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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 #314316

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

Location: Hydrology and Remote Sensing Laboratory

Title: Multi-profile analysis of soil moisture within the U.S. Climate Reference Network

Author
item Coopersmith, Evan
item Cosh, Michael
item BELL, JESSE - National Oceanic & Atmospheric Administration (NOAA)
item Crow, Wade

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/17/2015
Publication Date: 1/15/2016
Citation: Coopersmith, E.J., Cosh, M.H., Bell, J., Crow, W.T. 2016. Multi-profile analysis of soil moisture within the U.S. Climate Reference Network. Vadose Zone Journal. 15(1). doi: 10.2136/vzj2015.01.0016.

Interpretive Summary: Soil moisture sensors are prone to biases and errors when they are deployed in the landscape. It is often difficult to discern the magnitudes of these errors. Triple collocation will enable a computation of the random errors associated with landscape variables, if there are three independent data sources. Applying this technique to the Climate Reference Network, which contains triplicate sensor installations, we have determined the random errors associated with the Hydra Probe sensor, which is a common sensor in the U.S. The random error associated with this sensor installed in nature is approximately 0.01 m3/m3, which is an important statistic for network managers and decision support specialists who must account for these errors in their models and calculations. This also has an impact on remote sensing validation programs.

Technical Abstract: Soil moisture estimates are crucial for hydrologic modeling and agricultural decision-support efforts. These measurements are also pivotal for long-term inquiries regarding the impacts of climate change and the resulting droughts over large spatial and temporal scales. However, it has only been the past decade during which ground-based soil moisture sensory resources have become sufficient to tackle these important challenges. Despite this progress, random and systematic errors remain in ground-based soil moisture observations. Such errors must be quantified (and/or adequately minimized) before such observations can be used with full confidence. In response, this paper calibrates and analyzes USCRN profile estimates at each of three sensors collocated at each USCRN location. With each USCRN location consisting of three independent, hydraprobe measurements, triple collocation analysis of these sensory triads reveals the random error associated with this particular sensing technology in each individual location. This allows a quantification of the accuracy of these individual profiles, the random errors associated with these measurements in different geographic locations, and offers the potential for more adept quality control procedures in near real-time. Averaged over USCRN gauge locations nationally, this random error is determined to be approximately 0.01 m3/m3.