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Science China Earth Sciences

, Volume 57, Issue 4, pp 824–838 | Cite as

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

  • HaiYun BiEmail author
  • JianWen Ma
  • SiXian Qin
  • HongJuan Zhang
Research Paper

Abstract

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.

Keywords

data assimilation residual resampling particle filter microwave brightness temperature soil moisture hydraulic parameter 

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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • HaiYun Bi
    • 1
    • 2
    Email author
  • JianWen Ma
    • 1
  • SiXian Qin
    • 1
    • 2
  • HongJuan Zhang
    • 1
  1. 1.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.University of the Chinese Academy of SciencesBeijingChina

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