Time Domain Reflectometry Calibration Research Articles

Time and frequency domain reflectometry for the measurement of tree stem water content: A review, evaluation, and future perspectives

Electromagnetic (EM) methods for estimation of water content in porous media (e.g., soil and tree stems) are based on estimates of the media's bulk dielectric permittivity, which is sensitive to water content. Time domain reflectometry (TDR) and frequency domain reflectometry (FDR) are routinely used for rapid, automated and minimally destructive approach for routine measurement of soil water content (θsoil) and stem water content (θstem) in the laboratory and field. Applications and advances of TDR and FDR in soil science have been well-documented, but no attempt has been made to critically review the use of EM methods for measuring θstem. In this paper we review the principles, calibration, sensor configuration, and sensor installation of the TDR and FDR methods for the measurement of θstem. Perspectives are presented on new TDR calibration, the development of new sensors, and technologies for the simultaneous measurement of θstem, sap flow, bulk density/porosity, radial distribution of θstem, and electric conductivity. Also discussed were the effects of tree morphology and wounding on the accuracy of both methods. This review provides information for the novice and expert alike to guide them on the advantages, limitations, development, and application of the EM methods required to improve the reliability and validity of measured values of θstem.

Comparison of soil water content estimation equations using ground penetrating radar

Soil water content has an important impact on many fundamental biophysical processes. The quantification of soil water content is necessary for different applications, ranging from large-scale calibration of global-scale climate models to field and catchment scale monitoring in hydrology and agriculture. Many techniques are available today for measuring soil water content, ranging from point scale soil water content sensors to global scale, active and passive, microwave satellites. Geophysical methods are important methods, used for several decades, to measure soil water content at different scales. Among these methods, ground penetrating radar has been shown to be one of the most reliable and promising ones. Soil water content measurement using ground penetrating radar requires the application of parametric equations that will convert the measured dielectric permittivity to water content. While several studies have been performed to test equations for soil water content sensors such as time domain reflectometry, a few studies have been performed to test different formulae for application to ground penetrating radar. In this study, we compare available formulae for converting dielectric permittivity obtained from detailed laboratory scale measurement of reflected waves using ground penetrating radar. Four soils covering a wide range of textures were used and the measured soil water contents were compared with values obtained from gravimetric measurements. Results showed that the dielectric mixing model of Roth et al. (1990) provided the best fit for individual soil textural classes, except for sandy soils. However, for all data combined the dielectric mixing model performed much better with significant difference in coefficient and determination and root mean square error. Empirical equations developed from calibration of time domain reflectometry performed poorly when applied to estimation of soil water content obtained from ground penetrating radar. Differences in sample volume, frequency of operation and data analysis between ground penetrating radar and time domain reflectometry, suggest to use more flexible and robust electromagnetic mixing formulae, allowing for incorporating the dielectric properties of constituents materials and geometrical features of the media. Sensitivity analysis was then performed to provide detailed information for the most accurate application of the selected dielectric model. Sensitivity analysis showed that the geometric parameter α and the dielectric permittivity of the solid phase ∊s are the two most sensitive parameters, determining important variations in the estimation of soil water content. Based on these results, these two parameters are suggested as fitting parameters, to be selected if the model is fitted to data. Otherwise, the model can be successfully used without calibration, as presented in this study, by using α = 0.5 (as also suggested by the authors) and ∊s = 4, which is an average value for soil minerals.

Calibration, measurement, and characterization of soil moisture dynamics in a central Amazonian tropical forest

AbstractSoil moisture plays a key role in hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. Accurate soil moisture measurements in remote ecosystems such as the Amazon are difficult and limited because of logistical constraints. Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture and require calibration to convert the TDR's dielectric permittivity measurement (Ka) to volumetric water content (θv). In this study, our objectives were to develop a field‐based calibration of TDR sensors in an old‐growth upland forest in the central Amazon, to evaluate the performance of the calibration, and then to apply the calibration to determine the dynamics of soil moisture content within a 14.2‐m‐deep vertical soil profile. Depth‐specific TDR calibration using local soils in a controlled laboratory setting yielded a novel Ka–θv third‐degree polynomial calibration. The sensors were later installed to their specific calibration depth in a 14.2‐m pit. The widely used Ka–θv relationship (Topp model) underestimated the site‐specific θv by 22–42%, indicating significant error in the model when applied to these well‐structured, clay‐rich tropical forest soils. The calibrated wet‐ and dry‐season θv data showed a variety of depth and temporal variations highlighting the importance of soil textural differentiation, root uptake depths, as well as event to seasonal precipitation effects. Data such as these are greatly needed for improving our understanding of ecohydrological processes within tropical forests and for improving models of these systems in the face of changing environmental conditions.

Field Calibration of TDR to Assess the Soil Moisture of Drained Peatland Surface Layers

The proper monitoring of soil moisture content is important to understand water-related processes in peatland ecosystems. Time domain reflectometry (TDR) is a popular method used for soil moisture content measurements, the applicability of which is still challenging in field studies due to requirements regarding the calibration curve which converts the dielectric constant into the soil moisture content. The main objective of this study was to develop a general calibration equation for the TDR method based on simultaneous field measurements of the dielectric constant and gravimetric water content in the surface layers of degraded peatlands. Data were collected during field campaigns conducted temporarily between the years 2006 and 2016 at the drained peatland Kuwasy located in the north-east area of Poland. Based on the data analysis, a two-slopes linear calibration equation was developed as a general broken-line model (GBLM). A site-specific calibration model (SSM-D) for the TDR method was obtained in the form of a two-slopes equation describing the relationship between the soil moisture content and the dielectric constant and introducing the bioindices as covariates relating to plant species biodiversity and the state of the habitats. The root mean squared error for the GBLM and SSM-D models were equal, respectively, at 0.04 and 0.035 cm3 cm−3.

Experimental Determination of TDR Calibration Relationship for Pyroclastic Ashes of Campania (Italy).

Time domain reflectometry (TDR) is one of the most widely used techniques for indirect determination of soil volumetric water content (θ). TDR measures the relative dielectric constant (εr) which, in a three-phase system like the soil, depends on water, air, and solid matrix dielectric constants. Since dielectric constant of water is much larger than the other two, εr of bulk soil mainly depends on water content. In many cases, the application of TDR requires a specific calibration of the relationship θ(εr) to get quantitatively accurate estimates of soil water content. In fact, the relationship θ(εr) is influenced by various soil properties, such as clay content, organic matter content, bulk density, and aggregation. Numerous studies have shown that pyroclastic soils often exhibit a peculiar dielectric behavior. In Campania (Southern Italy) wide mountainous areas are covered by layered pyroclastic deposits of ashes (loamy sands) and pumices (sandy gravels), often involved in the triggering of landslides induced by rainwater infiltration. Reliable field measurements of water content of such soils are therefore important for the assessment of landslide risk. Hence, in this paper, the θ(εr) relationship has been experimentally determined on samples of typical pyroclastic soil of Campania, collected around Sarno, reconstituted with different porosities. The aim of the study is to identify specific calibration relationships for such soils based not only on empirical approaches. In this respect, a three-phase dielectric mixing model with a variable exponent is introduced, and the variable value of the exponent is related to the different dielectric properties of bond and free water within the soil pores.

Calibration of TDR Test Probes for Measuring Moisture Changes in the Construction Layers of the Railway Line

In the introduction of the paper there is characterized a way of monitoring the moisture in the structure of the railway substructure and its subgrade in the Experimental stand DRETM II, which is a part of the experimental workplace of the Department of Railway Engineering and Track Management. The substantial part of the paper is devoted to the characteristics and calibration of TDR (Time domain reflectometry) test probes for selected rock materials, which are incorporated into the construction of Experimental stand DRETM II (crushed aggregate fr.0/31.5mm, crushed aggregate fr.0/63mm and clay blended with river gravel), as a basic prerequisite for determination of the actual moisture and its changes in the body of the railway substructure and subgrade during the year. Finally, this article outlines a comparison of individual TDR probes used in calibration and their suitability for monitoring moisture in various materials used in construction layers of the Experimental stand DRETM II.

Evaluation of a Direct‐Coupled Time‐Domain Reflectometry for Determination of Soil Water Content and Bulk Electrical Conductivity

Core Ideas TDR‐315 sensors could be calibrated using conventional TDR methodologies. Soil water content calibrations using conventional TDR and TDR‐315 were comparable. Measured travel time using TDR‐315 was insensitive to soil electrical conductivity. Signal degradation in coaxial cables and interconnects is a long‐standing problem in the practical deployment of time‐domain reflectometry (TDR) for soil water monitoring. Acclima, Inc. has recently commercialized a TDR sensor (TDR‐315) with all electronics required for waveform acquisition embedded in the probe head. We calibrated ten TDR‐315 sensors and conventional TDR for apparent permittivity (Ka) and bulk electrical conductivity (σa) measurements. Also, soil water content calibrations were completed for a Pullman (fine, mixed, superactive, thermic torrertic Paleustolls) clay loam soil. Lastly, the sensitivity of Ka to σa was examined using a saturated solute displacement experiment with both probe technologies installed in a column packed with Pullman clay loam. A range of σa (0.65–2.8 dS m−1) was established by equilibrating the column with 0.25 dS m−1 CaCl2 and introducing a step pulse of 7.3 dS m−1 CaCl2. Permittivity calibrations of the TDR‐315 could be accomplished with conventional TDR methods and with similar sampling errors. Conventional calibrations of σa using long time amplitudes yielded a linear response for σa ≤ 3 dS m−1, above which the response was nonlinear. The fitted water content calibrations of the Pullman clay loam for the TDR‐315 were nearly indistinguishable from conventional TDR calibrations with similar root mean square errors (0.017–0.020 m3 m−3). Response of the two measurement technologies in a lossy soil during changing solution conductivities demonstrated that, in contrast to conventional TDR, travel time measured using acquired TDR‐315 waveforms was insensitive to σa up to 2.8 dS m−1.

General calibration of TDR to assess the moisture of tropical soils using artificial neural networks

Summary Determinations of soil moisture are important for various agricultural, environmental and hydrological applications, and accurate assessments are required. Artificial neural networks (ANNs) were applied in the present study to conduct general calibrations of time-domain reflectometry (TDR) probes using the physical characteristics of soil to estimate the moisture. The ANNs were trained and tested using data from five different soils. All of the combinations of physical properties, including the bulk density and sand, silt, clay and organic matter contents, were tested, and the inclusion of at least one of those network variables along with the apparent dielectric constant ( K a ), which was assessed using the TDR device, were sufficient to calibrate all five of the soils simultaneously. The ANN selected for the general calibration has a hidden layer with 13 neurons and tan-sigmoid-type transfer function. The analysis of the statistical indexes values indicates that the ANNs were slightly better than the third-order polynomial equations (Topp-like equations), which were specifically fitted to each soil. The tests were conducted to assess the performance of the general calibrations that were applied to estimate the moisture of the soils excluded from the training process, although the ANNs have such a potential; the most representative variables in descending order of importance were as follows: organic matter, sand, clay, and bulk density. The soil silt content failed to stand out in this analysis and showed a lower performance. Based on the results, the organic matter content was the preferred variable for use along with the K a in the ANNs applied to the general calibration of TDR (RMSE ranging from 0.0126 to 0.0237 g/g and r 2 ranging from 0.9083 to 0.9891). The sand content was also considered an advantageous variable because it was more easily assessed. The variables clay content and bulk density or the combination of several variables may also be used when available. As found by previous studies, the TDR calibration using ANN were better to sandy soils.

Estimating Soil Water Content from Permittivity for Different Mineralogies and Bulk Densities

Time domain reflectometry (TDR) exploits the difference in dielectric permittivity between the soil solid, gas, and liquid phases and has become increasingly popular for the determination of soil water content. The electromagnetic determination of water content in soils with high clay contents requires a good understanding of the main factors that affect the relationship between the dielectric permittivity of clay minerals and soil water content. The objectives of this study were to: (i) investigate the relationships among soil bulk density, clay content, clay mineralogy, and the soil dielectric permittivity, and (ii) evaluate the performance of the approach to correct the dissipation effects due to high clay contents. Samples were taken from five areas with different textures and bulk densities, both topsoils and subsoils. Soil physical properties, i.e., organic matter, clay, silt, and sand contents, and clay mineralogy were determined. High clay contents caused an underestimation of soil water content in the low moisture range and its overestimation in the high moisture range. Results also demonstrated that soils with similar clay contents but different mineralogies showed different permittivity values. For soil samples with equal water contents, soils with higher bulk density, or lower porosity, had a larger permittivity than soils with low bulk density and high porosity. This behavior was the result of the increase in the ratio of solid particle volume to air because the solid particles have a larger dielectric permittivity than air. Soil bulk density and clay content impact the accuracy and calibration of TDR. Smectites also cause signal attenuation and dispersion, resulting in smaller signal amplitude and longer rise time. In addition, smectites cause dispersion of the reflected signal, resulting in a longer rise time.

Evaluation of wood degradation for timber check dams using time domain reflectometry water content measurements

Wood is currently widely used in low environmental impact transverse structures for stream management and restoration. During the service life of these structures, chemical, physical, and biological processes can naturally degrade the wood fibres with a consequent increase of porosity. This typically causes wood moisture to increase with decay; however, conventional wood water content measurement instruments are calibrated for wood that has not been degraded.In this study, we analysed larch wood (Larix decidua Mill.) logs at different degradation levels by surveying their water content with the time domain reflectometry (TDR) technique. TDR has been extensively used to measure water content in soil; however, it has rarely been applied to wood materials. A laboratory analysis was first performed to calibrate the TDR measurements with those obtained with the gravimetric method. Three field campaigns were conducted in three different check dams built between 1950 and 2003 and located in the NW Italian Alps.The proposed TDR calibration curve allowed for the determination of the volumetric wood water content with satisfactory accuracy. This curve showed two distinct regression slopes: the first slope was related to volumetric water contents between 0.0 and 0.16m3m−3, whilst the second slope was related to water contents above 0.16m3m−3. The TDR in situ measurements showed water content values ranging from 0.08 to 0.54m3m−3 for the old timber structures and from 0.11 to 0.45m3m−3 for the newer structures. This agrees with the conservation state of the timber, which was influenced by the position of the logs in the structure during the life of the check dams. Finally, we found an operative way to relate the wood water content to the wood degradation level.

Calibration of time domain reflectometry (TDR) on the basis of torf sand and its optimisation for irrigation automations

In this study, requirement calibrations performed for irrigation automation at the agricultural irrigation, particularly by means of taking as a base the torf sand, from the greenhouse, garden and flowerpot soil, were carried out. In order to make the TDR (time domain reflectometry) device suitable for watering automation, the water pump was automatically used to perform drip irrigation according to the humidity values provided via integrated adjusted resistances and the relay system to the device. Compatibility was determined between the values obtained from automation. As a result, TDR humidity sensors, used for soil water content measurement, are both easier and cheaper than other systems. It has been seen that this examined system can be applied for different types of soils too. Key words: Time domain reflectometry (TDR), soil water content, torf, irrigation, automation.

Laboratory calibration of time domain reflectometry to determine moisture content in undisturbed peat samples

Time domain reflectometry (TDR), while widely used to measure volumetric water content (θ) and bulk electrical conductivity (BEC) in unsaturated granular soils, remains less studied in peat than mineral soils. Empirical models commonly used in mineral soils are not applicable to peat for accurate determination of θ from measured apparent dielectric permittivity (ɛ). Past studies for peat report highly variable calibrations, and suggest differences in origin of organic matter, degree of decomposition and bound water to explain such variability. This study shows that bound water appears to have minimal impact on calibration because of its negligible volumetric fraction at the low bulk densities of peat. Increased volumetric air fraction at the same θ values attributed to high porosity of peat makes the ɛ-θ relationships of mineral soils inapplicable. Temperature effects on ɛ resulted in a correction factor for θ. The temperature correction factor decreased with decreasing θ and was determined experimentally to lie between −0.0021 m3 m−3 per °C for θ≥ 0.79 m3 m−3 and −0.0005 m3 m−3 per °C for θ = 0.35 m3 m−3. The decreasing value of the correction factor with θ can be explained by dependence of the ɛ-θ relationship on properties of free water alone. Temperature dependence of BEC was close to that of soil solution. Maxwell-De Loor's four-phase mixing model (MDL) based on physical properties of the multiphase soil system can efficiently simulate the effect of increased air volume and varying soil temperature on the ɛ-θ relationship in peat. In addition, linear ɛ-θ calibration in peat can be improved when BEC is included in the calibration equation.

Using of Artificial Neural Networks for Evaluation Soil Water Content with Time Domain Reflectometry

Time Domain Reflectometry (TDR) has become an established method for soil volumetric water content ( ) measurement. TDR exploits the difference in dielectric constant values between the solid phase, air phase and liquid phase. In this paper, we study and evaluate the ability of empirical models to fit TDR calibration data for the soils of different textures, and adopt artificial neural network (ANN) to predict the Ka– relationship using soil physical parameters for ten different heavy texture soil types. The explanatory parameters that gave the most significant reduction in the root mean square error (RMSE) were dielectric constant, bulk density, clay content, silt content, sand content and organic matter content. The Ka– relationship for each soil type was predicted using the other nine soils for calibration purposes. To find the optimum model, various multilayer perceptron (MLP) topologies, having one hidden layer of neurons were investigated. In this analysis, Ka, bulk density and clay content were selected as input to ANN. The (3-10-1)-MLP, namely a network having 10 neurons in its hidden layer resulted in the best-suited model estimating the soil water content of the heavy texture soils at all soil types. For this topology, R2 and RMSE values were 0.998 and 0.00433, respectively. A comparative study among ANN models and various empirical models was also carried out. ANN models with RMSE and R2 of 0.0043-0.0134 (m3 m-3) and 0.923-0.998, respectively, gave better predictions than empirical models. The ANN model performed superior than both empirical and physical models. Since (3-10-1)-MLP outperformed regression models and it uses only one set of weights and biases for all soil types, it should be preferred over empirical and physical models.

Laboratory calibration of time-domain reflectometry and analysis of soil water content in Korea LTPP sections

The water content of pavement substructure can exert a great influence on the pavement performance. Recently, TDR (Time-Domain Reflectometry) probe has been used to measure the water content of pavement substructure in the field. The accuracy of TDR probe in the measurement of water content generally depends on the type, particle size, density, and temperature of the material, necessitating the need for the laboratory calibration of the TDR measurement. This study carried out the laboratory calibration of TDR probe (CS616) for water content of subbase and subgrade material in Korea LTPP (Long Term Pavement Performance) sections. Calibration equations for the TDR probe were then developed based on the laboratory tests, and verified by checking the water content of the soil obtained from the field. Additionally, from the analysis of two year field data in Korea LTPP, it was found that the TDR probe was capable of verifying the freezing, unfrozen water, and matric suction present in pavement structure.

Water Content Reflectometer Application to Construction Materials and its Relation to Time Domain Reflectometry

Moisture content measurements using time domain reflectometry (TDR) or water content reflectometry (WCR) are basic in many research areas. The goals of this study were to establish a relation between apparent dielectric number and WCR sensor output, to compare TDR and WCR calibration equations for two coarse road construction materials, and to investigate the influence of sampling volume for horizontally installed sensors. Measurements were performed in fluids of known dielectric number and in two incrementally saturated coarse materials. The effect of sampling volume was evaluated using an electrostatic finite‐element model. A two‐parameter equation was determined relating the apparent dielectric number to Campbell CS616 (Campbell Scientific, Logan, UT) WCR output (r2 > 0.99). A simple calibration can adapt the equation to individual CS616, or similar, sensors. For the finer, coarse material, a three‐phase mixing model proved best, while for the coarser material no equation adequately described the measurements. Numerical simulations indicated that limited capillary rise, creating a rapid transition from wet to dry close to saturation, was the explanation, warranting caution when interpreting measurements in nearly saturated coarse materials.

Impact of density on the hydraulic properties of peat and the time domain reflectometry (TDR) moisture calibration curve

The hydraulic properties of a peat used in a commercial peat biofilter were evaluated to determine their relationship with density and to establish a time domain reflectometry (TDR) calibration curve for water content as a function of the measured dielectric constant. The peat studied was a milled Sphagnum peat with a high organic content (99%). The dry densities evaluated in this study ranged from 90 to 180 kg/m3. The saturated hydraulic conductivity (Ks) decreased with an increase in dry density (ρdry) and was found to follow a log-linear relationship (Ks = 0.2462 exp(–0.0438ρdry), correlation coefficient R2 = 0.9789). As expected, the soil moisture curve was impacted by density, with a higher density resulting in higher water contents for a given suction. The data were fit to the van Genuchten relationship. A TDR calibration curve was generated at five different densities. A comparison of the curves indicates that the water content as a function of dielectric constant was not dependent on density because of the significantly larger dielectric constant (Ka) of water compared with those of peat solids and air-filled voids. The TDR calibration curve for the peat evaluated in this study (volumetric water content Θv = 0.2667 ln(Ka) – 0.1405, R2 = 0.9564) predicted higher water contents for a given dielectric constant compared with those from similar calibration curves for peat published in the literature. The data were compared with those from six other studies and indicated that the TDR calibration varied for different organic soils. The density-dependent hydraulic parameters and TDR calibration curve are important parameters needed to study the hydraulics of peat biofilters.Key words: peat, TDR, time domain reflectometry, density, hydraulics, soil moisture.

Density Compensation of TDR Calibration for Geotechnical Applications

Abstract Time Domain Reflectometry (TDR) technology has been widely used in areas such as agricultural science, soil science, and water resources for measuring soil volumetric water content and is beginning to be used in geotechnical engineering. Calibration relationships, which generally relate the soil volumetric water content to TDR measured dielectric constant, are critical for the accuracy of TDR measurements. For TDR technology to be useful for geotechnical engineering practice, the calibration relationship needs to be expressed in terms of gravimetric water content. Besides, geotechnical engineering deals with more compacted earth materials, where the effects of soil density on TDR calibration are significant. This paper introduces an improved TDR calibration relation that accounts for the density of soil skeleton while at the same time is simple in format. Application of this calibration relationship shows that it significantly reduces data scatter and achieves more accurate results than other existing calibration relationships.

A physical interpretation of logarithmic TDR calibration equations of volcanic soils and their solid fraction permittivity based on Lichtenecker's mixing formulae

A physical interpretation of both logarithmic Time Domain Reflectometry (TDR) calibration equations and empirical estimates of the solid fraction permittivity of volcanic soils is given in terms of the power-law mixing formulae ε eff α= ∑ i=1 N f iε i α . It is shown that, for most of the moisture range, the logarithmic Lichtenecker's law ( α=0) may hold in volcanic soils, while for coarse mineral soils a Birchak model ( α=1/2) may be universally valid. Two distinct logarithmic dielectric regimes dominated by free and bound water were identified for at least some soils. Such a transition from high to low water content may be predicted from the wilting point of the soil. At very low water content Lichtenecker's formula breaks down in volcanic soils, and a Birchak refractive index model results are more appropriate. The latter provides a generalization and physical interpretation of previous empirical estimates of the permittivity of the mineral fraction of soils, ε s, and it permits the estimation of ε s in volcanic soils from dry soil samples packed in air, where previous estimates (developed for mineral soils) failed to do so.

Note of clarification about: Field and laboratory calibration and test of TDR and capacitance techniques for indirect measurement of soil water content, by P.N.J. Lane and D.H. Mackenzie, Vol. 39, pp. 1371-1386

Time domain reflectometry (TDR) and a frequency domain sensor, the Didcot Capacitance Probe, were tested in the field and laboratory. The results from an undisturbed large core TDR laboratory test found the Topp equation returned a close correspondence to thermogravimetrically derived water content, although there was a slight underestimation. Coefficients of determination and efficiency were >0.98 and 0.92, respectively, for individual cores, and 0.98 and 0.97 for the whole data set. The field exercise revealed the Topp equation to be superior to the laboratory derived equation and other published empirical equations, suggesting the Topp equation to be adequate. A field test of the capacitance probe found poor correspondence between measured and predicted observations of profile point soil water content. Although 81% of the variance was explained by the calibration regression, there was a poor fit to the 1:1 line (E = 0.34), and a non-significant relationship between measured and predicted soil water content for the A horizon. The instrument design proved problematic for use as a determiner of point profile soil water content, and the recommended calibration procedure was impossible in the study site soils.

Calibration of Time Domain Reflectometry for Water Content in Peat Soil

Since peat soil differs from mineral soil in several respects, mineral-soil calibration functions for time domain reflectometry (TDR) are not necessarily applicable. This paper evaluates a number of calibration functions, both empirical polynomial and theoretical mixing models, on the basis of laboratory measurements on undisturbed Sphagnum peat samples. Deviations between different samples within this study indicate dissimilarities in dielectric properties between peats with different degrees of humification. Connections to physical properties such as amount of bound water and structural orientation are likely to exist. There is, however, a lack of methods to measure and quantify parameters expressing these properties. Therefore, until further studies on physical properties are accomplished, empirical or semi-empirical calibration curves are preferred. The best fit was obtained by an empirical, third order polynomial model. This model also gave a better fit than the mixing models when data were grouped into humification classes. However, all models reproduced pooled data with an r2 better than 0.93.