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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 #344084
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Soil moisture monitoring with long term in situ sensors: Lessons from MOISST

Author
item Cosh, Michael
item OCHSNER, TYSON - Oklahoma State University
item McKee, Lynn
item COOPERSMITH, E. - Collaborator
item DONG, GEANO - Oklahoma State University
item SMALL, ERIC - University Of Colorado
item ZREDA, MAREK - University Of Arizona
item QU, J.J. - George Mason University

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 10/1/2017
Publication Date: 12/11/2017
Citation: Cosh, M.H., Ochsner, T., Mckee, L.G., Coopersmith, E., Dong, G., Small, E., Zreda, M., Qu, J. 2017. Soil moisture monitoring with long term in situ sensors: Lessons from MOISST. American Geophysical Union. Abstract No. H51E-0624.

Interpretive Summary:

Technical Abstract: In situ networks to monitor soil moisture regularly select a sensor based upon economics, soil characteristics, and landscape features. But as networks endure years of deployment, sensors fail and new sensors are sought. Therefore it is necessary to determine the impact of replacing existing sensors with new and alternate sensors. In 2010, a long term soil moisture sensor testbed was installed near Stillwater, OK. The Marena Oklahoma In Situ Sensor Testbed (MOISST) is an ideal location for long term sensor inter-comparison between soil moisture sensors. Spatial averages of soil moisture may be influenced by sensor selection as there are different distributions present in the long term data record between sensors technologies, such as TDR vs. impedance, TDR vs. TDT, etc. In addition, analysis is performed to determine the impact of sensor variation on random errors.