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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 #347939

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: Temporal transferability of soil moisture calibration equations

item ROWLANDSON, T. - University Of Guelph
item BERG, A. - University Of Guelph
item BULLOCK, P. - University Of Manitoba
item HANIS-GERVAIS, K. - University Of Manitoba
item OJO, E.R. - University Of Manitoba
item Cosh, Michael
item POWERS, J. - Agriculture And Agri-Food Canada
item MCNAIRN, H. - Agriculture And Agri-Food Canada

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/1/2018
Publication Date: 1/31/2018
Citation: Rowlandson, T., Berg, A., Bullock, P., Hanis-Gervais, K., Ojo, E., Cosh, M.H., Powers, J., McNairn, H. 2018. Temporal transferability of soil moisture calibration equations. Journal of Hydrology. 556:349-358.

Interpretive Summary: Land surface energy and water budgets are dependent upon an accurate understanding of the soil moisture status across the land surface. During hydrologic field experiments, the process of estimating soil moisture at large scales is time-consuming. This requires both many sampling points and a calibration dataset for accurate measurement. For field campaigns that reoccur in the same location it is possible to use a singular calibration dataset for all future campaigns as a cost saving effort. But little work has been done to determine if such calibrations are stable in time. Therefore, a dataset was collected to determine how well a calibration equation can be applied across multiple field campaigns. It was determined that several factors affect the ability to use this cost saving effort, including whether the dynamic range of soil moisture is consistent between the campaigns. To minimize errors, a new calibration dataset should be collected for each campaign. This study is useful for field experimenters who include soil moisture in their parameter collection as well as agriculturists who need soil moisture status as a decision support variable.

Technical Abstract: Several large-scale field campaigns have been conducted over the last 20 years that require accurate estimates of soil moisture conditions. These measurements are manually conducted using soil moisture probes which require calibration. The calibration process involves the collection of hundreds of soil moisture cores, which is extremely labor intensive. In 2012, a field campaign was conducted in southern Manitoba in which 55 fields were sampled and calibration equations were derived for each field. The Soil Moisture Active Passive Experiment 2016 (SMAPVEX16) was conducted in this same region, and 21 of the same fields were resampled. This study examines the temporal transferability of calibration equations between these two field campaigns. It was found that the larger range in soil moisture over which samples were collected in 2012 (average range 0.11-0.41 m3m-3) generally resulted in lower errors when used in 2016 (average range 0.24-0.44 m3m-3) than the equations derived in 2016 when used with data collected in 2012. Combining the data collected in 2012 and 2016 did not improve the errors, overall. These results suggest that the transfer of calibration equations from one year to the next is not recommended.