Mapping high‐resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

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...

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Veröffentlicht in:Water resources research 2016-10, Vol.52 (10), p.7690-7710
Hauptverfasser: Dong, Jianzhi, Steele‐Dunne, Susan C., Ochsner, Tyson E., Hatch, Christine E., Sayde, Chadi, Selker, John, Tyler, Scott, Cosh, Michael H., van de Giesen, Nick
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Sprache:eng
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Zusammenfassung: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. Key Points A new data assimilation approach, the Adaptive Particle Batch Smoother, is presented APBS can estimate soil moisture and soil properties using soil temperatures APBS was applied to distributed temperature sensing observations to yield meter resolution soil moisture
ISSN:0043-1397
1944-7973
DOI:10.1002/2016WR019031