328, 1997.
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and
Wood, E. F.: Inroads of remote sensing into hydrologic science during the WRR
era, Water Resour. Res., 51, 7309–7342, 2015.
Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W.,
McCabe, M. F., Evans, J. P., and van Dijk, A. I. J. M.: Trend-preserving
blending of passive and active microwave soil moisture retrievals, Remote
Sens. Environ., 123, 280–297, 2012.Manfreda, S., Lacava, T., Onorati, B., Pergola, N., Di Leo, M., Margiotta, M.
R., and Tramutoli, V.: On the use of AMSU-based products for the description
of soil water content at basin scale, Hydrol. Earth Syst. Sci., 15,
2839–2852, 10.5194/hess-15-2839-2011, 2011.Manfreda, S., Brocca, L., Moramarco, T., Melone, F., and Sheffield, J.: A
physically based approach for the estimation of root-zone soil moisture from
surface measurements, Hydrol. Earth Syst. Sci., 18, 1199–1212,
10.5194/hess-18-1199-2014, 2014.
Martinez-Fernandez, J. and Ceballos, A.: Mean soil moisture estimation using
temporal stability Analysis, J. Hydrol., 312, 28–38, 2005.Massari, C., Brocca, L., Barbetta, S., Papathanasiou, C., Mimikou, M., and
Moramarco, T.: Using globally available soil moisture indicators for flood
modelling in Mediterranean catchments, Hydrol. Earth Syst. Sci., 18,
839–853, 10.5194/hess-18-839-2014, 2014.
McCabe, M. F., Gao, H., and Wood, E. F.: Evaluation of AMSR-E-derived soil
moisture retrievals using ground-based and PSR airborne data using SMEX02, J.
Hydrometeorol., 6, 864–877, 2005.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R.
D., and Veith, T. L.: Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900,
2007.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.:
Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote Sens., 41,
215–229, 2003.Owe, M., De Jeu, R. A. M., and Holmes, T. R. H.: Multisensor historical
climatology of satellite-derived global land surface moisture, J. Geophys.
Res.-Earth, 113, F01002, 10.1029/2007JF000769, 2008.Peterson, A. M., Helgason, W. D., and Ireson, A. M.: Estimating field-scale
root zone soil moisture using the cosmic-ray neutron probe, Hydrol. Earth
Syst. Sci., 20, 1373–1385, 10.5194/hess-20-1373-2016, 2016.
Qiu, J., Crow, W. T., Nearing, G. S., Mo, X., and Liu, S.: The impact of
vertical measurement depth on the information content of soil moisture time
series data, Geophys. Res. Lett., 41, 4997–5004, 2014.
Reece, C. F.: Evaluation of a line heat dissipation sensor for measuring soil
matric potential, Soil Sci. Soc. Am. J., 60, 1022–1028, 1996.
Reichle, R., De Lannoy, G., Koster, R., Crow, W., and Kimball, J.: SMAP L4 9
km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 2,
NASA National Snow and Ice Data Center, 2016.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, R., Schaefer, G. L., Cosh, M. H., and
Jackson, T. J.: The USDA Natural Resources Conservation Service Soil Climate
Analysis Network (SCAN), J. Atmos. Ocean. Tech., 24, 2073–2077, 2007.
Schaefer, G. L., Cosh, M. H., and Jackson, T. J.: The USDA Natural Resources Conservation Service Soil Climate
Analysis Network (SCAN), J. Atmos. Ocean. Tech., 24, 2073–2077, 2007.
Seyfried, M. S., Grant, L. E., Du, E., and Humes, K.: Dielectric loss and
calibration of the Hydra Probe soil water sensor, Vadose Zone J., 4,
1070–1079, 2005.
Starks, P. J., Heathman, G. C., Jackson, T. J., and Cosh, M. H.: Temporal
stability of soil moisture profile, J. Hydrol., 324, 400–411, 2006.Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., Da Costa
Bechtold, V., Fiorino, M., Gibson, J.K., Haseler, J., Her- nandez, A., Kelly,
G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Anderson,
E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Van De Berg, L., Bidlot,
J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M.,
Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L.,
Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette,
J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenbreth, K.
E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40
re-analysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012, 10.1256/qj.04.176,
2005.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring
vegetation, Remote Sens. Environ., 8, 127–150, 1979.
Vachaud, G., DeSilnas, A. P., Balabanis, P., Vauclin, M.: Temporal stability
of spatially measured soil water probability density function, J. Soil Sci.
Soc. Am., 49, 822–828, 1985.
Wagner, W, Lemoine, G, and Rott, H.: A method for estimating soil moisture
from ERS scatterometer and soil data, Remote Sens. Environ., 70, 191–207,
1999.Wagner, W., Scipal, K., Pathe, C., Gerten, D., Lucht, W., and Rudolf, B.:
Evaluation of the agreement between the first global remotely sensed soil
moisture data with model and precipitation data, J. Geophys. Res.-Atmos.,
108, 4611, 10.1029/2003JD003663, 2003.
Wentz, F. J., Meissner, T., Gentemann, C., Hilburn, K. A., and Scott, J.:
Remote sensing systems GCOM-W1 AMSR2 Environmental Suite on 0.25 deg grid,
Remote Sensing Systems, Santa Rosa, Calfornia, USA, 2014.
Western, A. W., Zhou, S. L., Grayson, R. B., McMahon, T. A., Bloschl, G., and
Wilson, D. J.: Spatial correlation of soil moisture in small catchments and
its relationship to dominant spatial hydrological processes, J. Hydrol., 286,
113–134, 2004.Wilson, D. J., Western, A. W., and Grayson, R. B.: Identifying and
quantifying sources of variability in temporal and spatial soil moisture
observations, Water Resour. Res., 40, W02507, 10.1029/2003WR002306, 2004.