Towards soil property retrieval from space: A one-dimensional twin-experiment

Inference of Soil Hydrologic Parameters from Electronic Soil Moisture Records

“…Soil moisture is also an excellent metric of hydrologic model performance, as it integrates temporal variation in precipitation and evaporation and is responsive to topography and soil physical properties governing fluxes of water (Wood et al, 1992;Cuenca et al, 1996;RodriguezIturbe et al, 1999RodriguezIturbe et al, , 2001Laio et al, 2001;Western et al, 2004;Seneviratne et al, 2010;Bandara et al, 2013;Or et al, 2015;Paniconi and Putti, 2015). As such, the definition and quantification of physical properties that govern the ability of a soil to retain water against gravity drainage, evaporation, and transpiration have long been of interest to agronomists and physical scientists (Briggs and McLane, 1907;Buckingham, 1907).…”

“…Since transitions in fluxes in agronomic and ecohydrologic models are often indicated by FC, ws , and PEL either indirectly (Liang et al, 1996) or directly (e.g., Wigmosta et al, 1994;Boote et al, 2008;Seneviratne et al, 2010;Paniconi and Putti, 2015) and are bounded by s (or fs ) and either PEL or some lower limit to , we consider these important variables for hydrologic modeling. Recent approaches to determine soil moisture parameters include inverse modeling, remote sensing and other synthetic approaches (Santanello et al, 2007;Montzka et al, 2011;Bandara et al, 2013). However, soil hydrologic properties would ideally be determined by field measurement (Romano and Santini, 2002).…”

Towards soil property retrieval from space: An application with disaggregated satellite observations

“…The work presented in this paper uses the PSO code from Scheerlinck et al (2009), with some modifications to facilitate parallelization. For the work presented in this paper, w = 0.4, c 1 = 1.4 and c 2 = 1.3 were used as these were shown to be the best combination of parameters; Bandara et al (2013) contains a detailed discussion about the selection of PSO parameters. Additionally, the root mean square error (RMSE) for the predicted minus observed soil moisture was used as the objective function in this study.…”

“…Hence, there is the potential to derive more accurate soil hydraulic parameter datasets over large areas from these observations, but most work to date has focused on synthetic simulations at local scale (Ines and Mohanty, 2008;Montzka et al, 2011), or observations on engineered soils (Burke et al, 1997a(Burke et al, , 1997b(Burke et al, , 1998Camillo et al, 1986;Ines and Mohanty, 2008); for a more detailed review of these studies refer to Bandara et al (2013). There are only a few studies that have focused on estimating soil hydraulic properties from soils under transient flow or naturally occurring boundary conditions (Dane and Hruska, 1983;Ritter et al, 2003); a more detailed review of these studies can be found in Bandara et al (2014) In Bandara et al (2013), a methodology was developed for estimating the soil hydraulic properties of a heterogeneous soil column within a synthetic twin-experiment framework. According to this methodology, the soil hydraulic parameters were derived by calibrating a soil moisture prediction model to surface soil moisture observations, such as those which are available from satellite observations.…”

Brief communication: Estimation of hydraulic properties of active layers using ground-penetrating radar (GPR) and 2D inverse hydrological modeling

“…Accordingly, the model predictions using estimated hydraulic parameters solely based on near-surface soil moisture observations are not as good as the ones based the observations for the entire profile (Bandara et al, 2014). In order to yield good results, all inverse parameter estimation methods rely on a time series of measurements that contain significant information on soil water dynamics (Bandara et al, 2013).…”