Description: This study addresses two critical barriers to the use of passive Distributed Temperature Sensing (DTS) for large-scale, high-resolution monitoring of soil moisture. In recent research, a particle batch smoother (PBS) was developed to assimilate sequences of temperature data at two depths into Hydrus-1D to estimate soil moisture as well as soil thermal and hydraulic properties. However, this approach was limited to bare soil and assumed that the cable depths were perfectly known. In order for passive DTS to be more broadly applicable as a soil hydrology research and remote sensing soil moisture product validation tool, it must be applicable in vegetated areas. To address this first limitation, the forward model (Hydrus-1D) was improved through the inclusion of a canopy energy balance scheme. Synthetic tests were used to demonstrate that without the canopy energy balance scheme, the PBS estimated soil moisture could be even worse than the open loop case (no assimilation). When the improved Hydrus-1D model was used as the forward model in the PBS, vegetation impacts on the soil heat and water transfer were well accounted for. This led to accurate and robust estimates of soil moisture and soil properties. The second limitation is that, cable depths can be highly uncertain in DTS installations. As passive DTS uses the downward propagation of heat to extract moisture-related variations in thermal properties, accurate estimates of cable depths are essential. Here, synthetic tests were used to demonstrate that observation depths can be jointly estimated with other model states and parameters. The state and parameter results were only slightly poorer than those obtained when the cable depths were perfectly known. Finally, in-situ temperature data from four soil profiles with different, but known, soil textures were used to test the proposed approach. Results show good agreement between the observed and estimated soil moisture, hydraulic properties, thermal properties and observation depths at all locations. The proposed method resulted in soil moisture estimates in the top 10 cm with RMSE values typically 〈 0.04 3 /m 3 . This demonstrates the potential of detecting the spatial variability of soil moisture and properties in vegetated areas from Passive DTS data. This article is protected by copyright. All rights reserved.

Description: Soil moisture patterns are commonly thought to be dominated by land surface characteristics, such as soil texture, at small scales and by atmospheric processes, such as precipitation, at larger scales. However, a growing body of evidence challenges this conceptual model. We investigated the structural similarity and spatial correlations between mesoscale (∼1-100 km) soil moisture patterns and land surface and atmospheric factors along a 150-km transect using 4-km multisensor precipitation data and a cosmic-ray neutron rover, with a 400-m diameter footprint. The rover was used to measure soil moisture along the transect 18 times over 13 months. Spatial structures of soil moisture, soil texture (sand content), and antecedent precipitation index (API) were characterized using autocorrelation functions and fitted with exponential models. Relative importance of land surface characteristics and atmospheric processes were compared using correlation coefficients ( r ) between soil moisture and sand content or API. The correlation lengths of soil moisture, sand content, and API ranged from 12-32 km, 13-20 km, and 14-45 km, respectively. Soil moisture was more strongly correlated with sand content ( r = -0.536 to -0.704) than with API for all but one date. Thus, land surface characteristics exhibit coherent spatial patterns at scales up to 20 km, and those patterns often exert a stronger influence than do precipitation patterns on mesoscale spatial patterns of soil moisture.

Description: Since groundwater is Earth's largest pool of freshwater, understanding the sensitivity of deep drainage to climate, soils, and land-cover is critical to managing water resources. To better understand controls on this critical flux in the context of woody encroachment, we determined the sensitivity of deep drainage to climate, soil texture, soil compaction, rooting depth, growing season duration, and plant water stress response using Hydrus-1D to simulate deep drainage. To evaluate the simulation results, we compared these results with ground measurements at two anchor sites. At both anchor sites Hydrus-1D predictions of deep drainage matched measured values within the errors inherent in ground measurements. Sensitivity analysis suggested greatest sensitivity of deep drainage to climate (24 mm yr -1 ) and rooting depth (12 mm yr -1 ), moderate sensitivity to growing season duration (5 mm yr -1 ) and soil texture (4 mm yr -1 ), and lowest sensitivity to topsoil compaction and plant water stress response (3 mm yr -1 ). The sensitivity analysis indicated the relative importance of the plant-related factors considered was, in decreasing order, rooting depth, growing season duration, and plant water stress response—factors that change concomitantly as a result of forestation or woody encroachment. Further ground-truth measurements of woody encroachment effects on deep drainage are needed to confirm or refine the results of this simulation modeling study. This article is protected by copyright. All rights reserved.

Description: This study demonstrated a new method for mapping high resolution (spatial: 1 m, and temporal: 1 hour) 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. This article is protected by copyright. All rights reserved.