中国地理科学(英文版) ›› 2010, Vol. 20 ›› Issue (3): 218-225.doi: 10.1007/s11769-010-0218-6

• 论文 • 上一篇    下一篇

Assimilation of ASAR Data with a Hydrologic and Semi-empirical Backscattering Coupled Model to Estimate Soil Moisture

LIU Qian, WANG Mingyu, ZHAO Yingshi   

  1. College of Resource and Environment, Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
  • 收稿日期:2009-09-09 修回日期:2010-01-21 出版日期:2010-04-01 发布日期:2010-04-23
  • 通讯作者: WANG Mingyu.E-mail:mwang@gucas.ac.cn E-mail:mwang@gucas.ac.cn
  • 基金资助:

    Under the auspices of Major State Basic Research Development Program of China (973 Program) (No.2007CB714400);the Program of One Hundred Talents of the Chinese Academy of Sciences (No.99T3005WA2)

Assimilation of ASAR Data with a Hydrologic and Semi-empirical Backscattering Coupled Model to Estimate Soil Moisture

LIU Qian, WANG Mingyu, ZHAO Yingshi   

  1. College of Resource and Environment, Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
  • Received:2009-09-09 Revised:2010-01-21 Online:2010-04-01 Published:2010-04-23
  • Supported by:

    Under the auspices of Major State Basic Research Development Program of China (973 Program) (No.2007CB714400);the Program of One Hundred Talents of the Chinese Academy of Sciences (No.99T3005WA2)

摘要:

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

关键词: Advanced Synthetic Aperture Radar (ASAR), Distributed Hydrology-Soil-Vegetation Model (DHSVM), Oh Model, couple, soil moisture, data assimilation

Abstract:

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

Key words: Advanced Synthetic Aperture Radar (ASAR), Distributed Hydrology-Soil-Vegetation Model (DHSVM), Oh Model, couple, soil moisture, data assimilation