## 3 Results
### Improving the Prediction Errors in Organo-Mineral Topsoil Layers  
The statistical evaluations clearly indicate that accurate prediction of \(\theta_{\rm FDR}\) is most challenging for the topsoil layers due to high natural variability of \(\rho_{\rm b}\) and organic carbon in the organo-mineral layers. For the plots WJ and GON where the FDR sensors are installed in the OFH layers (B and C profiles), accurate calibration is jeopardized by the high variability of the reference samples taken each month. This can be seen from the scatterplot of the WIJ site in Figure 8a. With high \(PA\) values (\(PA>31\)\(\upmu\)s) of the sensor, \(\theta_{\rm v}\) may range from 0.31 to 0.73 m\({}^{3}\) m\({}^{-3}\) and \(\rho_{\rm b}\) from 300 kg m\({}^{-3}\) (pure organic matrix) to 1310 kg m\({}^{-3}\) (mineral matrix). It is also clear that the lower the \(\rho_{\rm b}\) (and hence more organic the reference sample), the more soil moisture (\(\theta_{\rm v}\)) may be stored, often more than 0.5 m\({}^{3}\) m\({}^{-3}\) compared to mineral samples (\(\rho_{\rm b}>850\) kg m\({}^{-3}\)) holding less than 0.5 m\({}^{3}\) m\({}^{-3}\) (see \(\theta_{\rm s}\) values in Table 2). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Set 1**} & \multicolumn{2}{c}{**Set 2**} \\ \hline
**Predictor** & **Rel.Inf** & **Predictor** & **Rel.Inf** \\ \hline \(\rho_{\rm bKop}\) & 21.90 & \(\theta_{\rm s}\) & 15.66 \\ Depth & 9.80 & Depth & 11.48 \\ \(\theta_{\rm s}\) & 9.62 & \(\theta_{\rm pwp}\) & 9.64 \\ Clay & 9.45 & Clay & 8.98 \\ pH-H\({}_{2}\)O & 8.75 & EC & 8.25 \\ \(\theta_{\rm r}\) & 8.17 & pH-H\({}_{2}\)O & 7.96 \\ EC & 6.46 & CEC & 7.71 \\ \(\theta_{\rm fc}\) & 5.72 & \(\theta_{\rm r}\) & 7.58 \\ Sand & 5.44 & Sand & 6.76 \\ TOC & 4.55 & \(\theta_{\rm fc}\) & 6.01 \\ \(\theta_{\rm pwp}\) & 4.37 & \(\rho_{\rm b}\) & 5.60 \\ TN & 2.91 & TOC & 3.17 \\ CEC & 2.86 & TN & 1.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Relative influence of two sets of soil properties in predicting RMSPE after calibration according to a generalized boosted regression tree model. The first set includes average bulk density of the gravimetric samples (\(\rho_{\rm bKop}\)), the second set \(\rho_{\rm b}\). Figure 8: Scatterplots of topsoil calibration data relating sensor output to observed volumetric water content (\(\theta_{\rm v}\)) and measured \(\rho_{\rm b}\) value (kg m\({}^{-3}\)) for each sample: (**a**) all topsoil (0\(-\)10 cm) samples of WIJ site; (**b**) topsoil (0\(-\)10 cm) samples of GON site. Linear regression lines for all topsoil samples (dashed line) and for selected organo-mineral samples (full line and filled dots). Selecting from each WIJ profile (max 25 calibration pairs) only the organo-mineral samples by their \(\rho_{\rm b}\) value (i.e., \(\rho_{\rm b}\)! 850 kg m\({}^{-3}\)) yields too few samples for adequate calibration per profile. Therefore, we pooled all topsoil samples from the three profiles of the WIJ plot (n = 75 samples). When calibrating a LM the slope of the model was not significantly different from 0 (Figure 8a). When subselecting organo-mineral samples with \(\rho_{\rm b}\)! 850 kg m\({}^{-3}\), a LM based on n = 47 paired observations was calibrated: \(\theta_{\rm FDR}\) = 0.320295 + 0.00971 \(PA\), with R\({}^{2}\) = 0.119, \(p\) = 0.0099 and RMSPE = 0.0745 m\({}^{3}\) m\({}^{-3}\). Based on AICc criterion, QM, LMTC nor QMTC models performed better than the LM. However, when varying different thresholds for \(\rho_{\rm b}\), the best linear calibration model was found for organo-mineral samples with a \(\rho_{\rm b}\)! 600 kg m\({}^{-3}\), and parameters: \(\theta_{\rm FDR}\) = 0.39962 + 0.008767 \(PA\), with R\({}^{2}\) = 0.276, \(p\) = 0.005 and RMSPE = 0.0417 m\({}^{3}\) m\({}^{-3}\). The selected samples (n = 24) and calibration function are indicated in Figure 8a. Only one-third of the total number of calibration samples was selected, but this number resembles the 25 samples used for calibration of each individual profile.