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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 #334824
Title: Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

Author
item DONG, JIANZHI - Delft University
item STEELE-DUNNE, SUSAN - Delft University
item OCHSNER, TYSON - Oklahoma State University
item HATCH, CHRISTINE - University Of Nevada
item SAYDE, CHADI - Oregon State University
item SELKER, J. - Oregon State University
item TYLER, S - University Of Nevada
item Cosh, Michael
item VAN DE GIESEN, NICK - Delft University

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2016
Publication Date: 12/1/2016
Citation: Dong, J., Steele-Dunne, S., Ochsner, T., Hatch, C., Sayde, C., Selker, J., Tyler, S., Cosh, M.H., Van De Giesen, N. 2016. Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother. Water Resources Research. 52(10):7690-7710.

Interpretive Summary: Monitoring soil moisture at a high resolution usually involves a significant amout of equipment and labor which is cost prohibitive. However, recent advances in fiber optics technology has revealed that buried fiber optic cables can be used to estimate soil moisture in the region around the cable at high resolution intervals. This requires an advanced algorithm to resolve at a small spatial scale with a reasonable accuracy. A study in Oklahoma was conducted to estimate at a 1 m resolution soil moisture for a length of 71 m with great success. This study will be drive forward the technology of soil moisture estimation using fiber optics which is a still developing field.

Technical Abstract: This study demonstrated a new method for mapping high-resolution (spatial: 1 m, and temporal: 1 h) soil moisture by assimilating distributed temperature sensing (DTS) observed soil temperatures at intermediate scales. In order to provide robust soil moisture and property estimates, we first proposed an adaptive particle batch smoother algorithm (APBS). In the APBS, a tuning factor, which can avoid severe particle weight degeneration, is automatically determined by maximizing the reliability of the soil temperature estimates of each batch window. A multiple truth synthetic test was used to demonstrate the APBS can robustly estimate soil moisture and properties using observed soil temperatures at two shallow depths. The APBS algorithm was then applied to DTS data along a 71 m transect, yielding an hourly soil moisture map with meter resolution. Results show the APBS can draw the prior guessed soil hydraulic and thermal properties significantly closer to the field measured reference values. The improved soil properties in turn remove the soil moisture biases between the prior guessed and reference soil moisture, which was particularly noticeable at depth above 20 cm. This high-resolution soil moisture map demonstrates the potential of characterizing soil moisture temporal and spatial variability and reflects patterns consistent with previous studies conducted using intensive point scale soil moisture samples. The intermediate scale high spatial resolution soil moisture information derived from the DTS may facilitate remote sensing soil moisture product calibration and validation. In addition, the APBS algorithm proposed in this study would also be applicable to general hydrological data assimilation problems for robust model state and parameter estimation.