This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and HOBE sites are based on the here-proposed natural log fit. The newly derived ThetaProbe fit functions should be used for hand-held applications only, but prove to be of value for the acquisition of instantaneous large-scale soil moisture estimates.
The circumpolar northern colder climate zone (boreal forest and tundra) contributes with a substantial fraction to the total global land mass. Because of slower decomposition rates in these regions pronounced organic layers have been accumulating on top of the mineral soils. Particularly when frozen, organic-rich soils store a significant amount of carbon acting as important sinks. However, the higher northern latitudes are especially sensitive to climate change (IPCC, 2007) due to above-average rising temperatures (e.g., Hansen et al., 2006). Thus, a considerable positive feedback on global warming is likely once additional carbon is respired from thawing grounds (Stokstad, 2004). The prediction of the overall response of these ecosystems to global warming is currently highly uncertain. In this context, hydrological processes play a key role and soil moisture is one of the main factors to be assessed to understand and quantify the processes and feedback mechanisms controlling water, energy, and carbon fluxes at the land surface–atmosphere interface.
Given the particular hostility and remoteness of high latitude environments, spaceborne remote sensing techniques together with land surface modeling constitute essential tools for soil moisture observations at high temporal resolution and with complete spatial coverage (e.g., Reichle et al., 2007; Albergel et al., 2012). Nevertheless, spatially distributed in situ soil moisture measurements are indispensable for the calibration/validation (cal/val) activities of these global soil moisture products as well as in order to increase process-understanding at local scale.
Electromagnetic-based sensors belong to the most popular in situ soil
moisture measuring techniques, as they can be used for automated continuous
measurements at high temporal resolution in most soil types and plant growth
substrates, including shallow recordings close to the surface. Different
sensor types have been developed using capacitance and impedance as well as
time- or frequency-domain reflectometry and transmissometry (TDR, FDR,
TDT, and FDT) methods. The shape and design of the sensors as well as the
measurement and/or raw data “interpretation” is highly variable (Robinson et
al., 2008). Nevertheless, they all take advantage of the large difference
between the relative permittivity (
In the case of all electromagnetic sensors, the measured raw signal of a
substrate is closely related to
Currently available impedance and capacitance sensors operate at frequencies
between 20 and 300 MHz, while TDR/FDR and TDT/FDT mainly function in the GHz
range (Vaz et al., 2013). The latter are generally considered more accurate
with less signal contribution of
While a lot of authors find manufacturers' default calibrations sufficiently
accurate for various mineral soil types (apart from very clayey soils), many
studies conclude that calibrations specific to organic-rich soils and humus
horizons are crucial (e.g., Topp et al., 1980; Herkelrath et al., 1991; Roth
et al., 1992; Paquet et al., 1993; Jones et al., 2002; Pumpanen and
Ilvesniemi, 2005; Kizito et al., 2008; Sakaki et al., 2011; Vaz et al.,
2013). Organic material differs from mineral by its complex structures and
small bulk densities. The resulting high porosities and large specific
surface areas cause the following two effects: (1) substantial water holding
capacities up to 0.8–0.9 cm
The influence of organic matter on the TDR response has been studied by many authors (e.g., Topp et al., 1980; Roth et al., 1990, 1992; Herkelrath et al., 1991; Pepin et al., 1992; Paquet et al., 1993; Malicki et al., 1996; Börner et al., 1996; Myllys and Simojoki, 1996; Schaap et al., 1996; Kellner and Lundin, 2001; Jones et al., 2002; Pumpanen and Ilvesniemi, 2005; Shibchurn et al., 2005; Nagare et al., 2011; Vasquez, 2013). However, for other electromagnetic sensors, such analyses are more scarce in the literature. Recently, Vaz et al. (2013) evaluated standard calibrations for eight electromagnetic sensors. They pointed to the rarity, and thus, necessity of further investigations on the capacitance and impedance sensor response in substrates of varying organic matter content.
At the Finnish Meteorological Institute's Arctic Research Center (FMI-ARC)
in Sodankylä, northern Finland, the exploration of hydrological
processes is one of the multidisciplinary key research topics. On this site
there are several projects dealing with the characterization of moisture
content in organic-rich soil surfaces as well as freeze-thaw characteristics
using different remote sensing techniques as well as land surface modeling
(e.g., Rautiainen et al., 2012, 2014; European Space Agency: ESA SMOS Mention of manufacturers is for the
convenience of the reader only and implies no endorsement on the part of the
authors.
With the purpose of serving coarse-resolution satellite remote sensing and land surface modeling studies, the objective was to provide generic calibration functions holding for different types of organic material as encountered within the large areas under consideration. Necessarily, these functions hold a decreased degree of detail and might lack high accuracy, but will clearly outperform default calibration functions provided by the sensor manufacturers. Additionally, they should be applicable without requiring auxiliary information for the large area of interest, such as bulk density/porosity or specific surface area/bound water fraction, as integrated in more sophisticated calibration methods (e.g., Malicki et al., 1996; Dirksen and Dasberg, 1993).
This article presents the Decagon 5TE and ThetaProbe sensor calibrations for organic soil surface layers, derived from field and laboratory measurements using soils from different locations in northern regions, mainly including the Sodankylä and HOBE network areas. While some ThetaProbe calibration efforts are present in the literature for organic material from natural soils (see Sect. 3.2), to the knowledge of the authors so far no equivalent studies have been reported in the case of the Decagon 5TE sensors. It seems that only Vaz et al. (2013) had looked into the issue for this sensor type, however, using artificial organic material in a limited water content range. Thus, the goal here was to extend the range of validity of the 5TE calibration function for a variety of natural organic substrates and create something more widely applicable.
To avoid inconsistencies, the same measurement and calibration protocol was followed at all sites. The developed fit functions were evaluated against the manufacturers' calibrations as well as earlier published fitting functions. Furthermore, soil moisture time series from both sensors collected at two Sodankylä network sites were compared, using both manufacturer's default and our own derived calibrations. Measurements from the underlying mineral soil layers with variable soil organic matter content were also considered in order to demonstrate the validity of the manufacturer calibrations within those layers.
Figure 1 gives an overview of the soil sample locations used in this study.
At the two main sites in Finland and Denmark, the Decagon 5TE and ThetaProbe
responses were studied in detail. Additionally, some samples used for
ThetaProbe analysis were collected in Scotland and Siberia. The soil samples
used for calibration and their characteristics are listed in Table 1. It is
also indicated which samples were used for laboratory and field
calibrations, respectively. According to humus form classifications (Broll
et al., 2006; Zanella et al., 2011), a layer is considered organic if the
soil organic matter (SOM) content is greater than
Overview of the samples used for calibration. The sample name
starts with the study site, followed by land cover type, soil material and
indication whether used in laboratory or field calibration. O, M, F, and L
denote organic, mineral, field, and lab, respectively. The letter specifying
the soil material is complemented by a number if more than one sample of the
same soil material is available at a given study site.
Overview over organic samples, classified according to the European Humus Forms Reference Base (Zanella et al., 2011).
Overview over all sampling locations (main study sites are in bolt).
Soil dry bulk densities range 0.05–0.4 and 1.0–1.5 g cm
In the following, the different sites, including the collected samples and data, are described in detail.
The FMI-ARC is situated in Sodankylä (67.368
Soil moisture sensor characteristics from manufacturer manuals as well as findings of Vaz et al. (2013).
Around the same two stations spatial soil moisture variability had been assessed during summer 2012 by means of ThetaProbe measurements. For 20 days within a 3-month period (June–August 2012), these measurements were taken from the surface in a hand-held fashion. As they were not involved in the calibration process these data served for validation in this study.
The Danish site is situated in the Skjern River catchment in western Denmark
and has been intensely investigated in the framework of HOBE (Jensen and Illangasekare, 2011). Soil samples were
collected within the Gludsted spruce plantation (56.074
In fall 2013, the Centre d'Etudes Spatiales de la Biosphère (CESBIO),
Toulouse, collected peat samples in two neighboring bogs on the Island Islay
in western Scotland (55.743
The Decagon ECH2O 5TE sensor is based on the capacitance method to measure
the medium around three 5.2 cm-long prongs at 70 MHz frequency (Decagon
Devices Inc., 2014). The plastic-coated sensor head is sensitive to the
surrounding permittivity and thus, should be completely covered by the
medium. When using a Decagon Em50 digital/analog data logger,
Some of the probe's characteristics are listed in Table 3, including
information from the manufacturer manual as well as findings by Vaz et al. (2013).
Soil moisture accuracy in mineral soils is around 0.03–0.04 cm
The Delta-T ThetaProbe ML2x is a soil moisture sensor with four 6 cm-long
steel rods building an array whose impedance varies with the moisture
content of the measured medium (Delta-T Devices Ltd., 1999). The
corresponding voltage output V at 100 MHz can be converted into the soil's
apparent relative permittivity, using
Laboratory sensor calibrations for the organic and mineral substrates
collected in Finland and Denmark (Sects. 2.1 and 2.2) were carried out at the
respective institutions, following the same protocol. As organic material
can be strongly affected by shrinkage during drying (e.g., Schaap et al.,
1996; Pumpanen and Ilvesniemi, 2005), a significant error might occur when
assuming a constant bulk density over the entire water content range. To
avoid this issue the material was initially saturated and the changing
volume and bulk density during the subsequent dry down were automatically
accounted for. The saturated bulk densities of the respective soils were
previously estimated from field samples and the collected saturated material
was packed accordingly into large buckets. In the center of each bucket one
Decagon 5TE sensor was installed permanently at the surface. The sensors
were always placed in horizontal position with the blades in a vertical
direction in order to avoid ponding of water. Distances to the bucket
borders were clearly larger than the maximum diameter of the probe's
sensitivity (Table 3). The Decagon 5TE readings were logged continuously,
while ThetaProbe measurements and gravimetric samples were taken from the
surface at defined times. A lot of attention was paid to proper application
of the ThetaProbes: the four rods of the instrument were inserted vertically
and pushed firmly into the substrate in order to assure good contact and
avoid air gaps, and yet careful not to compress the material too much. This
is common practice with this sensor type, in the case of organic material, for
example, applied by Nemali et al. (2007), Kargas and Kerkdis (2008), and Vaz
et al. (2013) in the laboratory as well as by Kurum et al. (2012) in the
field. In our case, three readings were taken at a given time step in order
to check the repeatability of the measurements, while the mean was recorded
each time. Additionally, one reference sample was extracted per time step
using steel rings of known volume. As buckets were of large sizes, enough
material for all the gravimetric samples was available without disturbing
the sensor measurements and no backfilling of material was necessary. The
samples were oven dried at 105
The samples from organic surface horizons in Siberia and Scotland (Sect. 2.3) were handled at CESBIO, France. They were not large enough to place Decagon 5TE sensors. Thus, only ThetaProbe readings (in triplicates) and respective gravimetric samples were taken.
During the field calibration experiment in the vicinity of one Danish forest network station (see Sect. 2.2), a Decagon 5TE sensor was installed in the organic horizon and logged continuously. After the first measurements of extremely dry conditions in summer 2013, the soil was saturated. During the drying period, three ThetaProbe readings and gravimetric samples were acquired and averaged for each measurement in time. In the case of these data and all additional field observations used in this study (Decagon 5TE–ThetaProbe–gravimetric sample couples described in Sect. 2.2), sensor installation, measurement, and drying protocols were identical to the ones described above for the laboratory calibration.
All field and laboratory data were gathered and sensor output was plotted against volumetric moisture content for Decagon 5TE–ThetaProbe and organic–mineral samples, respectively. In the case of continuously logged Decagon 5TE data, the two measurements closest to each ThetaProbe/sample timestamp were extracted and averaged. The resulting number of available data points per site and sensor type is indicated in Table 1. Sensor calibrations based on our measurements were carried out for the ensemble of data measured in the organic horizons of all studied sites, while for the data from underlying mineral soil layers the validity of the manufacturer calibrations was tested. Calibration curves were fitted through the data using mathematical descriptions already reported in the literature on soil moisture sensor calibration. The fitted functions were compared with corresponding manufacturer calibration curves as well as calibrations reported in the literature (specified in Sect. 5.3, Table 6, Fig. 4).
To further validate the proposed fit functions, Decagon 5TE and ThetaProbe
soil moisture time series from the forest (“UG Forest 1”) and heathland
(“HA Open 1”) network stations in Sodankylä recorded during summer 2012
(see Sect. 2.1) and not used in the calibration process were compared
to test whether the soil moisture from the two sensor types agreed. At both
sites, one of the three Decagon 5TE sensors at 5 cm depth was chosen for this
study together with the ThetaProbe surface data sampled in the immediate
vicinity. The five ThetaProbe values available per day were averaged for our
purpose. In the case of the Decagon 5TE data the two time steps closest to the
mean ThetaProbe acquisition time were averaged, resulting in maximum time
shift between the two measurements of less than 30 min. For the organic
surface layer at the “UG Forest 1” site soil moisture estimates using
manufacturer default calibrations as well as newly derived fit functions
were compared. Thereby, the ThetaProbe “organic” default function was
chosen, while for the Decagon 5TE sensor the only available Topp et al. (1980)
equation for mineral soils was applied. For the low organic mineral
surface soil at the “HA Open 1” site default functions for mineral soils
provided by the manufacturers were considered. To get a better insight into
the temporal evolution of the soil moisture pattern over time, hourly
rainfall intensities (R_1H) measured at Tähtelä at
the center of the Sodankylä research area (
For the statistical analysis throughout our study the Pearson's correlation
coefficient (
Figure 2 depicts the Decagon 5TE and ThetaProbe output (
Decagon 5TE apparent relative permittivity
For both sensor types, the field measurements (triangles) are in good
agreement with the laboratory data (dots). For the mineral soils with a SOM
content below 10 % (blue colors) both Decagon 5TE and ThetaProbe data
scatter around the respective manufacturer calibration curves, and thus,
demonstrate the validity of the latter. In the case of the Decagon 5TE sensor
this underlines earlier results by Vasquez and Thomsen (2010) and Bircher et
al. (2012a) who also found the sensor to be accurate within
In contrast, for the mineral samples with a SOM exceeding 10 % (purple
colors) the trends in the data differ for both sensor types. While for the
ThetaProbe (right column) the data of increased SOM content show a behavior
comparable to the measurements in mineral soils with SOM
The ThetaProbe data (right column panels) for the organic soil layers (top row panels) again show scatter but with a clear trend irrespectively of the sample location or humus type. However, in contrast to the Decagon 5TE data (left column, top row panels), there is a closer match between our soil moisture measurements and soil moisture computed based on the default calibrations for mineral and organic substrates. It is worth noting that there is only a small difference in the soil moisture estimation between the two default calibration curves whilst their shape remains consistent. Nevertheless, in the medium to high range of the sensor outputs (600–1000 mV) for the organic samples the default curves are not able to reproduce our measurements due to more pronounced curvature in our data. This results in (1) a tendency towards increased sensor output at a given moisture content compared to both default curves in the middle range, and (2) saturation in the sensor's response around 1000 mV.
In conclusion, one can state that for both sensor types deviating sensor
outputs in the case of measurements conducted in organic horizons (yellow–red
colors) compared to mineral layers with low SOM content (blue colors) are
clearly demonstrated. The scatter in the data from organic horizons is in
comparable range as reported for similar calibration studies using TDR
sensors (e.g., Schaap et al., 1996; Kellner and Lundin, 2001; Pumpanen and
Ilvesniemi, 2005; Nagare et al., 2011). Thereby, the spread is always higher
for organic substrates compared to mineral soils due to the complex nature
(i.e., very high porosities and large specific surface areas) of the former.
However, no distinct differences in measurements' behavior from samples
ranging a variety of humus types and acquired by different users are
noticeable. Based on this first analysis it can be hypothesized that for
each sensor type one calibration function should hold for reliable estimates
of the moisture content in organic surface horizons (
Figure 3 illustrates the calibration curves fitted through the data measured in the different organic soil layers (black circles). For the Decagon 5TE sensor data pairs of apparent relative permittivity readings and corresponding volumetric moisture contents (left column panels) different functions were tested: third-order polynomial (dark blue), power (light blue), natural logarithm (red), and square root (orange). With respect to the ThetaProbe (right column panels), fit functions (red) were derived for both output voltage–volumetric moisture and apparent relative permittivity–volumetric moisture pairs, (third- and first-order polynomial in top and bottom row, respectively), as they are equally used in many studies. For comparison, manufacturer calibration curves are also included in the plots (continuous and dashed black lines in the case of curves for organic and mineral materials, respectively). All functions shown in Fig. 3 are listed in Table 4 and the corresponding fitting statistics are presented in Table 5.
Fitting functions for the Decagon 5TE apparent relative
permittivity
For the Decagon 5TE sensor, the statistics show no clear difference between
the different tested fit functions. Compared to the manufacturer calibration
all of them result in a significantly decreased bias and an improved RMSD
while
For the ThetaProbe the third-order polynomial fit between the sensors
millivolt (mV) output and the measured soil moisture (right column, top row panels)
shows a similar curve shape as the default functions for mineral and organic
substrates, but with the aforementioned increased curvature. Meanwhile, a
steeper slope compared to the quasi-linear default curves becomes apparent
in the case of the first-order polynomial fit through the
Figure 4 displays the functions fitted (red curves) to the measurements
performed on our organic samples (only the selected logarithmic function for
Decagon 5TE) together with petrophysical or empirical relationships for
organic samples taken from the literature. In the case of the Decagon 5TE sensor
(left column) this includes the calibration for an organic plant potting mix
reported by Vaz et al. (2013) for the same sensor type (orange line) as well
as the following calibration laws for organic samples obtained from TDR
measurements (blue and green lines): Pepin et al. (1992), Roth et al. (1992),
Paquet et al. (1993), Malicki et al. (1996) using a bulk density of
0.1 g cm
Comparison between reported petrophysical and empirical
relationships applied to our data measured in organic soil layers for the
Decagon 5TE apparent relative permittivity
The natural log fit through the Decagon 5TE data (left column panels, red line) and
the calibration proposed by Vaz et al. (2013) applied to the respective data
(left column, orange line) exhibit a similar
Manufacturer's default calibration functions and functions fitted
through the data measured in organic layers (SOM
Statistics for manufacturer's default calibration curves and
functions fitted through the data measured in organic layers (SOM
For the functions derived from TDR measurements in organic soil layers
For the ThetaProbe mV versus moisture content relationship (right column,
top row) all considered calibrations show very similar behavior as the
default calibrations (black lines) up to
In the case of the ThetaProbe
The presented results indicate that for the ThetaProbe data a clear consistency between measurements, fitted functions, theory, and the literature calibrations is lacking. As practiced in our experimental setup, Nemali et al. (2007), Kurum et al. (2012), and Vaz et al. (2013) also removed and reinserted the ThetaProbe after each measurement, while in the studies by Yoshikawa et al. (2004) and Kargas and Kerkidis (2008) probes remained installed throughout the entire experiments. Certainly, a hand-held application with slightly changed sampling location each time results in increased data variability compared to permanently installed probes, the effect being more pronounced in organic substrate of complex structure compared to more homogeneously distributed mineral soils. However, irrespectively of the two approaches used, no clear difference is detectable in the functions' curve shapes. Another plausible explanation for the nonuniform behavior could be the ThetaProbe's rod configuration that significantly concentrates the electromagnetic field around the central electrode, resulting in a small sampling volume (Table 3). This drawback was already raised by Robinson et al. (1999) and Vaz et al. (2013) who stated that this possibly renders the measurements more sensitive to compaction during the insertion of the instrument, as the effect is most distinct around the probe's center. Additionally, this problem becomes more important as moisture content increases. This would explain why the agreement between different calibration curves is best at very small water contents and deteriorates more and more towards high soil moisture values.
Figure 5 shows the comparison of average ThetaProbe and Decagon 5TE soil moisture estimates collected in Sodankylä during summer 2012. Time series (left column panels) and scatter plots (right column panels) of soil moisture measured in 0–5 cm depth from the “HA Open 1” network station with low organic mineral soil (top row panels) as well as at the “UG Forest 1” network station with a pronounced organic surface layer (bottom row panels) are depicted. In the case of “UG Forest 1”, soil moisture data sets using both default calibrations (black) and newly derived fits (red) are presented. For the ThetaProbe average of five readings, respective standard deviations are displayed as error bars in the time series (left column), and hourly rainfall intensities (R_1H) are also plotted along (black bars). Details on the applied calibration functions as well as corresponding statistics are given in Table 7.
Time series (left column panels) and scatter plots (right panels) for the
soil moisture (
Statistics for the applied petrophysical and empirical
relationships for organic soil layers extracted from the literature as well as
for manufacturer's default calibration curves and our best fits (as
presented in Fig. 4).
The measurements of the two sensor types at the “HA Open 1” site (top row panels) are in very good agreement using the default calibrations (black signatures) for mineral soils. In contrast, applying the most appropriate default calibrations available for the two sensors at the “UG Forest 1” site (bottom row), a pronounced difference in soil moisture content is observed. Thereby the ThetaProbe soil moisture estimates are much wetter and their dynamic range much larger compared to the Decagon 5TE sensor. When using our fit functions derived for organic material (third-order polynomial for ThetaProbe and natural logarithm for Decagon 5TE, red signatures), the agreement becomes much better with significantly decreased RMSD and bias. Also, it now nicely stands out that the mean soil moisture level of the sandy mineral soil (top row panels) is lower but with larger temporal dynamics compared to the organic surface layer (bottom row panels). This behavior is expected due to low and high water retention capacities of the two materials, respectively.
Statistics for comparison of 0–5 cm volumetric moisture content (
Only the correlation between the two sensors remains still low in the case of the organic layer, especially caused by the observed scatter in the ThetaProbe data obtained by a hand-held application with constantly changed sensor locations. This scatter is in similar range with the data variability presented by Kurum et al. (2012), and significantly larger than observed in the mineral soil, both in terms of daily standard deviations of the five probe readings (error bars) and day-to-day variations. As already discussed in Sect. 5.3, the more pronounced small-scale variabilities in the organic substrate are a consequence of more complex structure compared to the more homogeneously distributed sandy soil encountered at the “HA Open 1” site, possibly intensified by compaction effects originating from the susceptible sensor. However, irrespectively the cause, the newly derived fit functions clearly outperform the default calibration functions at the “UG Forest 1” site.
We suggest that these new ThetaProbe calibrations for organic substrates should only be used for the probe application method they were derived from, i.e., handheld. In that case, even if soil moisture data acquired using the ThetaProbe in organic-rich soils should be interpreted carefully, the sensor used together with the here-proposed calibration functions proves robust and of value for the acquisition of quick and instantaneous information about the moisture content for large areas as, for example, practiced in airborne campaigns for satellite cal/val purposes (e.g., Cosh et al., 2005; Bircher et al, 2012b). There, averaging over larger sets of readings will further balance out differing compaction and heterogeneity effects in individual readings – compared to our example where the mean of only five ThetaProbe readings was taken for comparison with point station data.
Finally, comparison with hourly rainfall intensities shows that the Decagon 5TE soil moisture time series estimated using the newly developed calibration function also well reflect the precipitation pattern, demonstrating the sensor's ability to yield reliable soil moisture time series in both mineral and organic substrates. Based on the very satisfying overall performance of the derived natural log fit function, it was applied in the calculation of the Decagon 5TE network soil moisture from organic surface layers at the Sodankylä and HOBE study sites to improve the quality in the data gathered so far.
At both the Finnish Meteorological Institute's Arctic Research Center (FMI-ARC) in Sodankylä and the study site of the Danish Center for Hydrology (HOBE), soil moisture is a key research topic. With the purpose of serving coarse-resolution satellite remote sensing and land surface modeling studies, Decagon 5TE sensors are applied in permanent soil moisture networks while ThetaProbes are used for hand-held soil moisture measurement campaigns. Because both locations are characterized by organic-rich soils, a joined effort aimed at calibrating these two electromagnetic sensor types for organic surface layers with SOM contents above 30 %. While some ThetaProbe calibration efforts for organic soil horizons are present in the literature, for the Decagon 5TE sensor such a calibration function has only been reported for an artificial organic material measured throughout a limited water content range (Vaz et al., 2013). The objective of the here-presented study was to provide generic and widely applicable calibrations for both studied sensor types holding for a variety of natural organic substrates as encountered within the large areas under consideration. Necessarily, these functions hold a decreased degree of detail and might lack high accuracy, but will clearly outperform standard calibration functions reported by the manufacturers. The used soil samples originated from different locations in northern regions, mainly including the Sodankylä and HOBE network areas, spanning a wide range of different humus types. We believe that a reliable calibration approach has been worked out with (1) the same measurement and calibration protocol followed at all sites, (2) comparison of data from organic and mineral horizons including laboratory and field measurements, and (3) consideration of material-specific characteristics such as shrinkage and charring during drying.
For both the Decagon 5TE sensor and the ThetaProbe, the variety of organic
samples showed a consistent sensor output–moisture content response.
Likewise, this was the case when the laboratory experiment was repeated in
the field under less disturbed conditions, demonstrating independence of the
acquired data from the chosen experimental setup. Deviating sensor outputs
for measurements conducted in organic horizons (
Based on the above results, for all data measured in the organic horizons,
one calibration function was derived per sensor type. A natural logarithm
and first-order polynomial were fitted through the
The fact that there was no clear difference in the data obtained from the different sampling sites spanning a variety of humus types and acquired by different users strengthens confidence that the derived calibration functions are not only site specific but can be applied over a wide range of locations and organic materials of differing characteristics and SOM contents. This renders them highly suitable to support large-scale remote sensing and land surface modeling studies.
In the case of the Decagon 5TE sensor, the reliability of the proposed calibration function is further underlined by the fact that it obeys basic physical principles (i.e., increased bound water fraction in the case of organic material), the good agreement with the Decagon 5TE calibration law for a plant potting mix reported by Vaz et al. (2013), as well as by comparable curve shapes as presented in respective TDR calibration studies. Meanwhile, for the ThetaProbe data, such a clear consistency between measurements, fitted functions, and theory is lacking, which is further reflected in the nonuniform behavior of earlier derived calibration laws for organic material reported by other authors.
Comparison of independent Decagon 5TE and ThetaProbe soil moisture time series using default calibrations (not used for the calibration) yield good agreement for the Sodankylä “HA Open 1” network stations' mineral surface layer. In the case of the “UG Forest 1” network stations' organic surface horizon reasonable accordance could only be achieved when using our fit functions derived for organic material (natural logarithm for Decagon 5TE and third-order polynomial for ThetaProbe). The latter significantly improved RMSD and bias so that average soil moisture levels coincided. Only the correlation between the two sensors in the organic layer stayed low, especially caused by the observed scatter in the ThetaProbe data. This is mostly a consequence of the hand-held application with constantly changed sensor locations, leading to more pronounced short range variabilities in the data from a highly heterogeneous material, possibly intensified by compaction effects originating from the susceptible sensor. However, irrespectively the cause, the newly derived calibration fit functions clearly outperform the default functions at the “UG Forest 1” site.
We suggest that the newly derived ThetaProbe calibration fit functions for organic substrates should only be used together with the probe application it was derived from, i.e., handheld. In that case, the functions prove robust and of value for the acquisition of quick and instantaneous information about the moisture content for large areas, where averaging over larger sets of readings will balance out differing compaction and heterogeneity effects in individual readings.
Finally, field data from Sodankylä demonstrate the ability of the Decagon 5TE sensor to reflect precipitation patterns in mineral soils as well as organic horizons. Based on the very satisfying overall performance of the derived natural log fit function it was applied in the calculation of soil moisture from organic surface layers at the Sodankylä and HOBE network sites to improve the quality in the so far gathered data.
Though the here-proposed calibration functions are derived based on samples collected in the higher northern latitudes, they should also be applicable to soil moisture measurements in similar media encountered in other regions of the world. If more data were collected in the future, a Decagon 5TE calibration law for mineral horizons as a function of SOM content could possibly be derived.
Financial support for conducting this research by the following institutions is gratefully acknowledged: European Space Agency (Support to Science Element – Changing Earth Science Network and Expert Support Laboratory); Centre National d'Etudes Spatiales (Terre, Océans, Surfaces Continentales, Atmosphère Programme, TOSCA), France; Villum Foundation (Hydrological Observatory, HOBE), Denmark; University of Copenhagen (Department of Geosciences and Resource Management), Denmark; Finnish Meteorological Institute; German Federal Ministry of Education and Research (Terrestrial Environmental Observatories, TERENO); Transnational Access to Research Infrastructures activity in the 7th Framework Programme of the EC under the ExpeER project. Edited by: C. Ménard