A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation

Tischler, M. A.; Garcia, Matthew; Peters‐Lidard, Christa D.; Moran, M. Susan; Miller, Steve; Thoma, David P.; Kumar, Sujay V.; Geiger, J.

doi:10.1016/j.envsoft.2006.05.022

Cited by 29 publications

(14 citation statements)

References 39 publications

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“…The PEST program for example, one of the most commonly used algorithms set for optimization (Doherty, 2003), adjusts model parameters until the fit between model outputs and observations is optimized in the weighted least squares sense. For example, PEST has been recently used with success in order to optimize parameter estimation in hydrological studies (Gallagher and Doherty, 2007;Tischler et al, 2007). PEST is a nonlinear estimator using the Gauss-Marquardt-Levenberg method, needing fewer runs than most of the other estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and Particle Filtering algorithms

Gaucherel

Campillo

Misson

et al. 2008

Environmental Modelling & Software

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a b s t r a c tFinely tuned process-based tree-growth models are of considerable help in understanding the variations of biomass increments measured in the dendrochronological series. Using site and species parameters, as well as daily climate variables, the MAIDEN model computes the water balance at ecosystem level and the daily increment of carbon storage in the stem through photosynthesis processes to reproduce the structure of the tree-ring series. In this paper, we use three techniques to calibrate this model with Pinus halepensis data sampled in the Mediterranean part of France: a standard optimization (PEST), Monte Carlo Markov Chains (MCMC) and Particle Filtering (PF). Contrary to PEST, which tries to find an optimum fit (giving the lowest error between observations and simulations), the principle of MCMC and PF is to walk, from a priori distributions, in the parameter space according to particular statistical rules to compute each parameter distribution. The PEST and MCMC calibrations of our dendrochronological series lead to rather similar adjustments between simulations and observations. PF and MCMC calibrations give different parameter distributions, showing how complementary are these methods, with a better fit for MCMC. Yet, independent validations over 11 independent meteorological years show a higher efficiency of the recent PF method over the others.

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Section: Introductionmentioning

confidence: 99%

Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and Particle Filtering algorithms

Gaucherel

Campillo

Misson

et al. 2008

Environmental Modelling & Software

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“…The experimental design of this wmk was &ginally developed for the purpose of estimating troop a& vehicle &li@ for the United States Amy based on operational soil moistwe pediction fim a very limited set of k p t data (Army Rem&e Moistwe System; U S ; TisChler et al 2006). Here, we have tested and extended AaMS to assess the ability of parameter e s t I m a h teehnlques to minimize inherent model e m , yet still provide infomaion on difficult to o&a& soil properties over the Withut Gukh Environmental Watershed (WGEW) in Arizona.…”

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confidence: 99%

Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

Santanello

Peters‐Lidard²,

Garcia

et al. 2007

Remote Sensing of Environment

112

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Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived fiom passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product fiom active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals. This study exarnines the ability of microwave remote sensing estimates of soil moisture to be used to calibrate a land surface model and, in the process, infer soil textural and hydraulic properties across spatially heterogeneous landscapes. Results also demonstrate the limitations and potential improvements in simulating soil moisture evolution using a combination of remote sensing, modeling, and parameter estimation techniques. Watershed. E r r m in simulated versus observed soil moistwe were m&mized by adjustbg the soil texae, which k ttt.m eontrols the hytlraa1-iit:propefiies though k use of pedob-msfer h e k s . By estimating a continms range of widely applicable soil prope~es swh sts sad, silt, and cl...

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“…The weather data input to IWEDA go beyond soil moisture information and can include topography, forecast wind fields, cloud conditions, and air temperatures. More recently, the Army Remote Moisture System (ARMS) evolved from a tri-agency effort (US Department of Agriculture, USACE and NASA) to develop a platform capable of predicting soil moisture over large areas (up to 25,000 km 2 ) down to tactical scales (<100 m) [47]. The ARMS relies heavily upon simulation modeling, but also uses SAR imagery to constrain soil hydraulic parameters input to land surface hydrology models using the Parameter ESTimation (PEST) platform.…”

Section: Discussionmentioning

confidence: 99%

Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

Flores

Entekhabi

Bras

2014

Journal of Terramechanics

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Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a \ soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active-Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of "slow go" conditions in approximately two-thirds of the watershed, and an increase in the risk of "no go" conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability.Highlights: 1. We apply a soil moisture data assimilation system (DAS) to military trafficability 2. Assimilation improves prediction of impaired trafficability 3. Ensemble-based assimilation supports quantification of trafficability risk

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A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation

Cited by 29 publications

References 39 publications

Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and Particle Filtering algorithms

Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and Particle Filtering algorithms

Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

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