## 5 Conclusions  
On all sites, in situ recalibration removed the bias of standard functions up to 0.16 m\({}^{3}\) m\({}^{-3}\) in toposils and reduced the overall prediction error by a factor of 1.8 to 2.8. Standard functions performed very poorly in organic and organo-mineral topsoil layers and clayey substrates. Definitely, recalibration is required for each location where an FDR sensor is installed and new layer-specific empirical calibration functions have to be developed for each site in order to meet the data quality objectives (bias less than 0.01 m\({}^{3}\) m\({}^{-3}\) and RMSPE < 0.03 m\({}^{3}\) m\({}^{-3}\)). However, average single FDR sensor accuracy for the studied forest soils is 0.043 m\({}^{3}\) m\({}^{-3}\) so for most layers multiple sensors are required to predict average \(\theta_{\text{v}}\) with an accuracy better than 0.03 m\({}^{3}\) m\({}^{-3}\) at the plot scale. Operators should be aware of the level of effective field accuracy of their sensors. Prediction bias is strongly related with clay content and soil depth and therefore there is a need for depth specific empirical calibration functions or mechanistic functions that incorporate clay content and depth variables. Precision error is most positively related to carbon content (organic matter) and negatively with soil bulk density. Especially organo-mineral topsoil layers showing high \(\rho_{\text{b}}\) variation are hard to calibrate in situ due to imprecise field measurements. The current number of sensors at GON is too low to reduce the total measurement error and this might thwart future application of water balance models for environmental modeling purposes. For most sites, more than 19 monthly calibrations are required to lower the bias consistently below 0.01 m\({}^{3}\) m\({}^{-3}\), which is in fact a 2 drying and 1 rewetting phase if calibration started when soils are near field capacity. We recommend intensifying the calibration sampling during the growing season, for instance by two-weekly sampling instead of monthly for a better spread of calibration points between field capacity and permanent wilting point. Finally, we found that two to three FDR sensors per layer for the Flemish ICP Forests Level II study plots are adequate to reach an acceptable measurement accuracy in the homogeneous sand and silt loam sites, but up to 10 to 16 sensors may be required in heterogeneous topsoil or clayey subsoil layers of the other sites to account for their high intrinsic variability, otherwise hydrological modeling will be tentative. Future research should focus on development of dedicated calibration functions for forest floors and other organic layers followed by an adequate in situ validation. The following Supplementary data are available online at [[https://www.mdpi.com/article/10.3390/app112411620/s1](https://www.mdpi.com/article/10.3390/app112411620/s1)]([https://www.mdpi.com/article/10.3390/app112411620/s1](https://www.mdpi.com/article/10.3390/app112411620/s1)). Figures S1-S5--Layer specific relationships between reference gravimetric water content and FDR sensor measurements for each site; Tables S1-S5 with Coefficients and predictive quality of standard calibration functions for all profiles and depths of the 5 plots; 5 calibration datasets (excel) with corresponding recalibrated FDR measurements: WIJ.calibDB.xlsx, RA.calibDB.xlsx, BRA.calibDB.xlsx, GON.calibDB.xlsx and HOE.calibDB.xlsx. This study was mainly conducted by B.D.V., but FDR sensor data was collected, validated and stored by A.V. and N.C. (physico-chemical soil data). Review and editing by J.N., A.V., and N.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. **Data Availability Statement:** Survey and monitoring data (soil, meteo, deposition) of the studied LII plots are available from the ICP Forests database residing at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany.