Improving estimated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint

Li, Bailing; Toll, D. L.; Zhan, Xiwu; Cosgrove, B.

doi:10.5194/hess-16-105-2012

Cited by 45 publications

(30 citation statements)

References 43 publications

(68 reference statements)

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“…For example, most remote sensing soil moisture products reflect the water status of a very shallow top layer of the soil, whereas hydrologic models typically simulate the water storage of a deeper soil column. Appropriate DA approaches for assimilating derived satellite products (e.g., Li et al, 2012) can mitigate but not entirely avoid this limitation. Assimilation of satellite radiance (i.e., brightness temperature) observations may also help to mitigate the conceptual mapping issue and has shown to result in improved atmospheric and hydrologic predictions (e.g., Masahiro et al, 2008;DeChant and Moradkhani, 2011a).…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

386

296

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Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters.The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, asPublished by Copernicus Publications on behalf of the European Geosciences Union. Y. Liu et al.: Advancing data assimilation in operational hydrologic forecastingwell as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

386

296

View full text Add to dashboard Cite

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“…Errors caused by parameter uncertainty can also be accounted for by using the augmented state vector technique in the EnKF-based assimilation algorithm. Li et al [35] assimilated the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) SM retrievals into the Noah LSM and found that the SM fields are less biased if a mass conservation scheme is implemented. This did not result in the expected maximum minimization of the estimation error because the algorithm only updated states and not parameters in the EnKF, and because the retrievals were not downscaled prior to using them in the model [35].…”

Section: Introductionmentioning

confidence: 98%

“…Li et al [35] assimilated the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) SM retrievals into the Noah LSM and found that the SM fields are less biased if a mass conservation scheme is implemented. This did not result in the expected maximum minimization of the estimation error because the algorithm only updated states and not parameters in the EnKF, and because the retrievals were not downscaled prior to using them in the model [35]. Kaheil et al [36] used support vector machines (SVM) combined with variogram analysis to downscale and assimilate SM observations from airborne imagery from a scale of 800 to 50 m. Extending SVM-based methodology to coarser scales of satellite observations may be difficult due to computational complexity and over-fitting the training data [37], further complicated during dynamic vegetation and heterogenous micrometeorological conditions.…”

Section: Introductionmentioning

confidence: 98%

Assimilation of SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions

Chakrabarti

Bongiovanni

Judge

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

101

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This study investigates the effects of agricultural drought on crop yields, through integration of crop growth models and remote sensing observations. The soil moisture (SM) product from SM and Ocean Salinity (SMOS) mission obtained at 25 km was downscaled to a spatial resolution of 1 km, compatible with the crop models. The downscaling algorithm is based upon information theoretic learning and uses data-driven probabilistic relationships between high-resolution remotely sensed products that are sensitive to SM and in situ SM. The downscaled SM values are assimilated in the crop model using an Ensemble Kalman filter-based augmented state-vector technique that estimates states and parameters simultaneously. The downscaling and assimilation framework are implemented for predominantly agricultural region of the lower La-Plata Basin (LPB) in Brazil during two growing seasons. This rain-fed region was affected by agricultural drought in the second season, indicated by markedly lower precipitation compared to the first growing season. The downscaled SM was compared with the in situ SM at a validation site and the root mean square difference (RMSD) was . The crop yields estimated by the downscaling-assimilation framework were compared with those provided by the Companhia Nacional de Asastecimento (CONAB) and Instituto Brasileiro de Geografia e Estatistica (IBGE). The assimilated yields are improved during both seasons with increased improvement during the second season that was affected by agricultural drought. The differences between the assimilated and observed crop yields were 16.8% during the first growing season and 4.37% during the second season.

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“…Microwaves only penetrate the top few centimeters, retrieving only the surface soil moisture rather than the root-zone soil moisture. Some efforts have been done to estimate the soil moisture profile from the surface values [17]- [19], but these studies often encountered several obstacles, including the hydraulic parameterization, initial moisture condition, and satellite measurement accuracy [8], [20]. Another issue of satellite data is the shorter temporal record, which limits its application on the soil moisture extremes/agricultural drought assessment [21].…”

Section: Introductionmentioning

confidence: 99%

Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

Yan

DeChant

Moradkhani

2015

IEEE Trans. Geosci. Remote Sensing

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Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.Index Terms-Data assimilation (DA), hydrologic measurements, remote sensing, satellite applications.

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Improving estimated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint

Cited by 45 publications

References 43 publications

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Assimilation of SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions

Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

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