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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #328805
Title: Comparison Of In Situ Soil Moisture Measurements: An Examination of the Neutron and Dielectric Measurements within the Illinois Climate Network

Author
item Coopersmith, Evan
item Cosh, Michael
item JACOBS, J. - University Of New Hampshire

Submitted to: Journal of Atmospheric and Ocean Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/18/2016
Publication Date: 8/18/2016
Citation: Coopersmith, E.J., Cosh, M.H., Jacobs, J. 2016. Comparison Of In Situ Soil Moisture Measurements: An Examination of the Neutron and Dielectric Measurements within the Illinois Climate Network. Journal of Atmospheric and Ocean Technology. doi:10.1175/JTECH-D-16-0029.1.

Interpretive Summary: A soil moisture technologies evolve, in situ networks are updated to use better sensors and methodologies. However, this can create a discontinuity in the data record as the sensor performance may be different between the old and new sensors. One example of this is the Illinois Climate Network which originated in 1980's with neutron probes. In the 2000's, these sensors were replaced with Hydra probes (Stevens Water, Inc.). This study analyzes the two data records versus a bridging model and develops a homogenized data record for the entire period of record of the network with reasonable accuracy. The results of this study are useful for both climate modeling and long term watershed planning as it provides a climatology for soil moisture at the state scale.

Technical Abstract: The continuity of soil moisture time series data is crucial for climatic research. Yet, a common problem for continuous data series is the changing of sensors, not only as replacements are necessary, but as technologies evolve. The Illinois Climate Network has one of the longest data records of soil moisture, yet it has a discontinuity when the primary sensor (neutron probes) was replaced with a dielectric sensor. Applying a simple model coupled with machine learning, the two time series can be merged into one continuous record by training the model on the latter dielectric model and minimizing errors against the former neutron probe data set. The model is able to be calibrated to an accuracy of 0.050 m3/m3 and applying this to the earlier series and applying a gain and offset, an RMSE of 0.055 m3/m3 is possible. As a result of this work, there is now a singular network data record extending back to the 1980’s for the state of Illinois.