Accurate Measurements of Forest Soil Water Content Using FDR Sensors Require Empirical In Situ (Re)Calibration

Abstract

Monitoring volumetric soil water content (θv) is the key for assessing water availability and nutrient fluxes. This study evaluated the empirical accuracy of θv measurements using standard and in situ calibrated frequency domain reflectometers (FDR) with gravimetric water content and bulk density measurements of 1512 samples gathered from 15 profiles across 5 ICP Forests level II intensive monitoring plots. The predicted θv, calibrated with standard functions, predominantly underestimated the real water content. The measurement error exceeded the threshold of 0.03 m3 m−3 in 93% of all soil layers. Layer specific calibration removed bias and reduced the overall prediction error with a factor up to 2.8. A simple linear regression often provided the best calibration model; temperature correction was helpful in specific cases. To adequately remove bias in our study plots, a calibration dataset of up to 24 monthly observations was required for topsoils (whereas 12 observations sufficed for subsoils). Based on estimated precision errors, 3 sensors per soil layer proved to be sufficient, while up to 16 sensors are needed to meet the required accuracy in organic topsoils. Validating FDR sensor outputs using in situ gravimetric measurements is essential for quality control and assurance of long term θv monitoring and for improving site specific instrumentalization.

Recent Advances in Dielectric Properties-Based Soil Water Content Measurements

“…Combining TDR and FDR can also improve measurement accuracy by minimizing noise and interference. TDR can also be used to calibrate FDR readings, creating a calibration curve that relates FDR results to soil water content [126,127]. The combination of TDR and FDR technologies in SWC can improve irrigation schedules, reduce water waste, reveal the health and resilience of wetlands, forests, and other ecosystems, as well as being used to monitor soil moisture levels in dams, levees, and other structures, identifying possible stability issues before they become serious in geotechnical engineering [127].…”

Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network

“…Fuzzy model validations are commonly conducted using various metrics, among which the determination coefficient or r 2 is widely utilized [10,40,49,50]. The r 2 can be interpreted as the proportion of the variance in the dependent variable can be predicted from the independent variables.…”

“…It can also be used to compare the outputs of two models, then observe if there is some linearity. Ranging from 0 to 1, a value of 0 indicates a nonlinear relationship between variables and 1 indicates a perfect linear relationship [50,51]. It is important to note that an r 2 value above 0.5 is considered statistically acceptable [33,52] and also considered the best metric to evaluate regression models, even better than mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric absolute percentage error (SMAPE) [51].…”