Currently several nondestructive, indirect methods for measurement of moisture (θ) in porous media have been developed. These methods include neutron thermalization (Greacen, 1981), time domain reflectometry [TDR (Topp et al., 1980)], electrical capacitance (Fares and Polyakov, 2006), and others (Bittelli, 2011). Time domain reflectometry is generally regarded as the most accurate electronic technique for determining substrate moisture content (Noborio et al., 1994; Robinson et al., 2003). Its method is based on the measurement of electric pulses travel time along a waveguide, which is directly related to substrate dielectric permittivity (ɛ). Water has a much greater value of dielectric permittivity (ɛ = 80) compared with air (ɛ = 1) or soil solids (ɛ = 3–7) and thus the measured permittivity is primarily a function of porous media moisture content (Seyfried and Murdock, 2001).
However, the high cost of TDR and difficulties associated with the required waveform analysis have led to the development of alternative dielectric sensors that also use substrate dielectric properties to determine moisture content (Seyfried and Murdock, 2004). Capacitance and frequency techniques offer an excellent alternative to TDR due to their lower cost, capacity for continuous monitoring and data logging, repeatability, and applicability to a wide range of porous media (Dean et al., 1987; Kargas et al., 2011; Kargas and Soulis, 2012). Most of the reports that describe TDR method and other dielectric sensors are field studies and only recently have researchers explored their use in soilless and coarse-textured substrates (Morel and Michel, 2004; Nemali et al., 2007; Scoggins and van Iersel, 2006; van Iersel et al., 2011). In the case of TDR, it has been shown that permittivity can be related to porous media moisture content with reasonable accuracy for a wide variety of soils using a single calibration equation developed by Topp et al. (1980). Alternatives to empirically derived calibrations are often based on dielectric mixing models (Roth et al., 1990). Ferre and Topp (2000) reviewed the application of mixing models for moisture content determination and showed that a “square root” mixing model is equivalent to a linear relationship between square root of permittivity and moisture content.
In general, a linear relationship between moisture content and square root of permittivity has been observed in several studies for inorganic soils using commercial sensors (Kargas et al., 2011; Seyfried and Murdock, 2004). In contrast to inorganic soils, the use of dielectric sensors in organic media has revealed that calibration parameters can vary considerably, which is not surprising considering the wide range of these substrate properties. Thus, dielectric sensors generally require substrate-specific calibrations due to the different dielectric properties of the substrates that affect sensor performance (van Iersel et al., 2011; Yoshikawa et al., 2004). This has led researchers to investigate and determine the most suitable calibration equations for each sensor and substrate type (Morel and Michel, 2004).
Substrate moisture content is the major factor affecting the dielectric permittivity of a soilless substrate. However, other substrate-related factors such as EC, temperature, and bulk density can cause dielectric loss and affect the permittivity of a substrate (Nemali et al., 2007). Robinson et al. (1999) reported that TDR overestimated water content in saline soils due to the overestimation of dielectric permittivity. The newly developed sensors received little or no attention regarding salinity effect on the estimation of permittivity, since it was implicitly assumed that these dielectric devices could perform more or less in a similar manner compared with TDR. Thus, substrate-specific calibrations are recommended by most dielectric sensor manufacturers to achieve maximum accuracy of substrate moisture content determination. Although in situ calibration has been attempted, dielectric sensor calibration is mostly performed in laboratory conditions due to the much easier calibration of sensors for a wide range of moisture content. There are two different calibration approaches. The first one uses soil samples in cells with various predetermined moisture regimes, which are assumed uniform throughout the volume of the cells, and a relationship between permittivity and actual moisture content (θm) is obtained. This approach could not guarantee a constant bulk density, it is time consuming, and it can give limited ɛ − θm data points (Seyfried et al., 2005). The second approach is the upward infiltration method proposed by Young et al. (1997), which is relatively rapid and provides more data within the full range of the actual substrate moisture content.
Two moisture sensors have recently become available: 1) the frequency domain dielectric sensor (WET-2 sensor; Delta-T Devices, Cambridge, UK) and 2) the time domain dielectric sensor (TDR300; Spectrum Technologies, Plainfield, IL). Due to their reduced rod length, both frequency domain (6.8 cm rod length) and time domain sensors (rod length varying from 3.8–20 cm) are appropriate for determining moisture content in shallow green roof substrates.
To date, both sensors have not been fully investigated for their capacity to accurately determine moisture content in coarse-textured substrates such as those used for green roofs. Green roof substrates comprised coarse-textured substances to fulfill several criteria such as to maintain adequate moisture for plant growth, to facilitate the quick removal of excess water, to provide support and anchoring of the plants, to provide nutrients, and to possess a pH and EC appropriate for plant growth [Forschungsgesellschaft Landschaftsentwicklung Landschaftsbau (FLL), 2008; Getter and Rowe, 2006]. In addition to the above-mentioned criteria, green roof substrates must have reduced weight, which could be accomplished both by the use of lightweight materials and by substrate depth reduction. Thus, several lightweight materials have been used as green roof substrate constituents including pumice (Nektarios et al., 2011); expanded shale, slate, and clay (Beattie and Berghage, 2004; Rowe et al., 2006); perlite (Kotsiris et al., 2012); and others. These materials are coarse textured with most particles having a diameter range from 0.5 to 16 mm to comply with FLL guidelines (2008). Despite having common coarse-textured characteristics, these materials have different water absorbance and release capacity based on whether they possess or lack internal porosity as well as on the type of internal porosity.
Even though the formulaic concept of extensive green roofs involves irrigation only during plant establishment (FLL, 2008), there is a new trend concerning an “adaptive approach” that aims to use extensive green roof advantages in Mediterranean and other semiarid climates of the globe (Kotsiris et al., 2012; Ntoulas et al., 2012). In the adaptive approach, carefully planned and controlled irrigation is necessary to permit plant growth when the harsh climatic conditions of semiarid regions are combined with shallow extensive green roof substrates. As a result, the accurate moisture content measurement is significant to determine timely application of irrigation and drainage capacity of green roof systems.
Thus, the main objective was to develop calibration equations of frequency and time domain dielectric sensors for determining moisture content in green roof substrates. More specifically, the goals were 1) to evaluate frequency domain sensor performance in measuring moisture content in different coarse-textured green roof substrates using manufacturer’s calibration equations and to examine the reliability of the two-point specific calibration equation; 2) to determine time domain sensor response under increasing green roof substrates moisture content due to the lack of calibration provided by the manufacturer for these types of porous substrates; 3) to determine whether time domain and frequency sensor specific calibrations varied with different green roof substrates; 4) to investigate the effect of EC on both sensor outputs. The result from such a detailed evaluation of these two sensors is to assure their improved use in future irrigation programs monitoring of coarse-textured porous substrates.
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