Evaluation of a low cost soil water cont

() Evaluation of a low cost soil water content sensor for wireless network applications H R Bogena *, J A Huisman, C Oberdörster, H Vereecken Research Centre Jülich, Agrosphere Institute, ICG 4, 524[.]

Journal of Hydrology (2007) 344, 32– 42 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/jhydrol Evaluation of a low-cost soil water content sensor for wireless network applications ărster, H Vereecken H.R Bogena *, J.A Huisman, C Oberdo ălich, Agrosphere Institute, ICG 4, 52425 Ju ălich, Germany Research Centre Ju Received February 2007; received in revised form 29 May 2007; accepted 15 June 2007 KEYWORDS Soil water content sensor; EM sensor characterization method; Wireless sensor network Wireless sensor networks are a promising new in situ measurement technology for monitoring soil water content changes with a high spatial and temporal resolution for large areas However, to realise sensor networks at the small basin scale (e.g 500 sensors for an area of 25 ha), the costs for a single sensor have to be minimised Furthermore, the sensor technique should be robust and operate with a low energy consumption to achieve a long operation time of the network This paper evaluates a low-cost soil water content sensor (ECH2O probe model EC-5, Decagon Devices Inc., Pullman, WA) using laboratory as well as field experiments The field experiment features a comparison of water content measurements of a forest soil at cm depth using TDR and EC-5 sensors The laboratory experiment is based on a standardized sensor characterisation methodology, which uses liquid standards with a known dielectric permittivity The results of the laboratory experiment showed that the EC-5 sensor has good output voltage sensitivity below a permittivity of 40, but is less sensitive when permittivity is higher The experiments also revealed a distinct dependence of the sensor reading on the applied supply voltage Therefore, a function was obtained that allows the permittivity to be determined from the sensor reading and the supply voltage Due to the higher frequency of the EC-5 sensor, conductivity effects were less pronounced compared to the older EC-20 sensor (also Decagon Devices Inc.) However, the EC-5 sensor reading was significantly influenced by temperature changes The field experiment showed distinct differences between TDR and EC-5 measurements that could be explained to a large degree with the correction functions derived from the laboratory measurements Remaining errors are possibly due to soil variability and discrepancies between measurement volume and installation depth Overall, we conclude that the EC-5 sensor is suitable for wireless network applications However, the results of this paper also suggest that temperature and electric conductivity effects on the sensor reading have to be compensated using appropriate correction functions ª 2007 Elsevier B.V All rights reserved Summary * Corresponding author Tel.: +49 (0)2461 616752; fax: +49 (0)2461 612518 E-mail addresses: h.bogena@fz-juelich.de (H.R Bogena), s.huisman@fz-juelich.de (J.A Huisman), c.oberdoerster@fz-juelich.de (C Oberdo ărster), h.vereecken@fz-juelich.de (H Vereecken) 0022-1694/$ - see front matter ª 2007 Elsevier B.V All rights reserved doi:10.1016/j.jhydrol.2007.06.032 Evaluation of a low-cost soil water content sensor for wireless network applications Introduction A remaining challenge in hydrology is to explain the observed patterns of hydrological behaviour over multiple space-time scales as a result of interacting environmental factors The large spatial and temporal variability of soil water content is determined by factors like atmospheric forcing, topography, soil properties and vegetation, which interact in a complex nonlinear way (e.g Grayson et al., 1997; Western et al., 2004) For the determination of the spatiotemporal structure of hydrological state variables, an enormous measuring effort is necessary In the case of soil water content remote sensing, technologies like passive or active radiometry can avoid direct measurements by providing area-wide indications of surface soil water content (e.g Wigneron et al., 2003; Lo ăw et al., 2006) However, the received signal is strongly influenced by the vegetation and surface structure, and the sampling depth is restricted to the uppermost soil (2–5 cm) (Walker et al., 2004) Consequently, direct measurements are still indispensable in areas with significant vegetation and litter cover In recent years, ground penetrating radar methods have been developed that allow mapping of the spatial variability of soil water content profiles (e.g Huisman et al., 2001, 2002; Lambot et al., 2006) However, these methods are not easily amenable to automation Clearly, there is still a need for measurement techniques that can assess large scale three-dimensional soil moisture fields with extremely high temporal and spatial resolution (Schulz et al., 2006) A promising new technology for environmental monitoring is the wireless sensor network (Cardell-Oliver et al., 2005) Environmental sensor networks will play an important role in the emerging terrestrial environmental observatories (Bogena et al., 2006), since they bridge the gap between local (e.g lysimeter) and regional scale measurements (e.g remote sensing) A wireless network may consist of hundreds of water content sensors that can transmit information to a main server with wireless communication technology There are several factors that have to be considered when selecting a sensor for network applications In order to maximise the lifetime of such a sensor network, the sensors have to be very economic concerning energy demand and should be reasonably robust Because of the multitude of soil water content measurements within the sensor network, the interpretation of the sensor signal has to be straightforward and unambiguous Last but not least, in order to maximise the number of sensor nodes, the soil water content sensors have to be as inexpensive as possible Since capacitance sensors are relatively inexpensive and easy to operate, they seem to be a promising choice for soil water content measurements with sensor networks The aim of this paper is to evaluate a low-cost capacitance sensor (ECH2O probe, model EC-5, Decagon Devices Inc., Pullman, WA) using laboratory as well as field experiments We chose to use a two-step calibration procedure in the laboratory experiments In the first step, the sensor reading is related to permittivity using the standardized sensor characterization methodology proposed by Jones et al (2005) In the second step, permittivity is related to soil water content Such a two-step procedure permits a more physically based calibration procedure (Kelleners et al., 2004) Furthermore, knowledge of the sensor read- 33 ing–permittivity relationship enables a more direct comparison with other electromagnetic sensing systems, e.g other capacitance sensor designs, passive and active radiometry, etc Sensor evaluations that directly relate the sensor reading to the soil water content are more ambiguous and difficult to compare between sensors because of the uncertainty and systematic deviations introduced by the variable permittivity–soil water content relationships for different soil types The field experiment features a comparison of permittivity and water content measurements in a forest soil at cm depth using TDR and EC-5 sensors Theory Sensor technology The capacitance and time domain reflectometry (TDR) method are two widely used electromagnetic (EM) techniques for soil water content estimation (Blonquist et al., 2005) Both methods make use of the strong dependence of EM signal properties on volumetric water content that stems from the high permittivity of water (ew  80) compared to mineral soil solids (es  2–9), and air (ea = 1) The capacitance method is already known for a long time (Dean et al., 1987) One of the first workers to use a high frequency capacitance technique for soil water content determination was Thomas (1966) The basic principle of the capacitance method is to incorporate a dielectric medium (e.g soil) as part of the dielectric of the sensor capacitor The equivalent circuit diagram of the ECH2O probe is illustrated in Fig The ECH2O sensor circuitry measures the dielectric permittivity of the material surrounding a thin, fiberglass enclosed probe The circuit board includes an electronic oscillator that generates a repetitive square waveform with a characteristic frequency (e.g EC-5: 70 MHz) The total sensor capacitance is then made up of the capacitance of medium C and the capacitance Cs due to stray electric fields (Kelleners et al., 2004) The soil permittivity is determined by measuring the charge time from a starting voltage, Vi, to a voltage V, with an applied voltage, Vf of a capacitor which uses the soil as a dielectric If the resistance R, Vf and Vi are held constant, then the charge time of the capacitor, t, is related to the capacitance according to   V  Vf ỵ Vi t ẳ RC ln ð1Þ Vi  Vf The capacitance is a function of the dielectric permittivity (e) of the medium and a geometrical factor g and can be calculated by C ¼ ge ð2Þ The factor g is associated with the electrode configuration and the shape of the electromagnetic field penetrating the medium By assuming that the charge time of the capacitor is a linear function of the dielectric permittivity of the surrounding medium the dielectric permittivity e can be calculated as follows:    1 V  Vf þ Vi ¼ Rg ln Vi  Vf e t ð3Þ 34 H.R Bogena et al RMS-value converter Electronic oscillator R RMS Cs Vinp Circuit board C G Electrodes and medium Vout Sensor reading Figure Equivalent circuit diagram of a capacitance sensor where R is a resistor, C is the capacitance of the medium, Cs is the stray capacitance, G is the energy loss due to relaxation and ionic conductivity and Vinp and Vout are the supply and sensor reading voltage, respectively A typical capacitance probe determines V at a high frequency (between 10 and 100 MHz) for a specific pulse length Dt (see Fig 2) The course of the charging curve depends on the dielectric permittivity and thus on the soil water content At a high soil water content, the capacitor will charge slower and, therefore, the charge curve will be flatter than at a low soil water content This implies that a capacitor filled with wet soil will reach a given threshold voltage (Vt) later than a capacitor filled with dry soil (see Fig 2) The sensor output is directly related to the average voltage over the period Dt Consequently, a high soil water content will result in a high output voltage of the sensor This basic principle is utilized in all ECH2O soil water content sensors used in this study To transform the alternating current into direct current, the ECH2O sensor uses a RMS-value converter The resultant output voltage is then related to permittivity or directly to soil water content by fitting regression curves Sensor response to temperature variation Soil water content sensors may be affected by temperature variations through effects on the dielectric permittivity of water, through effects on soil–water interactions, as well as through direct effects on the sensor circuitry The dielectric permittivity of pure water ew decreases with increasing temperature According to the semi-empirical model for the dielectric permittivity of water by Meissner and Wentz (2004), a negative temperature trend of 0.35 K1 for frequencies 0.5 dS m1 using the EC-20 sensor Therefore, the EC-5 sensor was used to evaluate to which extent the 70 MHz technology of the EC-5 sensor improves the conductivity sensitivity Fig shows that the EC-5 sensor underestimates the actual soil water content in the presence of significant bulk electrical conductivity The maximum underestimation is vol.% at a conductivity of 0.8 dS m1 Interestingly, at a conductivity of 2.5 dS m1 the underestimation switches to an overestimation of soil water content The EC-20 sensor already provided unrealistic soil water content measurements at a bulk electrical conductivity of 0.5 dS m1 To put these numbers in a hydrological context, a saturated sandy soil may have a bulk electrical conductivity of 0.1 dS m1, which results in a soil water content underestimation of 1.3 vol.% A loamy soil will typically have a higher bulk electrical conductivity For example, a typical bulk electrical conductivity of 0.5 dS m1 results in a soil water content underestimation of 4.5 vol.% Even higher bulk electrical conductivities of dS m1 are typically observed in 2:1 clays, which results in the maximum soil water content underestimation of 5.4 vol.% Overall, it can be concluded that the EC-5 sensor is much less sensitive to bulk electrical conductivity than the older EC-20 sensor It should be noted that the numbers provided above are valid for a reference permittivity of 40, which corresponds with an equivalent soil water content of 51 vol.% Since the actual voltage differences associated with the permittivity and equivalent soil water content estimates in Fig were relatively small, it is expected that the influence of electrical conductivity is even less for lower soil water content To verify whether temperature and conductivity correction functions are feasible for adjusting the field data, preliminary temperature and electric conductivity correction functions (third- and fourth-order polynomial equations) were fitted through the EC-5 measurements shown in Figs and 7: ect ẳ 0:0002217T  0:01442T ỵ 0:1175T ỵ 1:6403 RMSE ẳ 0:1220 11ị ecc ẳ 0:4180r þ 4:5804r  18:335r þ 23:393r  0:0516 RMSE ¼ 0:0722 ð12Þ 40 H.R Bogena et al 10 15 Sensor Θ at dS/m - Sensor Θ Sensor ε a t dS/m - Sensor ε 20 10 -5 EC-5 (3.0 V) EC-5 (5.0 V) Conductivity correction model ε , 3.0 V -10 -15 -2 -4 EC-5 (3.0 V) EC-5 (5.0 V) Conductivity correction model Θ , 3.0 V -6 -8 -10 -20 0.5 1.5 Conductivity [dS/m] 2.5 0.5 1.5 Conductivity [dS/m] 2.5 Figure Deviations of the permittivity (left) and the equivalent soil water content predictions (right) of a 0.6 i-C3E1–water solution using EC-5 sensors with two supply voltages, and the conductivity correction model of Eq (12) where ect and ecc are temperature and electric conductivity correction factors, T is the soil temperature in C and r is the soil bulk electrical conductivity in dS m1 The corrected dielectric permittivity ec is then calculated as follows: ec ẳ e ỵ ect ỵ ecc 13ị It has to be noted that Eqs (11) and (12) are only preliminary correction functions since more measurements for a larger range of reference permittivity; temperature and electric conductivity have to be collected and to be used to develop more universal functions that can be used for correcting the EC-5 measurements Field experiment Fig presents the dielectric permittivity and soil water content time series hourly measured with TDR and the EC5 sensors, and the topsoil temperature and precipitation during the period from June to 31 December 2006 The permittivity measured with the EC-5 sensor was calculated with the appropriate SRP model derived earlier The corresponding soil water content was calculated using Eq (5) for both the EC-5 sensor and the TDR probe Although Eq (5) might not be the most appropriate permittivity–soil water content relationship for the loamy silt soil, the comparison between the two sensors is unaffected by this choice It can be seen in Fig that the mean of the EC-5 and the TDR sensors show similar soil water content dynamics in response to precipitation However, there is a systematic deviation between both sensor types TDR resulted in a mean soil water content of 31.0 vol.%, whereas the EC-5 sensors only measured 26.6 vol.% There are three possible explanations for the lower soil water content measured with the EC-5 sensor First, the electrical conductivity of the investigated soil is moderate with an average bulk conductivity of 0.06 dS m1, as measured with TDR Fig shows that this mean bulk electrical conductivity will result in an underestimation of 0.8 vol.% for e = 40 Due to the nonlinearity of Eq (4), the correction will be higher for lower soil water contents Second, there might be a temperature ef- fect on either the soil dielectric permittivity or the sensor electronics Since the maximum topsoil temperature was 19.76 C the temperature effect on the sensor electronics will lead to underestimation of the soil water content (see Fig 6) The temperature effect on the soil dielectric permittivity has not been yet investigated at this experimental site, and the impact of these changes can therefore not be investigated in this study Preliminary investigations with correction functions for bulk electrical conductivity and temperature using Eqs (11) and (12) showed that the systematic deviations can be reduced (see Fig 8c) The corrected sensor signal using all installed EC-5 sensors led to a mean soil water content of 29.5 vol.%, which is only 0.6 vol.% lower than the mean of the TDR sensors Furthermore, the Nash-Sutcliffe efficiency coefficient (Nash and Sutcliffe, 1970) increased from 0.23 to 0.74 indicating that the correction functions were able to improve the EC-5 measurements However, it has to be noted that given the complex relation between voltage reading and permittivity, it seems likely that the correction functions will vary with dielectric permittivity Therefore, the results achieved with Eqs (11) and (12) are only a first indication that correction functions are able to improve soil water content measurement with capacitance sensors A further explanation for the observed differences is soil variability Although we considered the mean of two TDR probes and four EC-5 probes within a relatively small soil volume, this is no guarantee that all variability is captured by the mean values The significance of vegetation patterns and soil variability at the test site is indicated by significant standard deviations of the measured soil water content by four EC-5 sensors (see Fig 8) Also, the two TDR sensors showed distinct deviations in the measured soil water content (mean: 2.2 vol.%, max.: 7.9 vol.%) Finally, there is a sampling depth issue because the TDR probes were installed horizontally at cm depth and the EC-5 sensors measure the soil water content of the upper 3.5 cm Clearly, a dryer upper soil could also partly explain the observed deviations These sampling volume and soil heterogeneity issues, which are typical for field evaluations of soil water content sensors, motivated the development of the standardized sensor 20 20 10 10 —1 a b 0.5 θ [cm3 cm—3] 06/01/06 0.4 T [°C] 41 P [mm h ] Evaluation of a low-cost soil water content sensor for wireless network applications 07/01/06 07/31/06 08/31/06 09/30/06 10/31/06 11/30/06 12/31/06 07/01/06 07/31/06 08/31/06 09/30/06 10/31/06 11/30/06 12/31/06 0.3 0.2 c 0.5 θ [cm3 cm—3] 0.1 06/01/06 0.4 0.3 EC5 mean TDR TDR EC5 std 0.2 0.1 06/01/06 07/01/06 07/31/06 08/31/06 09/30/06 10/31/06 11/30/06 12/31/06 date Figure Rainfall intensities and temperatures for the observed period (a), as well as the volumetric water content obtained from both TDR sensors and mean values and standard deviations of all EC-5 sensors (b), respectively The EC-5 permittivities were corrected using Eq (13) (c) testing methodologies (Blonquist et al., 2005; Jones et al., 2005) Conclusions and outlook The laboratory and field evaluation of the EC-5 sensor have led us to the following conclusions: • The standardized EM sensor characterization method proposed by Jones et al (2005) leads to reproducible results Therefore, it is recommended that future sensor evaluations also adhere to this characterization method • The sensor sensitivity test showed that the sensitivity of the sensor reading to soil water content decreased strongly with increasing soil water content and to some extent with decreasing supply voltage Models that relate the sensor reading to soil permittivity for a given supply voltage were successfully derived In addition, a model that relates the sensor reading and the supply voltage to soil permittivity was also derived successfully • The electronics of the EC-5 sensor are sensitive to temperature variations A maximum error of 1.8 vol.% occurred at a soil temperature of 40 C for a medium with a permittivity of 40, which corresponds to an equivalent soil water content of 51 vol.% • The EC-5 sensor is sensitive to bulk electrical conductivity, although to a lesser extent than the older EC-20 sensor The maximum error of vol.% occurred at a bulk electrical conductivity of dS m1 for a permittivity of 40 (equivalent water content of 51 vol.%) • The field evaluation showed distinct differences between TDR and EC-5 measurements that could be explained to a large degree with the correction functions derived from the laboratory measurements Remaining errors are possibly due to soil variability and discrepancies between measurement volume and installation depth Overall, we conclude that the EC-5 sensor is suitable for wireless network applications However, the results of this paper also suggest that temperature and electric conductivity effects on the sensor reading have to be compensated using appropriate correction functions The use of temperature and conductivity correction functions requires the continuous measurement of these properties This could be realized using the ECH2O TE sensor (Decagon Devices Inc.), which uses the same technology for measuring soil water content as the EC-5 sensor Therefore, future work will focus on the evaluation of this sensor We will also evaluate temperature and conductivity effects in more detail to derive more general correction functions 42 Acknowledgements We gratefully acknowledge financial support by the SFB/TR 32 ‘‘Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation’’ funded by the Deutsche Forschungsgemeinschaft (DFG) We are especially indebted to A Weuthen for technical support References Blonquist Jr., J.M., Jones, S.B., Robinson, D.A., 2005 Standardizing characterization of electromagnetic water content sensors Part Evaluation of seven sensing systems Vadose Zone J 4, 1059 1069 Bogena, H., Huisman, J.A., Oberdo ărster, C., Vereecken, H., 2007 Characterization of ECH2O capacitance sensors for measuring soil water content Unpublished Technical Report, Forschungszentrum Ju ălich Bogena, H., Schulz, K., Vereecken, H., 2006 Towards a Network of Observatories in Terrestrial Environmental Research Advances in Geosciences 9, 109–114 (available at www.adv-geosci.net/9/ 109/2006/) (verified May 29, 2007) Cardell-Oliver, R., Smettem, K., Kranz, M., Mayer, K., 2005 A reactive soil moisture sensor network: design and field evaluation Inter J Distribut Sensor Networks 12, 149–162 Czarnomski, N.M., Moore, G.W., Pypker, T.G., Licata, J., Bond, B.J., 2005 Precision and accuracy of three alternative instruments for measuring soil water content in two forest soils of the Pacific Northwest Can J For Res 35, 1867–1876 Dean, T.J., Bell, J.P., Baty, A.J.B., 1987 Soil moisture measurement by an improved capacitance technique, Part I Sensor design and performance J Hydrol 93, 67–78 Decagon Devices Inc 2006 ECH2O Soil Moisture Sensor Operator’s Manual for Models EC-20, EC-10, and EC-5, Version 2.2., Decagon Devices Inc., Pullman, WA (available at http://www.decagon.com/manuals/echomanual.pdf) (verified May 29, 2007) Evett, S.R., Tolk, J.A., Howell, T.A., 2006 Sensors for soil profile moisture measurement: accuracy, axial response, calibration, precision and temperature dependence Vadose Zone J 5, 894– 907 Grayson, R.B., Western, A.W., Chiew, F.H.S., Blo ăschl, G., 1997 Preferred states in spatial soil moisture patterns: local and nonlocal controls Water Resour Res 33 (12), 2897–2908 Hilhorst, M.A 1998 Dielectric characterisation of soil Ph.D Thesis, Wageningen Agricultural University, Wageningen, The Netherlands, 141 p Hook, W.R., Ferre ´, T.P.A., Livingston, N.J., 2004 The effects of salinity on the accuracy and uncertainty of water content measurement Soil Sci Soc Am J 68, 47–56 Huisman, J.A., Sperl, C., Bouten, W., Verstraten, J.M., 2001 Soil water content measurements at different scales: accuracy of time domain reflectometry and ground-penetrating radar J Hydrol 245 (1), 48–58 Huisman, J.A., Snepvangers, J.J.J.C., Bouten, W., Heuvelink, G.B.M., 2002 Mapping spatial variation in surface soil water content: comparison of ground-penetrating radar and time domain reflectometry J Hydrol 269 (3), 194–207 Jones, S.B., Blonquist Jr., J.M., Robinson, D.A., Rasmussen, V.P., Or, D., 2005 Standardizing characterization of electromagnetic water content sensors Part Methodology Vadose Zone J 4, 1048–1058 Kaatze, U., Kettler, M., Pottel, R., 1996 Dielectric relaxation spectrometry of mixtures of water with isopropoxy- and isobu- H.R Bogena et al toxyetanol Comparison to unbranched polyethylene glycol monoalkyl ethers J Phys Chem 100, 2360–2366 Kelleners, T.J., Soppe, R.W.O., Robinson, D.A., Shape, M.G., Ayars, J.E., Skaggs, T.H., 2004 Calibration of capacitance probe sensors using electric circuit theory Soil Sci Soc Am J 68, 430439 Lambot, S., Weihermu ăller, L., Huisman, J.A., Vereecken, H., Vanclooster, M., Slob, E.C., 2006 Analysis of air-launched ground-penetrating radar techniques to measure the soil surface water content Water Resour Res 42, W11403 doi:10.1029/ 2006WR005097 Lo ăw, A., Ludwig, R., Mauser, W., 2006 Derivation of surface soil moisture from ENVISAT ASAR wide swath and image mode data in agriculturalareas.IEEE Trans.Geosci.Remote Sens 444, 889–899 McMichael, B., Lascano, R.J., 2003 Laboratory evaluation of a commercial dielectric soil water sensor Vadose Zone J 2, 650– 654 Meissner, Th., Wentz, F.J., 2004 The complex dielectric constant of pure and sea water from microwave satellite observations IEEE Trans Geosci Remote Sens 42, 1836–1849 Nash, J.E., Sutcliffe, J.V., 1970 River flow forecasting through conceptual models Part I A discussion of principles J Hydrol 103, 282–290 Or, D., Wraith, J.M., 1999 Temperature effects on soil bulk dielectric permittivity measured by time domain reflectometry: a physical model Water Resour Res 35, 371–383 Pepin, S., Livingston, J.J., Hook, W.T., 1995 Temperature-dependent measurement errors in time domain reflectometry determinations of soil water Soil Sci Soc Am J 59, 38–43 Robinson, D.A., Jones, S.B., Wraith, J.M., Or, D., Friedman, S.P., 2003 A review of advances in dielectric and electrical conductivity measurement in soils using time domain reflectometry Vadose Zone J 2, 444–475 Schwank, M., Green, T.R., Ma ătzler, C., Benedickter, H., Flu ăhler, H., 2006a Laboratory characterization of a commercial capacitance sensor for estimating permittivity and inferring soil water content Vadose Zone J 5, 1048–1064 Schulz, K., Seppelt, R., Zehe, E., Vogel, S., Attinger, H.J., 2006 Importance of spatial structures in advancing hydrological sciences Water Resour Res 42, W03S03 doi:10.1029/ 2005WR004301 Sun, Z.J., Young, G.D., McFarlane, R.A., Chambers, B.M., 2000 The effect of soil electrical conductivity on moisture determination using time-domain reflectometry in sandy soil Can J Soil Sci 80, 13–22 Thomas, A.M., 1966 In situ measurement of moisture in soil and similar substances by ‘fringe’ capacitance J Sci Instrum 43, 21–27 Topp, G.C., Davis, J.L., Annan, A.P., 1980 Electromagnetic determination of soil water content: measurements in coaxial transmission lines Water Resour Res 16, 574–582 Walker, J.P., Houser, P.R., Willgoose, G.R., 2004 Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS-2 Hydrol Proc 1811, 1975–1997 Western, A.W., Zhou, S.-L., Grayson, R.B., McMahon, T.A., Blo ăschl, G., Wilson, D.J., 2004 Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes J Hydrol 286, 113–134 Wigneron, J.-P., Calvet, J.-C., Pellarin, T., Van de Griend, A.A., Berger, M., Ferrazzoli, P., 2003 Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans Rem Sens Environ 854, 489–506 Wraith, J.M., Or, D., 1999 Temperature effects on soil bulk dielectric permittivity measured by time domain reflectometry: experimental evidence and hypothesis development Water Resour Res 35 (2), 361–369  ... permittivity and thus on the soil water content At a high soil water content, the capacitor will charge slower and, therefore, the charge curve will be flatter than at a low soil water content This... recent years, ground penetrating radar methods have been developed that allow mapping of the spatial variability of soil water content profiles (e.g Huisman et al., 2001, 2002; Lambot et al., 2006)... space-time scales as a result of interacting environmental factors The large spatial and temporal variability of soil water content is determined by factors like atmospheric forcing, topography,