|OCHSNER, TYSON - Oklahoma State University|
|BASARA, J. - University Of Oklahoma|
|Evett, Steven - Steve|
|HATCH, CHRISTINEE - University Of Nevada|
|SELKER, J - Oregon State University|
|SMALL, ERIC - University Of Colorado|
|STEELE-DUNNE, SUSAN - Delft University Of Technology|
|ZREDA, MAREK - University Of Arizona|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/15/2014
Publication Date: 3/12/2014
Citation: Cosh, M.H., Ochsner, T., Basara, J., Evett, S.R., Hatch, C., Selker, J., Small, E., Steele-Dunne, S., Zreda, M. 2014. Inter-comparison of soil moisture sensors from the soil moisture active passive marena Oklahoma in situ sensor testbed (SMAP-MOISST) [abstract]. Third In Situ and Remote Soil Moisture Sensing Technology Conference. 2014 CDROM.
Technical Abstract: The diversity of in situ soil moisture network protocols and instrumentation led to the development of a testbed for comparing in situ soil moisture sensors. Located in Marena, Oklahoma on the Oklahoma State University Range Research Station, the testbed consists of four base stations. Each station contains many different soil moisture sensors installed in profiles to compare their performance, and multiple sensors of the same type to find out how replicable the measurements are. These stations have been operating since their installation in 2010, providing data with one hour resolution. Additional sensors have been added to the testbed as they become available. These include stations from the COSMOS network, GPS Reflectometry Network, Climate Reference Network, and active and passive Distributed Temperature Systems. Early results of this testbed include calibration and scaling analysis as well as performance of the sensors during freeze-thaw cycles. Most sensors are able to perform with an error less than 0.04 m3/m3, when compared to a gravimetric sample, but site specific calibration is necessary for most of the sensors. In addition, analysis of time series of soil moisture revealed that the temporal characteristics of sensors are not always linear with respect to each other, so comparisons of diverse networks may require conversion equations to unify the network data records.