## 4 Wireless Soil Moisture Sensing Applications  
### Applications in Hydrological Research  
WSNs have already been successfully deployed in various locations around the world, providing a large number of soil moisture measurements (see references in Table 2). For instance, the SoilNet WSN installed in a headwater catchment has continuously operated since 2009 and has already produced more than 50 million soil moisture measurements [80]. Most WSN studies of soil moisture were focused on the investigation of hydrological processes at the field and catchment scale [14; 16; 66; 80]. For instance, [PERSON] et al. [14] analyzed data from a SoilNet WSN implemented in a headwater catchment and found that soil moisture variability in forested mountainous regions can be remarkably high during intermediate wetness conditions. Similar results were found by other studies in mountainous regions using campaign-style soil moisture measurements with handheld sensors [31; 89]. However, the SoilNet data showed less scatter than such discontinuous soil moisture measurements, indicating that WSN data provide more detailed insights into the hydrological processes generating soil moisture patterns [66; 90; 91]. [PERSON] et al. [14] also \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Name** & **Sensor Type** & **Manufacturer** & **WSN Applications** \\ \hline EC-5/EC-TM & Capacitance & METER Group & [14; 32; 65; 66; 67; 68] \\ ThetaProbe ML2x & Capacitance & Delta-T Devices & [69] \\ GS3/TEROS11 & Capacitance & METER Group & [70] \\ TDR probes & TDR & Custom-made & [71] \\ ACC-SEN-TDT & TDT & Acclima Inc. & [72] \\ SISOMOOP/SPADE & TDT similar & sceme.de GmbH & [16; 73; 74] \\ SMT100 & TDT similar & Truebner GmbH & [75; 76; 77; 78] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of soil moisture sensors commonly used in WSN applications. showed that soil moisture variability strongly decreased with depth, indicating that the factors controlling prolonged travel time decrease the spatial variability of soil moisture. In addition, topographic attributes had the strongest correlation with soil moisture during dry conditions, suggesting that the control of topography on soil moisture patterns depends on water storage in the soil. Finally, it could be shown that interpolation of the densely sampled point data from SoilNet allowed the capture of the key patterns of soil moisture variation at the catchment scale [14]. Using a longer time series of SoilNet data, [PERSON] et al. [66] identified hysteresis in the relationship between spatial variability and mean soil moisture at the event and seasonal time scales and as a function of the mean soil moisture and precipitation conditions. Soil moisture data from a WSN was also used to analyze the temporal stability of soil moisture, and it was found that both soil moisture and saturation degree showed temporally stable characteristics that were correlated with the spatial variation in hydraulic parameters [86]. In a subsequent study, [PERSON] et al. [87] developed a new closed-form expression to predict local variability of soil moisture and to estimate the spatial variability of hydraulic properties on the basis of WSN data from several sites in Germany and China. Soil moisture data from WSN has also proven to be useful for water balance analysis at the catchment scale. For example, using an empirical orthogonal functional analysis, [PERSON] et al. [10] were able to show that two underlying soil moisture patterns explain the overall soil water storage variability in a headwater catchment. Using the same WSN data, [PERSON] et al. [12] compared the spatiotemporal distribution of soil moisture before and after partial deforestation and found that soil moisture in the deforested area was significantly higher compared with the forested part, especially during the summer period. This, in turn, caused an increase in the frequency of high discharge in the first year after the deforestation. In another study, [PERSON] et al.