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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 #312237

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: The Soil Moisture Active Passive Marena Oklahoma In Situ Sensor Testbed (SMAP-MOISST): Design and initial results

Author
item Cosh, Michael
item Ochsner, Tyson - Oklahoma State University
item Mckee, Lynn
item Dong, Geano - Oklahoma State University
item Basara, J. - University Of Oklahoma
item Evett, Steven - Steve
item Hatch, Christine - University Of Nevada
item Small, Eric - University Of Colorado
item Steele-dunne, Susan - Delft University
item Zreda, Marek - University Of Arizona
item Sayde, Chadi - Oregon State University

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2016
Publication Date: 4/18/2016
Citation: Cosh, M.H., Ochsner, T., Mckee, L.G., Dong, G., Basara, J., Evett, S.R., Hatch, C., Small, E., Steele-Dunne, S., Zreda, M., Sayde, C. 2016. The Soil Moisture Active Passive Marena Oklahoma In Situ Sensor Testbed (SMAP-MOISST): Design and initial results. Vadose Zone Journal. 15(4). doi: 10.2136/vzj2015.09.0122.

Interpretive Summary: In situ soil moisture monitoring systems rely on different technological principles, depending on the manufacturer. This can lead to variations in the soil moisture record as different networks are integrated together into a single data record. The Marena Oklahoma In Situ Sensor Testbed (MOISST), a comprehensive study, was developed to inter-compare these different technologies at a single location over a long time period and determine how this would impact studies based on the time series. A location near Stillwater, Oklahoma was instrumented in 2010 with eleven different sensor technologies. Most of the sensors were calibrated to the specific soil type at the study site and physical collection of volumetric soil moisture was collected as ground truth for the larger field scale. Most sensors could reach accuracy targets and were able to be scaled to the field scale with limited effort in scaling activity. The results of this study will be useful to the international soil moisture community as they work toward standardized methodologies and unified data records. The remote sensing community will also find this work helpful in their calibration and validation programs.

Technical Abstract: In situ soil moisture monitoring networks are critical to the development of soil moisture remote sensing missions as well as agricultural and environmental management, weather forecasting and many other endeavors. These in situ networks are composed of a variety of sensors and installation practices, which confounds the development of a unified reference database for satellite calibration and validation programs. As part of the Soil Moisture Active Passive Mission, the Marena, Oklahoma, In Situ Sensor Testbed (SMAP-MOISST) was initiated to perform inter-comparisons and study sensor limitations. Soil moisture sensors that are deployed in major monitoring networks were included in the study, along with new and emerging technologies, such as the Cosmic Ray Soil Moisture Observing System (COSMOS), Passive/Active Distributed Temperature Sensing System (DTS), and Global Positioning System Reflectometers (GPSR). Four profile stations were installed in May of 2010 and soil moisture was monitored to a depth of 1 m on an hourly basis. The four stations were distributed within a circular domain of approximately 600 meters diameter, adequate to encompass the sensing range of COSMOS. The sensors included in the base station configuration included the Stevens Water Hydra Probe, Campbell Scientific 616 and 229, Decagon EC-TM, Delta-T Theta Probe, Acclima, and Sentek EnviroSMART capacitance system. In addition, the Pico TRIME system and additional time domain reflectometry (TDR) systems were deployed when available. Using gravimetric calibrations, the majority of the sensors achieved accuracies better than 0.04 m3 m-3. It is also shown that in situ measurement errors can be greatly reduced when compared to local gravimetric sampling by using a simple linear regression equation.