## 4 Discussion
### Attainable Level of Predictive Accuracy

Table 10: Average RMSPE values for FDR CS616 sensor performance in various textures reported in literature compared to this study. international group of soil water instrumentation experts came to the conclusion that \"all soil moisture sensors must be field calibrated in order to obtain reasonable accuracy\" except conventional time domain reflectometry (TDR), which is accurate to \(\pm\)0.02 m\({}^{3}\) m\({}^{-3}\) in most soils [11]. Most studies mentioned in Table 10 collected (large) volumetric field samples in containers and transported them to the lab in order to test a number of moisture sensors and compare the results using standard and revised functions. For practical reasons they calibrate their sensors during a drying phase [3] under controlled temperature and humidity conditions. In our in situ calibration approach we sampled over two years two drying phases and two rewetting phases (Figure 2b), accounting also for the real soil temperature variations associated with the moment and depth of sampling. On the other hand, since gravimetric soil sampling is destructive and we cannot sample close (i.e., less than 1 m) to the sensors to avoid interference, we add spatial and sampling errors to our validation measurements. This set-up requirement is the major limitation of our in situ calibration approach, certainly since many forest soils tend to have a high small-scale variability of moisture influencing soil parameters. Most reference studies reported that the factory-based standard function underestimates the real moisture content, but under- or overestimation seems dependent on the soil texture. However, varble and Chavez [4] found in the laboratory that the factory calibrated CS616 sensors overestimated \(\theta_{\mathrm{v}}\) by an average bias of 0.10 m\({}^{3}\) m\({}^{-3}\) in sandy clay loam, 0.03 m\({}^{3}\) m\({}^{-3}\) in loamy sand, and 0.24 m\({}^{3}\) m\({}^{-3}\) in clay loam. This is contrasting to [1] where negative biases and much lower values were found for the same texture classes. In our study we found that standard functions generally underestimated sand layers with MPE = \(-\)0.042 m\({}^{3}\) m\({}^{-3}\) and loamy sand with \(-\)0.053 m\({}^{3}\) m\({}^{-3}\) which is greater than these studies, but this might be related to our in situ sampling. [PERSON] et al. [1] corrected the standard functions with correction equations but the improvement was quite small: 0.011 m\({}^{3}\) m\({}^{-3}\) for sand to 0.002 m\({}^{3}\) m\({}^{-3}\) in loamy sand and 0.027 m\({}^{3}\) m\({}^{-3}\) in sandy clay loam. We believe that recalibration of the models is more effective than applying correction equations on poorly performing factory-based functions, and that only calibrations using in situ measurements can lower the effective bias substantially. The standard functions (Equations (1) and (2)) provided by [PERSON] are found inadequate for clay and clay loam soils (Table 10), often overestimating \(\theta_{\mathrm{v}}\). [PERSON] and [PERSON] [4] reported the average RMSE of the clay loam samples (n = 65) as 0.289 m\({}^{3}\) m\({}^{-3}\) compared to 0.21 m\({}^{3}\) m\({}^{-3}\) for clays in our study. Using laboratory-based recalibration, they succeeded to eliminate bias and reduce the resulting RMSE below 0.021 m\({}^{3}\) m\({}^{-3}\) for the clay loam soil, an error reduction of 93%. In our study RMSE after in situ recalibration is still 0.076 m\({}^{3}\) m\({}^{-3}\) for clay samples, an error reduction of 63% (Table 10). [PERSON] and [PERSON] [7] tested heavy clay soils (60\(-\)80% clay) and found standard calibrated CS616 sensors to underestimate water content up to 0.15 m\({}^{3}\) m\({}^{-3}\). This is in contrast with our findings that clay soils are overestimated with the standard function, but clay mineralogy clearly differed between our studies. Instead of recalibrating the models for the clayey soils [7] developed a 3-phase mixing model for topsoil and subsoil separately and reduced RMSE for these clay soils to maximum 0.012 m\({}^{3}\) m\({}^{-3}\). (error reduction of 93%).