Location: Hydrology and Remote Sensing LaboratoryTitle: Field evaluation of portable soil moisture sensors in a sandy loam
|KIM, H. - University Of Virginia|
|BINDLISH, R. - National Aeronautics And Space Administration (NASA)|
|LAKSHMI, V. - University Of Virginia|
Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/15/2020
Publication Date: 5/20/2020
Citation: Kim, H., Cosh, M.H., Bindlish, R., Lakshmi, V. 2020. Field evaluation of portable soil moisture sensors in a sandy loam. Vadose Zone Journal. 19:e20033. https://doi.org/10.1002/vzj2.20033.
Interpretive Summary: Soil water content observations taken with small electronic instruments often are used to quickly and easily estimate true soil water content for management decisions. However, these instruments vary in quality and accuracy. An experiment was conducted to provide a point of comparison for these sensors in a common soil to determine the accuracy of each type of sensor. It was established that all of the tested sensors were able to estimate soil water content within good accuracy, provided a site-specific calibration is developed for the site of interest. This study is of value to hydrologists and managers who need to quickly and accuracy determine soil moisture conditions in the field.
Technical Abstract: Ground observations are critical in the validation of soil water content (SWC) estimates from both satellites and land surface models. Portable SWC sensors provide useful information to determine the amount of SWC in the topsoil layer for various applications; however, these probes are not accurate without site-specific calibration. In the present study, we examined and compared 6 different types of portable electromagnetic (EM) soil water content sensors, including multiple sensors made by the same manufacturers, for a total of 16 EM-based SM probes equipped with portable data loggers. All SM probes met the target accuracy after on-site calibration - the Root Mean Square Difference (RMSD) was less than 0.025 m3m-3. Using the two-sample t-tests, we observed that SWC data obtained from similar electrode lengths and from different manufacturers showed similar distributions over time with the same mean. Furthermore, using the maximize R method to combine SM data from two different types of sensors increased the accuracy of the results. When datasets from two different types of sensors were combined, the Pearson’s correlation coefficient (R-value) and RMSD values were improved the average R-value improved from 0.930 to 0.945, and the RMSD decreased from 0.036 m3m-3 to 0.018 m3m-3. These results indicate that, along with site-specific calibration, synergetic use of multiple manufacturers’ EM-based SWC probes can improve the R-value and reduce systematic bias.