Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter

Land data assimilation (DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations. Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus, improve the accuracy of state variables. It can also reduce uncertainties in the model by estimating some model parameters simultaneously. Among the various DA methods, the particle filter is free from the constraints of linear models and Gaussian error distributions, and can be applicable to any nonlinear and non-Gaussian state-space model; therefore, its importance in land data assimilation research has increased. In this study, a DA scheme was developed based on the residual resampling particle filter. Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously. Finally, to verify the scheme, a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona. The results show that the scheme can improve the accuracy of soil moisture estimations significantly. In addition, the three hydraulic parameters were also well estimated, demonstrating the effectiveness of the DA scheme.

[1]  G. Hornberger,et al.  Empirical equations for some soil hydraulic properties , 1978 .

[2]  G. Hornberger,et al.  A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils , 1984 .

[3]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[4]  T. Schmugge,et al.  Passive microwave remote sensing system for soil moisture: some supporting research , 1989 .

[5]  T. Schmugge,et al.  Vegetation effects on the microwave emission of soils , 1991 .

[6]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[7]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[8]  Eric F. Wood,et al.  One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model , 1996 .

[9]  T. Hamill,et al.  A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme , 2000 .

[10]  Leonard A. Smith,et al.  Probabilistic noise reduction , 2001 .

[11]  Jeffrey P. Walker,et al.  A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index , 2001, IEEE Trans. Geosci. Remote. Sens..

[12]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .

[14]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[15]  Eric Moulines,et al.  Comparison of resampling schemes for particle filtering , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[16]  Jean-Pierre Wigneron,et al.  A parameterized multifrequency-polarization surface emission model , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[17]  A. Weerts,et al.  Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models , 2006 .

[18]  Marco Mancini,et al.  Dynamic Calibration with an Ensemble Kalman Filter Based Data Assimilation Approach for Root-Zone Moisture Predictions , 2007 .

[19]  Yaoming Ma,et al.  Auto-calibration System Developed to Assimilate AMSR-E Data into a Land Surface Model for Estimating Soil Moisture and the Surface Energy Budget( Coordinated Enhanced Observing Period(CEOP)) , 2007 .

[20]  L. Feyen,et al.  Assessing parameter, precipitation, and predictive uncertainty in a distributed hydrological model using sequential data assimilation with the particle filter , 2009 .

[21]  C. Shi,et al.  A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature , 2009 .

[22]  K. Nagarajan,et al.  Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions , 2011 .

[23]  H. Vereecken,et al.  Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach , 2009 .

[24]  A. Dai,et al.  A dual‐pass variational data assimilation framework for estimating soil moisture profiles from AMSR‐E microwave brightness temperature , 2009 .

[25]  Ronggao Liu,et al.  Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal , 2009 .

[26]  Hui Tian,et al.  Application and improvement of an adaptive ensemble Kalman filter for soil moisture data assimilation , 2010 .

[27]  Li Xin.  A Bayesian Filter Framework for Sequential Data Assimilation , 2010 .

[28]  Eric F. Wood,et al.  Impact of Accuracy, Spatial Availability, and Revisit Time of Satellite-Derived Surface Soil Moisture in a Multiscale Ensemble Data Assimilation System , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Jiang Zhu,et al.  Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments , 2011 .

[30]  M. Canty,et al.  Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter , 2011 .

[31]  Miaoling Liang,et al.  China land soil moisture EnKF data assimilation based on satellite remote sensing data , 2011 .

[32]  Y. Tachikawa,et al.  Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization , 2011 .

[33]  G. Lannoy,et al.  The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter , 2011 .

[34]  Yaoming Ma,et al.  The Tibetan plateau observatory of plateau scale soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and model products , 2011 .

[35]  Ma Jianwen,et al.  Recent Advances and Development of Data Assimilation Algorithms , 2012 .

[36]  Hamid Moradkhani,et al.  Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting , 2012 .

[37]  Jun Qin,et al.  Optimal Exploitation of AMSR-E Signals for Improving Soil Moisture Estimation Through Land Data Assimilation , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Jianwen Ma,et al.  Using Residual Resampling and Sensitivity Analysis to Improve Particle Filter Data Assimilation Accuracy , 2013, IEEE Geoscience and Remote Sensing Letters.