A particle batch smoother for soil moisture estimation using soil temperature observations. (September 2015)

• Record Type: Journal Article
• Authors: Dong, Jianzhi; Steele-Dunne, Susan C.; Judge, Jasmeet; van de Giesen, Nick
• Is Part Of: Advances in water resources. Volume 83(2015)
• Page Start: 111
• Page End: 122
• Publication Date: 2015-09
• Subjects: Soil moisture -- Distributed temperature sensing -- Data assimilation -- Particle approaches -- Smoothing; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2015.05.017
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: A particle batch smoother (PBS) is derived from the standard particle filter. The PBS is used to determine soil moisture profile by assimilating soil temperature. The PBS outperforms the standard particle filter with similar computational cost. The PBS provides a valuable tool to determine soil moisture from DTS. Abstract: In this study, we present a particle batch smoother (PBS) to determine soil moisture profiles by assimilating soil temperatures at two depths (4 and 8 cm). The PBS can be considered as an extension of the standard particle filter (PF) in which soil moisture is updated within a window of fixed length using all observed soil temperatures in that window. This approach was developed with a view to assimilating temperature observations from distributed temperature sensing (DTS) observations, a technique which can provide temperature observations every meter or less along cables up to kilometers in length. Here, the PBS approach is tested using soil moisture and temperature, and meteorological data from an experimental site in Citra, Florida. Results demonstrate that the PBS provides a statistically significant improvement in estimated soil moisture compared to the PF, with only a marginal increase in computational expense ( < 3% of CPU time). This confirms that assimilating a sequence of temperature observations yields a better soil moisture estimate compared to sequential assimilation of individual temperature observations. The impact of observation interval was investigated for both PF and PBS, and the optimal window length was determined for the PBS. While increasing the observation interval is essential to maintain the spread of particle values in the PF, the PBS performance is best when all available observations are assimilated.